Tensions in transparent urban AI: designing a smart electric vehicle charge point
Open accessAbstract
The increasing use of artificial intelligence (AI) by public actors has led to a push for more transparency. Previous research has conceptualized AI transparency as knowledge that empowers citizens and experts to make informed choices about the use and governance of AI. Conversely, in this paper, we critically examine if transparency-as-knowledge is an appropriate concept for a public realm where private interests intersect with democratic concerns. We conduct a practice-based design research study in which we prototype and evaluate a transparent smart electric vehicle charge point, and investigate experts’ and citizens’ understanding of AI transparency. We find that citizens experience transparency as burdensome; experts hope transparency ensures acceptance, while citizens are mostly indifferent to AI; and with absent means of control, citizens question transparency’s relevance. The tensions we identify suggest transparency cannot be reduced to a product feature, but should be seen as a mediator of debate between experts and citizens.
AI snapshot
Written from the paper’s full text.
TL;DR
This practice-based design study prototypes a transparent smart EV charge point and compares how experts and citizens understand AI transparency. It finds that transparency-as-knowledge creates tensions: experts expect truthful explanations to support acceptance and fairness assessment, while citizens often experience explanations as burdensome, irrelevant without control, and only occasionally motivating concern or strategic behavior.
Key findings
- Experts framed transparency as truthful information about automated decisions and as enabling fairness assessment; project documents described making “black boxes” visible and conveying a “positive tone of voice” about fair representation of interests.
- Citizens were largely indifferent to AI in EV charging, often trusting technicians or accepting automation as optimizing common interests such as grid stability and sustainability; concern arose mainly around the shared-car priority rule, which some saw as unfair or politically threatening.
- Citizens experienced transparency as burdensome: participants reported too much information (“I just want to charge”; “it’s confusing”), and many did not read instructions, instead using trial and error.
- Transparency invited strategic behavior and a desire for control: some participants said they would change charging times for speed, sustainability, or cost, while many expected to be able to choose or override settings; when they could not, some questioned why explanations were provided at all.
- The paper identifies three tensions: information quality over quantity, level of concern, and sense of control; it concludes transparency should be reframed as a communicative process between experts and citizens, not merely a product feature.
Methods
- Qualitative-interpretive, practice-based design research in the “field” mode, with participatory action research elements, conducted as part of the “UI for Smart EV Charging” project with commercial partners and the municipality of Amsterdam.
- Data included project documents, field observations, a reflective field journal, and interviews; the first author observed all design-team meetings, and analysis used reflexive thematic analysis.
- Design phase included interviews with five EV drivers; evaluation used a 1:1 cardboard prototype with a 12.9-inch tablet at a fast charging facility, in two one-day rounds: round one had five participants (P1–P5; 1 female, 4 male), round two had four participants (P6–P9; 1 female, 3 male).
Limitations
- The paper states findings relate specifically to smart EV charging, including the challenge of an intuitive charge-speed measure, and that the AI studied was deterministic, so design solutions may not be viable for stochastic systems.
- The paper states EV driving is accessible mostly to affluent or professionally employed people, the citizen sample skews highly educated, and the expert–citizen distinction leaves out other stakeholders, including non-EV-driving citizens and experts without formal influence.
- Evident from the text: the prototype was non-functional and evaluated in two one-day rounds with nine EV drivers total, so usability and experience findings are based on a small, situated sample.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 50 cited sources checked · 0 supported · 0 flagged
- Findingown data
Citizens experience transparency as burdensome.
“citizens experience transparency as burdensome”
Quote in the paperSubstantial claim- Rests on an assumption: The prototype evaluation with a small number of EV drivers is a valid basis for conclusions about how citizens experience transparency in urban AI systems.
- Rests on an assumption: The prototype's transparency interface accurately represents the smart charging system's rules and behavior.
- Findingown data
With absent means of control, citizens question transparency's relevance.
“with absent means of control, citizens question transparency's relevance”
Quote in the paperSubstantial claim- Rests on an assumption: The prototype evaluation with a small number of EV drivers is a valid basis for conclusions about how citizens experience transparency in urban AI systems.
- Rests on an assumption: The prototype's transparency interface accurately represents the smart charging system's rules and behavior.
- Findingown data
Citizens are mostly indifferent to AI.
“citizens are mostly indifferent to AI”
Quote in the paperSubstantial claim- Rests on an assumption: The prototype evaluation with a small number of EV drivers is a valid basis for conclusions about how citizens experience transparency in urban AI systems.
- Rests on an assumption: The prototype's transparency interface accurately represents the smart charging system's rules and behavior.
- Findingown data
Those who were more or less indifferent to AI typically did not respond too strongly to the revelation they in fact could not exercise any control.
“Those who were more or less indifferent to AI typically did not respond too strongly to the revelation they in fact could not exercise any control.”
Quote in the paperNot a substantial claim - Findingown data
Not all participants were as keen on changing their behavior.
“It should be noted not all participants were as keen on changing their behavior.”
Quote in the paperSubstantial claim
- Findingown data
Additional information lead to confusion.
“Additional information lead to confusion.”
Quote in the paperSubstantial claim - Findingown data
Some participants did respond with some chagrin to the revelation they could not exercise any control.
“Some participants, however, did respond with some chagrin, such as P3 here:”
Quote in the paperNot a substantial claim - Findingown data
Most participants expected to be able to change things to their advantage, not only in terms of charge speed but also in terms of price.
“Most participants expected to be able to change things to their advantage, not only in terms of charge speed but also in terms of price:”
Quote in the paperSubstantial claim - Findingown data
Many participants responded positively to the notion of using automation to optimize EV charging towards common interests: a stable electric grid, a fair distribution of power, and sustainability in general terms.
“Many responded positively to the notion of using automation to optimize EV charging towards what could be described as common interests: a stable electric grid, a fair distribution of power, and sustainability in general terms.”
Quote in the paperNot a substantial claim - Findingown data
Some participants were driven less by a desire to be more sustainable and were more interested in reaping economic benefits.
“Some participants were driven less by a desire to be more sustainable and were more interested in reaping economic benefits.”
Quote in the paperNot a substantial claim - Findingown data
Many people seemed more or less indifferent to the presence of automated decision-making.
“In addition, many people seemed more or less indifferent to the presence of automated decision-making.”
Quote in the paperNot a substantial claim - Findingown data
When asked how they would deal with automated decisions they disagree with, the majority of participants seemed somewhat resigned to accepting the system's functioning as it is presented to them.
“When asked how they would deal with automated decisions they disagree with, the majority of participants seemed somewhat resigned to accepting the system's functioning as it is presented to them.”
Quote in the paperSubstantial claim - Findingreasoning
Participants are focused on completing the task of EV charging with confidence and minimal hassle.
“Apparently, participants are focused on completing the task of EV charging with confidence and minimal hassle.”
Quote in the paperSubstantial claim - Findingown data
In general, participants were welcoming of automated decision-making in the EV charging process.
“In general, participants were welcoming of automated decision-making in the EV charging process.”
Quote in the paperNot a substantial claim - Findingown data
When put on the spot, participants frequently admitted they did not expect to change anything about their behavior themselves.
“But when we put them on the spot, they frequently admitted they did not expect to change anything about their behavior themselves.”
Quote in the paperSubstantial claim- Rests on an assumption: Time and place of a charging session is strongly dictated by circumstances, such as availability of free parking spots.
- Findingown data
In prototype evaluations, most participants started a charge session in the wrong way, even though instructions were listed on the opening screen.
“In our prototype evaluations, most participants started a charge session in the “wrong” way, even though instructions were listed on the opening screen.”
Quote in the paperSubstantial claim - Findingown data
Prototype v1 displayed the real-time charge speed in amperes, which was a largely meaningless indicator for participants.
“While charging, prototype v1 displayed the real-time charge speed in amperes (amp), a measure of current.This was a largely meaningless indicator for participants.”
Quote in the paperSubstantial claim - Findingown data
Many participants interpreted explanations themselves as things to be interacted with.
“Many participants interpreted explanations themselves as things to be interacted with.”
Quote in the paperSubstantial claim - Findingown data
None of the participants were shared car drivers themselves.
“But none of the participants were shared car drivers themselves.”
Quote in the paperNot a substantial claim - Findingown data
Most participants stated they would simply try to find a different (non-smart) charge point if they disagreed with automated decisions.
“Although one might expect some participants would want to make their disagreement known to system operators, most stated they would simply try to find a different (non-smart) charge point.”
Quote in the paperSubstantial claim - Findingown data
Most participants happily speculate about what other people might do with the information provided.
“Most participants happily speculate about what other people might do with the information provided.”
Quote in the paperSubstantial claim - Findingown data
EV charging in general is an error-prone activity.
“First, EV charging in general is an error-prone activity.”
Quote in the paperSubstantial claim - Findingown data
Participants typically made their way through charging in a trial and error fashion, not taking time to read any instructions beforehand.
“They typically made their way through it in a trial and error fashion: swiping a card and plugging in a connector in succession until the system progressed to the next state, not taking time to read any instructions beforehand.”
Quote in the paperSubstantial claim- Rests on an assumption: Users do not read instructions (a usability truism).
- Findingown data
The strongest reactions against automated decision-making related to the shared car priority feature.
“The strongest reactions against automated decision-making related to the shared car priority feature.”
Quote in the paperNot a substantial claim - Findingown data
In some cases participants wanted to have the choice to be altruistic.
“In some cases participants wanted to have the choice to be altruistic.”
Quote in the paperNot a substantial claim - Findingown data
Fairness assessment is impacted by design choices.
“Fairness assessment is impacted by design choices.”
Quote in the paperSubstantial claim - Findingown data
Participants do not welcome additional demands put upon them by this information when all they want to do is charge their EV.
“Participants do not welcome additional demands put upon them by this information when all they want to do is charge their EV.”
Quote in the paperSubstantial claim - Findingnone given
Poor design and engineering of charge points and wider infrastructure frequently lead to failed charging attempts.
“Poor design and engineering of charge points and wider infrastructure frequently lead to failed charging attempts.”
Quote in the paperNot a substantial claim - Findingown data
Lack of control in some cases leads to participants questioning the value of including any explanations at all.
“Furthermore, lack of control in some cases leads to participants questioning the value of including any explanations at all.”
Quote in the paperSubstantial claim - Findingown data
A final source of unease is uncertainty over the amount of charge delivered.
“A final source of unease is uncertainty over the amount of charge delivered.”
Quote in the paperNot a substantial claim - Findingown data
There appears to be a relationship between people's attitude towards automated decision-making, and the purposes to which it is put.
“There appears to be a relationship between people's attitude towards automated decision-making, and the purposes to which it is put.”
Quote in the paperSubstantial claim - Findingown data
The information added to the interface in the interest of transparency does not directly support task completion.
“The information added to the interface in the interest of transparency does not directly support task completion.”
Quote in the paperSubstantial claim - Findingown data
Availability of more information about smart charging system operation appears to inspire an intent to optimize behavior.
“Regardless of whether a participant was looking to improve charging speed, sustainability, or cost, availability of more information about smart charging system operation appears to inspire an intent to optimize behavior.”
Quote in the paperSubstantial claim - Findingown data
Many participants reported feeling overwhelmed by explanations.
“For all of these reasons, it should come as no surprise many participants reported feeling overwhelmed by explanations.”
Quote in the paperSubstantial claim - Findingnone given
A need for legal compliance and societal acceptance drives efforts to make AI transparent.
“A need for legal compliance and societal acceptance drives efforts to make AI transparent.”
Quote in the paperNot a substantial claim - Findingreasoning
Information provided in the interest of transparency is frequently experienced by citizens as burdensome.
“Information provided in the interest of transparency is frequently experienced by citizens as burdensome (C2).”
Quote in the paperSubstantial claim - Findingown data
Experts in our study talk about transparency in terms of making the hidden visible, as if an AI is something that can be seen and there is merely something obstructing our view in need of removal.
“Experts in our study talk about transparency in terms of making the hidden visible, as if an AI is something that can be seen and there is merely something obstructing our view in need of removal.”
Quote in the paperSubstantial claim - Findingown data
None of the study's participants spontaneously suggested they would use the transparency resources if they disagreed with the system's functioning.
“However, none of our participants spontaneously suggested they would use those resources if they disagreed with the system's functioning.”
Quote in the paperSubstantial claim - Findingown data
AI is seen as a convenient way of optimizing towards broadly shared collective interests such as electric grid stability and sustainability.
“AI is seen as a convenient way of optimizing towards broadly shared collective interests such as electric grid stability and sustainability.”
Quote in the paperNot a substantial claim - Findingown data
It is only when something is at stake (illustrated in our study by the shared car priority feature) that citizens start to question AI.
“It is only when something is at stake (illustrated in our study by the shared car priority feature) that citizens start to question AI (C1).”
Quote in the paperSubstantial claim - Findingreasoning
The absence of control leads some participants to question the relevance of explanations.
“The absence of control leads some participants to question the relevance of explanations.”
Quote in the paperSubstantial claim - Findingnone given
Experts did not question the decision to use an AI system in EV charging.
“Experts did not question the decision to use an AI system in EV charging.”
Quote in the paperSubstantial claim - Findingown data
The explanations created expectations of user control, understood as an ability to override automated decisions.
“Explanations created expectations of user control, an ability to override automated decisions (C4).”
Quote in the paperSubstantial claim - Findingnone given
The citizen participants intend to use explanations as a resource for adapting behavior towards altruistic or egoistic ends.
“Citizens intend to use explanations as a resource for adapting behavior towards altruistic or egoistic ends (C3).”
Quote in the paperSubstantial claim - Findingown data
The citizen participants question the relevance of explanations if they are not accompanied with the ability to override system decisions.
“Meanwhile, our citizen participants are largely indifferent to AI, and they primarily experience explanations as burdensome, and question their relevance if they are not accompanied with the ability to override system decisions.”
Quote in the paperSubstantial claim - Findingown data
The citizen participants are largely indifferent to AI.
“Meanwhile, our citizen participants are largely indifferent to AI, and they primarily experience explanations as burdensome, and question their relevance if they are not accompanied with the ability to override system decisions.”
Quote in the paperSubstantial claim - Findingnone given
In case of disagreements with automated decisions, most citizens opt to defect to an alternative means of charging rather than try to influence policies shaping system behavior.
“In case of disagreements with automated decisions, most citizens opt to defect to an alternative means of charging, rather than try and influence policies shaping system behavior (C4).”
Quote in the paperNot a substantial claim - Findingown data
The expert participants believe transparency is achieved by providing truthful information about automated decisions.
“Our expert participants believe transparency is achieved by providing truthful information about automated decisions.”
Quote in the paperSubstantial claim - Findingown data
The citizen participants primarily experience explanations as burdensome.
“Meanwhile, our citizen participants are largely indifferent to AI, and they primarily experience explanations as burdensome, and question their relevance if they are not accompanied with the ability to override system decisions.”
Quote in the paperSubstantial claim - Findingown data
The participants did not appear to feel they had a substantial say in the operation of the system.
“Our participants did not appear to feel they had a substantial say in the operation of the system.”
Quote in the paperSubstantial claim - Findingown data
The expert participants expect citizens can act on the provided information.
“They expect citizens are able to assess system fairness using this information, and that they can act on this information.”
Quote in the paperSubstantial claim - Premisecitation
Making AI system data and models visible is not the same as holding whole socio-technical assemblages accountable.
“Making AI system data and models visible is not the same as holding whole socio-technical assemblages accountable.”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisecitation
Increasing transparency by providing explanations can improve people's trust in AI systems.
“Increasing transparency by providing explanations can improve people's trust in AI systems (Kizilcec 2016;Eslami et al. 2018;Binns et al. 2018).”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites Kizilcec 2016
Not checked in the free run.
- cites Eslami et al. 2018
Not checked in the free run.
- cites Binns et al. 2018 source
Not checked in the free run.
- Premisereasoning
There are ways of increasing accountability that do not depend on transparency at all.
“Furthermore, there are ways of increasing accountability that do not depend on transparency at all.”
Quote in the paperSubstantial claim - Premisecitation
Transparency requires active production of information.
“The language of transparency suggests we are removing things obscuring our view, while in fact transparency requires active production of information (Menéndez-Viso 2009).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Menéndez-Viso 2009 source
Not checked in the free run.
- Premisecitation
Providing too much information can actually erode trust.
“Providing too much information can actually erode trust (Kizilcec 2016).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kizilcec 2016
Not checked in the free run.
- Interpretationreasoning
It is likely this situation is even worse outside of a prototype evaluation because when EV charging at a public charge point, people are likely to be in a rush.
“It is likely this situation is even worse outside of a prototype evaluation because when EV charging at a public charge point, people are likely to be in a rush.”
Quote in the paperNot a substantial claim- Rests on an assumption: People charging an EV at a public charge point are likely to be in a rush and have somewhere else to be.
- Premisecitation
Placebo explanations can still improve trust.
“For example, placebo explanations can still improve trust (Eiband et al. 2019).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Eiband et al. 2019
Not checked in the free run.
- Premisecitation
Explanations can be satisfying to users without necessarily being truthful.
“In addition, explanations can be satisfying to users without necessarily being truthful (Eslami et al. 2018).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Eslami et al. 2018
Not checked in the free run.
- Interpretationcitation
The project can be considered an example of how pursuing transparency sidelines the question of whether AI should be used at all.
“The project can be considered an example of how pursuing transparency sidelines the question “should we be using AI at all?” (De Laat 2019).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites De Laat 2019 source
Not checked in the free run.
- Interpretationown data
People's indifference to AI may be in part due to the fact that when charging in the city, less is at stake compared to a fast charging session.
“People's indifference to AI may be in part due to the fact that when charging in the city, less is at stake compared to say a fast charging session.”
Quote in the paperNot a substantial claim- Rests on an assumption: In city charging, less is at stake compared to fast charging; charge speed is slow, session duration is short, and public charge points are the least depended on.
- Premisecitation
Users typically cannot ascertain correctness of system output from an explanation alone.
“What is more, users typically cannot ascertain correctness of system output from an explanation alone (Rader et al. 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Rader et al. 2018
Not checked in the free run.
- Interpretationreasoning
This confusion is caused at least in part because participants think they are expected to act on it somehow.
“This confusion is caused at least in part because participants think they are expected to act on it somehow.”
Quote in the paperSubstantial claim - Premisecitation
The belief that being truthful leads to increased trust is not born out by previous research.
“However, the belief that being truthful leads to increased trust is not born out by previous research.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Eiband et al. 2019
Not checked in the free run.
- cites Eslami et al. 2018
Not checked in the free run.
- cites Rader et al. 2018
Not checked in the free run.
- Interpretationreasoning
Having the ability to assess fairness by itself does not equip a person to act on that assessment.
“Having the ability to assess fairness by itself does not equip a person to act on that assessment.”
Quote in the paperSubstantial claim - Interpretationcitation
The language experts use is at odds with the view that understanding a system requires more than seeing inside it.
“The language our experts use is also at odds with the view that understanding a system requires more than seeing inside it”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ananny and Crawford 2018 source
Not checked in the free run.
- Interpretationreasoning
The extent to which participants consider local government, power companies, etc. to be on their side also figures into how they perceive use of AI for automated decisions.
“Similarly, the extent to which participants consider local government, power companies, etc. to be “on their side” also figures into how they perceive use of AI for automated decisions.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Citizens apparently perceive the benefits of engaging with explanations to not outweigh the costs.
“Citizens apparently perceive the benefits of engaging with explanations to not outweigh the costs.”
Quote in the paperSubstantial claim - Interpretationcitation
What experts do in the project supports the notion that transparency requires active production of information.
“However, what we see them do in our project is something quite different, and supports the notion that transparency requires active production of information (Menéndez-Viso 2009).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Menéndez-Viso 2009 source
Not checked in the free run.
- Premisecitation
The actionability of explanations is influenced not only by their content but also by their format.
“However, actionability is influenced not only by content but also by the format of explanations.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Vakarelov and Rogerson 2020 source
Not checked in the free run.
- Interpretationreasoning
Actionable information by itself does not appear to provide sufficient means of influence over system behavior.
“However, actionable information by itself does not appear to provide sufficient means of influence over system behavior.”
Quote in the paperSubstantial claim - Interpretationown data
If we consider responses of some citizens to the explanations provided, it appears trust may have decreased instead of improved.
“If we consider responses of some citizens to the explanations provided, it appears trust may have decreased instead of improved.”
Quote in the paperSubstantial claim - Interpretationown data
The citizens' desire to change behavior in response to explanations suggests the information is actionable to some degree.
“The fact that citizens indicate desire to change behavior in response to explanations suggests information is actionable to some degree.”
Quote in the paperSubstantial claim - Interpretationreasoning
The pursuit of transparency serves to support the ongoing use of AI.
“The pursuit of transparency serves to support the ongoing use of AI.”
Quote in the paperSubstantial claim - Interpretationcitation
A limited form of transparency offering justification of decisions is more likely to increase perceived legitimacy of a system, rather than the more extensive transparency of the automated decision-making process itself attempted in this project.
“It may also be the case that a limited form of transparency offering justification of decisions is more likely to increase perceived legitimacy of a system, rather than the more extensive transparency of the automated decision-making process itself attempted in this project (Fine Licht and Fine Licht 2020).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Fine Licht and Fine Licht 2020 source
Not checked in the free run.
- Premisecitation
Prior experiences with organizations deploying AI influence the extent to which people trust AI.
“Prior experiences with organizations deploying AI influence the extent to which people trust AI (Brown et al. 2019).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Brown et al. 2019
Not checked in the free run.
- Interpretationreasoning
Our project more closely matches the category of explanations of AI decisions with only an indirect connection to user actions.
“Our project more closely matches the latter category.”
Quote in the paperSubstantial claim - Interpretationcitation
A more likely reason for citizens not engaging with explanations is that they believe they can form an adequate mental model of the system by simply using it, where adequate may be next to no model at all.
“A more likely reason is that citizens believe they can form an adequate mental model of the system by simply using it, where “adequate” may be next to no model at all (Bunt et al. 2012).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Bunt et al. 2012
Not checked in the free run.
- Premisecitation
The literature reviewed provides a mixed view of the relationships between transparency, understanding, fairness and trust.
“However, the literature we have reviewed provides a mixed view of the relationships between transparency, understanding, fairness and trust (Binns 2018;Cheng et al. 2019;Kizilcec 2016).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Binns 2018 source
Not checked in the free run.
- cites Cheng et al. 2019
Not checked in the free run.
- cites Kizilcec 2016
Not checked in the free run.
- Interpretationreasoning
There exists a tension between experts' desire to make citizens aware of the presence of an AI and the aim of old-fashioned user interface design in support of user tasks.
“There exists a tension here between experts' desire to make citizens aware of the presence of an AI and the aim of old-fashioned user interface design in support of user tasks.”
Quote in the paperSubstantial claim - Premisecitation
AI systems mediate the communicative process of transparency, inviting some actions and inhibiting others.
“AI systems mediate this process, inviting some actions, and inhibiting others (Verbeek 2006).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Verbeek 2006 source
Not checked in the free run.
- Interpretationnone given
Transparency puts additional cognitive demands on people.
“Transparency puts additional cognitive demands on people, and shifts responsibility of ensuring fairness onto them, reinforcing a neoliberal model of agency.”
Quote in the paperSubstantial claim - Interpretationreasoning
Explanations by themselves are not always perceived as sufficiently actionable.
“This underscores the fact that explanations by themselves are not always perceived as sufficiently actionable.”
Quote in the paperSubstantial claim - Interpretationreasoning
In this project, transparency alone was insufficient for creating any form of agency beyond EV drivers simply not buying the services of the charge point they disagree with, a reduction of citizen participation to consumer choice.
“In any case, we feel that in our project, transparency alone was insufficient for creating any form of agency beyond EV drivers simply not”
Quote in the paperSubstantial claim - Study design and conductnone given
All of the study's citizen participants were direct stakeholders and users of the system.
“Indeed, our citizens were all direct stakeholders and users of the system.”
Quote in the paperNot a substantial claim
- Classified as probably not a substantial claim. 61
- May combine two claims. 37
- The quote only partly states this claim. 3
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenExperts hope transparency ensures acceptance.
“experts hope transparency ensures acceptance”
Quote in the paperNot a substantial claim- Rests on an assumption: The experts participating in the design project represent the views of experts involved in urban AI systems more broadly.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataP2 made a connection between shared car priority and local politics, and expressed fear of politicians pushing for more extreme forms of shared car priority at the expense of private car owners.
“P2, a resident of Amsterdam, made a connection between shared car priority and local politics, which recently had taken a more left-leaning, progressive turn than in years before.They expressed fear of politicians pushing for more extreme forms of shared car priority at the expense of private car owners.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataMost participants were able to translate a session's worth of energy charged measured in kWh to range, because they had memorized the capacity of their EV battery.
“Most participants were also able to translate a session's worth of energy charged measured in kWh to range, because they had memorized the capacity of their EV battery.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataMany participants latched on to the shared car priority feature, while ignoring most of the other rules made transparent.
“Many participants latched on to this, while ignoring most of the other rules made transparent.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingreasoningThe system is imagined to convey a message that people are indeed being treated fairly.
“The system is imagined to convey a message that people are indeed being treated fairly.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingown dataTruthfulness is seen as a quality of information provided by the system, producing transparency about how a person is treated.
“Truthfulness is seen as a quality of information provided by the system, producing transparency about how a person is treated.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
FindingreasoningThe concern is that opacity threatens acceptance of EV driving and charging by citizens.
“The concern is that opacity threatens acceptance of EV driving and charging by citizens.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
FindingreasoningPrioritization appears to produce dilemmas: some people will lose and others will win out in resource distribution.
“Prioritization appears to produce dilemmas.Some people will lose and others will win out in resource distribution.”
Quote in the paperSubstantial claim- Rests on an assumption: When a city service is scarce, prioritization is required.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataSome participants wondered about what was considered a shared car, who determined this, and how the system would deal with shared cars from outside of Amsterdam.
“Some participants wondered about what was considered a shared car, who determined this, and how the system would deal with, for example, shared cars from outside of Amsterdam.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataA few participants did express concerns about situations in which they would be disadvantaged by the system, and the impossibility of making a one-time exception.
“A few participants did express concerns about situations in which they would be disadvantaged by the system, and the impossibility of making a one-time exception.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataParticipants expressed intent to adapt their behavior to the system, something that could be considered an unintended side effect of providing more transparency.
“Participants expressed intent to adapt their behavior to the system, something that could be considered an unintended side effect of providing more transparency.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingreasoningTruthfulness and fairness recur on different levels throughout the project.
“We see truthfulness and fairness recur on different levels throughout the project.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataAnother reason for changing behavior particularly in relation to solar power availability was sustainability.
“Another reason for changing behavior particularly in relation to solar power availability was sustainability.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSome participants recognized shared car priority would be beneficial for sustainability reasons, so they did accept its rationale.
“Some recognized it would be beneficial for sustainability reasons, so they did accept its rationale.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataParticipants suggested they might pick a different time to charge, so that they would benefit from increased speed during off-peak hours, or would enjoy extra speed when the sun was out.
“Participants suggested they might pick a different time to charge, so that they would benefit from increased speed during off-peak hours, or would enjoy extra speed when the sun was out.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataV2 switched to kilowatt (kW), and participants could at least extrapolate from this real-time measure to an expected amount of energy received at session end.
“V2 switched to kilowatt (kW), a measure of power.Participants could at least extrapolate from this real-time measure to an expected amount of energy received at session end.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingcitationExperts talk about transparency as something created by providing truthful information about automated decisions.
“Experts talk about transparency as something created by providing truthful information about “automated decisions” (D4).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites D4
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThroughout the documents, decision-making and prioritization are used interchangeably.
“However, throughout the documents, decision-making and prioritization are used interchangeably.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe design agency emphasized they had moved away from determining fairness by comparison.
“For example, at one point during the design process, the design agency emphasized they had moved away from determining fairness by comparison.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing).
FindingcitationThe core of the message is that the interests of different parties are fairly represented in order to arrive at solutions that work for citizens, government and private parties as smoothly as possible.
“The core of the message is that the interests of different parties are fairly represented in order to arrive at solutions that work for citizens, government and private parties as smoothly as possible.(D4)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites D4
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
FindingreasoningNot only decisions, but motivations for them must be made transparent.
“We can also see that not only decisions, but motivations for them must be made transparent.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAll this mental arithmetic meant more work for participants, and although some did take pride in their ability to perform it, most were perfectly happy to offload all of it onto a system.
“Needless to say, all this mental arithmetic meant more work for participants, and although some did take pride in their ability to perform it, most were perfectly happy to offload all of it onto a system.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingcitationProject members agreed this message should be conveyed using a positive tone of voice.
“Project members agreed this message should be conveyed using a “positive tone of voice” (D4).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites D4
Not checked in the free run.
May combine two claims.
FindingcitationAlgorithms that currently control smart city objects are black boxes: the public is affected by their decisions but does not know what factors are taken into consideration and how they are weighed against each other to reach a decision.
“However, algorithms that currently control smart city objects are “black boxes”: the public is affected by their decisions, but does not know what factors are taken into consideration and how they are weighed against each other to reach a decision.(D10)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites D10
Not checked in the free run.
Probably not a substantial claim: may not pass S1 (asserted). May combine two claims.
Findingnone givenExperts believe that, because automated decisions might benefit some more than others, and because AI is by its nature hidden, they need to be made visible.
“Experts believe that, because automated decisions might benefit some more than others, and because AI is by its nature hidden, they need to be made visible (X1).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataEV charging is often a suboptimal experience, made worse by the additional demands transparency puts on people.
“With this theme, we capture how EV charging is often a suboptimal experience, made worse by the additional demands transparency puts on people.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
FindingcitationVisibility of the automatic decision making in the smart charging process can help the adoption of this new technology.
“Visibility [of] the automatic decision making in the smart charging process can help the adoption of this new technology. (D10)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites D10
Not checked in the free run.
Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing).
FindingcitationOne of the aims of the design project is to convey a positive message about the municipality's role in the transition to fully electric driving in the city.
“One of the aims of the design project is to convey a “positive message” (D4) about the municipality's role in the transition to fully electric driving in the city.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites D4
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
Findingnone givenUsing transparency, users should be able to determine if they have been fairly treated.
“Using transparency, users should be able to determine if they have been fairly treated.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims. The quote only partly states this claim.
Findingnone givenThe expert participants presume explanations make it possible for citizens to assess the fairness of decisions by evaluating inputs, processes and outcomes of the AI, by having access to a justification for the AI's design, and by knowing who owns the AI.
“Experts presume explanations make it possible for citizens to assess the fairness of decisions by evaluating inputs, processes and outcomes of”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe expert participants believe explanations are actionable by citizens.
“Experts believe explanations are actionable by citizens (X2).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Findingnone givenA typical line of reasoning is that technology can improve on contested issues, and should, therefore, be welcomed.
“A typical line of reasoning is that technology can improve on these issues, and should, therefore, be welcomed.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenAt the same time it is felt AI lacks transparency and that this should be fixed.
“At the same time it is felt AI lacks transparency and that this should be fixed.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
FindingreasoningExperts pursue transparency because citizens may reject use of automated decision-making in public infrastructure.
“Experts pursue transparency because citizens may reject use of automated decision-making in public infrastructure (X1).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Findingnone givenAI opacity as such is hardly ever an issue for citizens.
“AI opacity as such is hardly ever an issue for citizens.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingreasoningThe experience of citizens is characterized by an overall acceptance of, or even indifference to, the presence of automated decision-making.
“The experience of citizens is characterized by an overall acceptance of, or even indifference to, the presence of automated decision-making (C1).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenExperts implement AI to achieve energy infrastructure reliability and sustainability.
“Experts implement AI to achieve energy infrastructure reliability and sustainability.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn this project, the experts did not express worries about gaming behavior or exposure of intellectual property.
“In our project, we did not see experts express such worries.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
FindingreasoningThe reasoning in the project seems to be that once citizens understand the workings of a system, they can ascertain its fairness, and once they know it is fair, they will accept the system.
“Various factors may lead to this acceptance, but the reasoning in the project seems to be that once citizens understand the workings of a system, they can ascertain its fairness, and once they know it is fair, they will accept the system.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Findingnone givenFor our experts, the goal is acceptance by the public, EV drivers in particular.
“For our experts, the goal is acceptance by the public, EV drivers in particular.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataTransparency enabled citizens to see some of the decisions affecting charge speed, and in some cases had them wondering about system developer motivations to include those factors, most notably in the case of the shared car priority feature.
“Transparency enabled them to see some of the decisions affecting charge speed, and in some cases had them wondering about system developer motivations to include those factors, most notably in the case of the shared car priority feature.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
FindingreasoningThis is certainly a general driver behind the push for electric mobility in Amsterdam.
“This is certainly a general driver behind the push for electric mobility in Amsterdam.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingown dataThe expert participants expect citizens are able to assess system fairness using the provided information.
“They expect citizens are able to assess system fairness using this information, and that they can act on this information.”
Quote in the paperSubstantial claimMay combine two claims.
FindingreasoningThe citizens do not want to, or believe they are not able to, petition experts for changes to system behavior, despite the presence of explanations that could be leveraged for purposes of recourse.
“Citizens do not want to, or believe they are not able to, petition experts for changes to system behavior, despite the presence of explanations that could be leveraged for purposes of recourse.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. The quote only partly states this claim.
Study design and conductown dataTo improve ecological validity, the study did not provide participants with details on how the system operates beyond telling them it was a 'smart' EV charge point adjusting speed based on unspecified factors.
“To improve ecological validity, we did not provide participants with details on how the system operates, beyond telling them we were testing the design of a”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationIn HCI research, transparency is typically framed as a form of objective knowledge that empowers people to make informed choices about how best to use and govern AI systems.
“In this HCI-research, transparency is typically framed as a form of objective knowledge that empowers people to make informed choices about how best to use and govern AI systems.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Abdul et al. 2018
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAlmost all the data were included in the themes.
“Almost all the data were included in the themes.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationMany ethical and policy frameworks see transparency as an important prerequisite for ensuring fairness and public acceptance.
“Many ethical and policy frameworks see transparency as an important prerequisite for ensuring fairness and public acceptance (Brauneis and Goodman 2018;Stoyanovich and Howe 2018).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Brauneis and Goodman 2018
Not checked in the free run.
- cites Stoyanovich and Howe 2018
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationTrust does not appear to be impacted by people's objective understanding of systems, nor by the form of explanation used.
“Trust does not appear to be impacted by people's objective understanding of systems, nor by the form of explanation used (Cheng et al. 2019).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Cheng et al. 2019
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe second author independently coded a subset of the data, and a refined set of themes was jointly developed.
“The second author independently coded a subset of the data, and a refined set of themes was jointly developed.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningTransparency cannot be reduced to a product feature, but should be seen as a mediator of debate between experts and citizens.
“The tensions we identify suggest transparency cannot be reduced to a product feature, but should be seen as a mediator of debate between experts and citizens.”
Quote in the paperNot a substantial claim- Rests on an assumption: The prototype evaluation with a small number of EV drivers is a valid basis for conclusions about how citizens experience transparency in urban AI systems.
- Rests on an assumption: The experts participating in the design project represent the views of experts involved in urban AI systems more broadly.
- Rests on an assumption: The prototype's transparency interface accurately represents the smart charging system's rules and behavior.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe first author checked the codes and themes with the commercial collaborators.
“The first author also checked the codes and themes with the commercial collaborators.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Interpretationown dataThree tensions were constructed from a comparison between the two groups of narrative themes: information quality over quantity, level of concern, and sense of control.
“In this next section, we reflect on three tensions constructed from a comparison between the two groups of narrative themes (Fig. 4).These are: (1) information quality over quantity; (2) level of concern; (3) sense of control.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningSince participants are largely indifferent to AI, this information is experienced mostly as a burden.
“Since, as we captured with the previous theme, participants are largely indifferent to AI, this information is experienced mostly as a burden.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationFor an explanation to be actionable, it must be usable as currency in a person's decision-making process.
“For an explanation to be actionable, it must be usable as”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Vakarelov and Rogerson 2020 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationPeople find AI useful, but the AI systems people find most useful is not necessarily the one they find most fair.
“This echoes others' findings that people find AI useful, but crucially also that the AI systems people find most useful is not necessarily the one they find most fair (Dolin et al. 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Dolin et al. 2018
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationUsers can have favorable cost-benefit perceptions of explanations if they are able to act on provided information by adjusting system behavior.
“Users can have favorable cost-benefit perceptions of explanations if they are able to act on provided information by adjusting system behavior (Kulesza et al. 2013).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kulesza et al. 2013
Not checked in the free run.
May combine two claims.
Interpretationown dataBeing critical is not necessarily the same as being distrustful, but uncritical acceptance is unlikely to be the result of transparency.
“Being critical is not necessarily the same as being distrustful, but uncritical acceptance is unlikely to be the result of transparency.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationcitationIf acceptance is the aim, relying on transparency is likely insufficient or may even backfire, for example when an excess of information decreases people's trust.
“What this means is that if acceptance is the aim, relying on transparency is likely insufficient or may even backfire, for example when an excess of information decreases people's trust (Kizilcec 2016).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kizilcec 2016
Not checked in the free run.
May combine two claims.
InterpretationreasoningEach response impacts citizen trust in positive or negative ways, which in turn affects social acceptance desired by experts.
“Each response impacts citizen trust in positive or negative ways, which in turn affects social acceptance desired by experts”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTransparency paired with control can alleviate anxiety caused by automated decisions.
“Others have shown transparency paired with control can alleviate anxiety caused by automated decisions (Jhaver et al. 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Jhaver et al. 2018
Not checked in the free run.
Classified as probably not a substantial claim.
InterpretationcitationThe position of experts mirrors the idea that transparency has no moral content, it is a means towards an end.
“The position of experts here mirrors the idea that transparency has no moral content (Menéndez-Viso 2009), it is a means towards an end.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Menéndez-Viso 2009 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe tension between transparency and the possibility of gaming behavior, and concerns over exposure of intellectual property, have been noted previously.
“The tension between transparency and the possibility of”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Alvarado and Waern 2018
Not checked in the free run.
- cites Jhaver et al. 2018
Not checked in the free run.
Classified as probably not a substantial claim.
InterpretationcitationInformation quality over quantity is the actual moral problem of transparency.
“In other words, information quality over quantity is the actual moral problem of transparency (Menéndez-Viso 2009).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Menéndez-Viso 2009 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe various motivations our participants have for EV driving may influence how they respond to AI features exposed to them in the interface.
“The various motivations our participants have for EV driving (e.g., saving money, protecting the environment) may influence how they respond to AI features exposed to them in the interface.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThis transparency increases citizen awareness, which triggers several responses: concern over optimization targets, increased cognitive burden, various forms of adaptive behavior, and an increased desire for control.
“This transparency increases citizen awareness, which triggers several responses: concern over optimization targets, increased cognitive burden, various forms of adaptive behavior, and an increased desire for control.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningPotentially, also in our project, people are making trade-offs between fairness and usefulness.
“Potentially, also in our project, people are making trade-offs between fairness and usefulness.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenThis point of tension is of particular importance when dealing with street-level touch-points of urban AI systems, because people's attention tends to be even more limited due to pressures of everyday urban activities.
“This point of tension is of particular importance when dealing with street-level touch-points of urban AI systems, because people's attention tends to be even more limited due to pressures of everyday urban activities.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationThe information produced in the interest of transparency can occlude as much as it reveals, and in the process add to anxiety already felt in response to being subjected to automated decision-making.
“The information produced in the interest of transparency can occlude as much as it reveals, and in the process add to anxiety already felt in response to being subjected to automated decision-making (Ananny and Crawford 2018;Caduff 2017).”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
PremisecitationDifferent people respond to transparency in different ways, and as a result, people also trust a given system in different ways.
“Others have pointed out different people respond to transparency in different ways.As a result, people also trust a given system in different ways (Ananny and Crawford 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ananny and Crawford 2018 source
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
InterpretationreasoningWhat is not considered is how a person's view on an issue like spatial justice may affect the extent to which they welcome EV charging, and by extension a smart charging system, regardless of how transparent it is.
“What is not considered is how a person's view on an issue like spatial justice may affect the extent to which they welcome EV charging, and by extension a smart charging system, regardless of how transparent it is.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningTransparency shifts the responsibility of ensuring fairness onto people, reinforcing a neoliberal model of agency.
“Transparency puts additional cognitive demands on people, and shifts responsibility of ensuring fairness onto them, reinforcing a neoliberal model of agency.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationTransparency invokes a neoliberal model of agency.
“Some have argued transparency invokes a neoliberal model of agency (Ananny and Crawford 2018;Cardullo and Kitchin 2017;Kitchin 2019).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Ananny and Crawford 2018 source
Not checked in the free run.
- cites Cardullo and Kitchin 2017
Not checked in the free run.
- cites Kitchin 2019
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningUnderstanding of a system is not the product of simply receiving and processing information.
“Understanding of a system is not the product of simply receiving and processing information.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningAll three tensions identified in the study (information quantity, level of concern, desire for control) point to the need for additional channels for voice through which debate between stakeholders can be facilitated.
“All three tensions we identify (information quantity, level of concern, desire for control) in various ways point to the need for additional channels for voice through which this debate can be facilitated.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningIt is likely that the high education of the citizen sample informs their attitudes to issues such as sustainability and automation.
“It is likely this informs their attitudes to issues such as sustainability and automation.”
Quote in the paperNot a substantial claim- Rests on an assumption: At the time of the study, EV driving is accessible mostly to significantly affluent or professionally employed people.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThis project is a clear illustration of the neoliberal logic of transparency in action.
“We think our project is a clear illustration of this logic in action.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe study's sample of citizens skews highly educated.
“Certainly, our sample of citizens skews highly educated.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningUnderstanding emerges from debate between stakeholders and is always provisional.
“Understanding emerges from debate between stakeholders, and is always provisional.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThe study's expert-citizen distinction leaves out a lot of other relevant stakeholders to investigate.
“Our expert-citizen distinction leaves out a lot of other relevant stakeholders to investigate.”
Quote in the paperNot a substantial claim- Rests on an assumption: There are many citizens who are not EV drivers but who are likely to be impacted by the roll-out of EV charging infrastructure in various ways.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThere are many stakeholders who have relevant expertise but who are not considered experts in the sense used in this study, that is, those with some level of formal influence over the shape the system takes.
“There are also a good deal of stakeholders who have relevant expertise, but who are not considered”
Quote in the paperNot a substantial claim- Rests on an assumption: There are many citizens who are not EV drivers but who are likely to be impacted by the roll-out of EV charging infrastructure in various ways.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningTransparency should not be seen as a property of technology or of a system through which information flows from experts to individual users, but must be understood as a communicative process between experts and citizens, mediated by AI systems.
“In closing, we argue these tensions show transparency should not be seen as a property of technology, but must be understood as a communicative process between experts and citizens, who are more than mere users.”
- Also stated in: § 7 Conclusion · plain
“It should not be seen as a property of a system through which information flows from experts to individual users.Rather, transparency must be seen as a communicative process between experts and citizens, mediated by AI systems.”
Quote in the paperNot a substantial claim- Also stated in: § 7 Conclusion · plain
May combine two claims.
InterpretationreasoningThe citizens' desire for control appears to be at odds with the experts not being willing or able to offer direct control over system behavior and anticipating explanations alone to be a sufficient form of accountability.
“This desire for control appears to be at odds with the fact that experts are not willing or able to offer direct control over system behavior and anticipate explanations alone to be a sufficient form of accountability.”
Quote in the paperSubstantial claimThe quote only partly states this claim.
InterpretationreasoningThe design solutions pursued in this project may not be viable when dealing with stochastic AI systems.
“The design solutions pursued in our project may not be viable when dealing with stochastic systems.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
The prototype evaluation with a small number of EV drivers is a valid basis for conclusions about how citizens experience transparency in urban AI systems.
“Overall, we conducted two rounds of one-day long design evaluations: round one included five participants (P1 through P5; 1 female, 4 male), whereas round two included four participants (P6 through P9; 1 female, 3 male).”
The paper relies on this without support.
The experts participating in the design project represent the views of experts involved in urban AI systems more broadly.
“we collaborated with commercial companies and the municipality of Amsterdam to prototype and evaluate a transparent smart EV charge point”
The paper relies on this without support.
The prototype's transparency interface accurately represents the smart charging system's rules and behavior.
“The prototype screens were created in a graphics package and data reflecting the imagined scenario of use was added.”
The paper relies on this without support.
When a city service is scarce, prioritization is required.
“When a city service is scarce, prioritization is required.”
The paper relies on this without support.
In city charging, less is at stake compared to fast charging; charge speed is slow, session duration is short, and public charge points are the least depended on.
“People's indifference to AI may be in part due to the fact that when charging in the city, less is at stake compared to say a fast charging session.Charge speed is slow, session duration is short, and out of all charging options (at home, at work, at a fast charging facility, in the city) public charge points are the least depended on.”
The paper relies on this without support.
Users do not read instructions (a usability truism).
“It is a usability truism users do not read, and people charging an EV are clearly no exception.”
The paper relies on this without support.
People charging an EV at a public charge point are likely to be in a rush and have somewhere else to be.
“It is likely this situation is even worse outside of a prototype evaluation because when EV charging at a public charge point, people are likely to be in a rush.They might have someone else waiting to use the same charge point, and in any case, they will probably have somewhere else to be.”
The paper relies on this without support.
Time and place of a charging session is strongly dictated by circumstances, such as availability of free parking spots.
“It could also be because, particularly in the context of city charging, time and place of a charging session is strongly dictated by circumstances, such as availability of free parking spots.”
The paper relies on this without support.
A vast number of decisions are made by city governments benefiting some more than others, and a lot of city governance recedes from the view of ordinary citizens.
“A vast number of decisions are made by city governments benefiting some more than others, and a lot of city governance recedes from the view of ordinary citizens.”
The paper relies on this without support.
At the time of the study, EV driving is accessible mostly to significantly affluent or professionally employed people.
“At the moment, EV driving is accessible mostly to significantly affluent or professionally employed people.”
The paper relies on this without support.
There are many citizens who are not EV drivers but who are likely to be impacted by the roll-out of EV charging infrastructure in various ways.
“There are plenty of citizens who are not EV drivers, but who are likely to be impacted by the roll-out of EV charging infrastructure in various ways.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Providing decision subjects with information beyond outcome explanations could support contestations that apply to the system lifecycle as a whole.
“Our results (e.g., Q.13) suggest that providing decision subjects with information that goes beyond outcome explanations could support contestations that are not only limited to post-decision mechanisms but that apply to the system lifecycle as a whole [2].”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [2]
In “Experts believe explanations are actionable by citizens” (page unavailable), the paper describes information about inputs, processes, outcomes, design, and ownership—not just decision outcomes. In the discussion of citizens’ responses (page unavailable), it proposes dialogue with designers, developers, and operators, but says information alone is insufficient without channels for citizens to voice disagreements. It does not establish that providing broader information would support contestation across the system lifecycle as a whole. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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BackgroundHow the machine “thinks”: Understanding opacity in machine learning algorithms
Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · cited 1× · see citing works
“Opacity of AI systems can stem from a variety of sources: deliberate secrecy by system developers and operators; lack of technical literacy of the observer; or technical properties of systems themselves (Burrell 2016).”
BackgroundAnanny, M., & Crawford, K. (2018) · New Media & Society · doi:10.1177/1461444816676645 · Cited by 1,836 · cited 7× · see citing works
“As a result, people also trust a given system in different ways (Ananny and Crawford 2018).”
BackgroundInfluentialGeels, F. W. (2012) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2012.01.021 · Cited by 1,186 · cited 1× · see citing works
“Electric mobility is seen by many cities as a key way to improve efficiency and equity of the flow of goods and people, and to reduce negative externalities including air pollution and climate change (Geels 2012).”
BackgroundExplainable Recommendation: A Survey and New Perspectives
Zhang, Y., & Chen, X. (2020) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000066 · Cited by 755 · cited 1× · see citing works
“When models are developed with techniques producing opaque or “black box” models resisting human interpretation, explanations can still be produced in a post-hoc fashion by means of a supplemental explanation model (Zhang and Chen 2020; Kim et al. 2020).”
MethodAbdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174156 · Cited by 743 · cited 1× · see citing works
“Empirical research in human–computer interaction (HCI) has focused on identifying which forms of user interface-level transparency are most effective for increasing user understanding and trust (Abdul et al. 2018).”
Background'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions
Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173951 · Cited by 691 · cited 3× · see citing works
“However, the literature we have reviewed provides a mixed view of the relationships between transparency, understanding, fairness and trust (Binns 2018; Cheng et al. 2019; Kizilcec 2016).”
BackgroundExplainable Recommendation a survey and new perspectives
Zhang, Y., & Chen, X. (2020) · Cited by 689 · see citing works
Materializing Morality: Design Ethics and Technological Mediation
Verbeek, P.-P. (2006) · Science, Technology, & Human Values · doi:10.1177/0162243905285847 · Cited by 683 · cited 1× · see citing works
“AI systems mediate this process, inviting some actions, and inhibiting others (Verbeek 2006).”
BackgroundHow Much Information?: Effects of Transparency on Trust in an Algorithmic Interface
Kizilcec, R. F. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858402 · Cited by 615 · cited 5× · see citing works
“However, the literature we have reviewed provides a mixed view of the relationships between transparency, understanding, fairness and trust (Binns 2018; Cheng et al. 2019; Kizilcec 2016).”
BackgroundInfluentialTo Save Everything, Click Here: The Folly of Technological Solutionism
Tonkinwise, C. (2013) · Journal of Design History · doi:10.1093/jdh/ept034 · Cited by 559 · cited 2× · see citing works
“In other words, smart EV charging can be seen as a form of “technological solutionism” (Morozov 2013), where social ills are framed as problems to be fixed by means of technology, while avoiding structural change (Foth et al.”
BackgroundParticipatory action research and the public sphere
Kemmis, S. (2006) · Educational Action Research · doi:10.1080/09650790600975593 · Cited by 532 · see citing works
Algorithms and their others: Algorithmic culture in context
Dourish, P. (2016) · Big Data & Society · doi:10.1177/2053951716665128 · Cited by 481 · cited 1× · see citing works
“For this, it is necessary to see who has power to change systems, and to be able to experiment with changes ourselves (Ananny and Crawford 2018; Hollanek 2020; Dourish 2016).”
BackgroundAlgorithmic Accountability and Public Reason
Binns, R. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0263-5 · Cited by 457 · cited 1× · see citing works
“However, the literature we have reviewed provides a mixed view of the relationships between transparency, understanding, fairness and trust (Binns 2018; Cheng et al. 2019; Kizilcec 2016).”
BackgroundHCI and environmental sustainability
Dourish, P. (2010) · ACM Conference on Designing Interactive Systems (DIS) · doi:10.1145/1858171.1858173 · Cited by 427 · cited 1× · see citing works
“This suggests efforts to make urban AI more transparent should be paired with more participatory and collaborative approaches to city governance and policy-making (Dourish 2010; Foth 2018; Frauenberger et al. 2018).”
BackgroundTransparency in Algorithmic and Human Decision-Making: Is There a Double Standard?
Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. (2019) · Philosophy & Technology · doi:10.1007/s13347-018-0330-6 · Cited by 420 · cited 1× · see citing works
“This can be seen as an example of setting too high standards of explanation for machine decisions (Zerilli et al. 2019).”
BackgroundSmart Charging Strategy for Electric Vehicle Charging Stations
Moghaddam, Z., Ahmad, I., Habibi, D., & Phung, Q. V. (2017) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2017.2753403 · Cited by 412 · cited 1× · see citing works
“Smart charging allows for dynamic management of demand by curtailing the rate and amount of electricity EVs can charge when connected to a charge point (Wang et al. 2016; Mehta et al. 2018; Moghaddam et al. 2018; Frendo et al. 2019).”
BackgroundToo much, too little, or just right? Ways explanations impact end users' mental models
Kulesza, T., Stumpf, S., Burnett, M., Yang, S., Kwan, I., & Wong, W.-K. (2013) · 2013 IEEE Symposium on Visual Languages and Human Centric Computing · doi:10.1109/vlhcc.2013.6645235 · Cited by 388 · cited 4× · see citing works
“1 3 perception does hinge on users being able to adjust system behavior (Kulesza et al. 2013).”
BackgroundInfluentialExplanations as Mechanisms for Supporting Algorithmic Transparency
Rader, E., Cotter, K., & Cho, J. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173677 · Cited by 388 · cited 3× · see citing works
“Various kinds of explanations can contribute to people’s understanding of an AI system (Rader et al. 2018).”
BackgroundInfluentialde Fine Licht, K., & de Fine Licht, J. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-00960-w · Cited by 334 · cited 1× · see citing works
“…be the case that a limited form of transparency offering justification of decisions is more likely to increase perceived legitimacy of a system, rather than the more extensive transparency of the automated decision-making process itself attempted in this project (Fine Licht and Fine Licht 2020).”
BackgroundChalmers, M., & Galani, A. (2004) · Designing Interactive Systems Conference · doi:10.1145/1013115.1013149 · Cited by 315 · cited 2× · see citing works
“Furthermore, it is also possible that, rather than alleviating frustrations around lack of control through something akin to seamful design (Alvarado and Waern 2018; Chalmers and Galani 2004), further opening up of systems actually produces even greater anxiety in users who are already overwhelmed by explanations.”
BackgroundSmart Energy Technologies in Everyday Life
Strengers, Y. (2013) · Palgrave Macmillan UK eBooks · doi:10.1057/9781137267054 · Cited by 293 · cited 1× · see citing works
“A focus on solving the problem of demand also distracts from rethinking everyday practices requiring energy in the first place (Strengers 2014, 2013).”
BackgroundExplaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders
Cheng, H.-F., Wang, R., Zhang, Z., O'Connell, F., Gray, T., Harper, F. M., & Zhu, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300789 · Cited by 292 · cited 2× · see citing works
“However, the literature we have reviewed provides a mixed view of the relationships between transparency, understanding, fairness and trust (Binns 2018; Cheng et al. 2019; Kizilcec 2016).”
BackgroundSmart Charging for Electric Vehicles: A Survey From the Algorithmic Perspective
Wang, Q., Liu, X., Du, J., & Kong, F. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2518628 · Cited by 275 · cited 1× · see citing works
“Smart charging allows for dynamic management of demand by curtailing the rate and amount of electricity EVs can charge when connected to a charge point (Wang et al. 2016; Mehta et al. 2018; Moghaddam et al. 2018; Frendo et al. 2019).”
BackgroundMehta, R., Srinivasan, D., Khambadkone, A. M., Yang, J., & Trivedi, A. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2550559 · Cited by 267 · cited 1× · see citing works
“Smart charging allows for dynamic management of demand by curtailing the rate and amount of electricity EVs can charge when connected to a charge point (Wang et al. 2016; Mehta et al. 2018; Moghaddam et al. 2018; Frendo et al. 2019).”
BackgroundHuang, Y., & Kockelman, K. M. (2019) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2019.11.008 · Cited by 265 · see citing works
Strengers, Y. (2014) · interactions · doi:10.1145/2621931 · Cited by 256 · cited 1× · see citing works
“A focus on solving the problem of demand also distracts from rethinking everyday practices requiring energy in the first place (Strengers 2014, 2013).”
BackgroundThe spectrum of control: A social theory of the smart city
Sadowski, J., & Pasquale, F. A. (2015) · First Monday · doi:10.5210/fm.v20i7.5903 · Cited by 226 · cited 1× · see citing works
“…relying on exit alone is problematic because citizens should have a say in the operation of these systems, and limiting participation to “voting with your feet” infringes on people’s right to the city (Shaw and Graham 2017; Sadowski and Pasquale 2015; Cardullo and Kitchin 2017; Foth et al. 2015).”
BackgroundDesigning for human rights in AI
Aizenberg, E., & van den Hoven, J. (2020) · Big Data & Society · doi:10.1177/2053951720949566 · Cited by 220 · cited 1× · see citing works
“However, a small but growing body of work seeks to bridge the gap between advocating for abstract principles and supporting design choices situated in context (Aizenberg and Hoven 2020) and opening up AI development processes to a broader range of stakeholders (Krafft et al. 2021).”
BackgroundBrown, A., Chouldechova, A., Putnam-Hornstein, E., Tobin, A., & Vaithianathan, R. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300271 · Cited by 207 · cited 2× · see citing works
“For example, looking at the application of AI in child welfare services, Brown et al. (2019) find people’s distrust of non-automated systems increases their discomfort with AI.”
BackgroundStrengers, Y. (2012) · Energy Policy · doi:10.1016/j.enpol.2012.01.046 · Cited by 194 · cited 1× · see citing works
“Smart grid solutions may reinforce and accelerate practices producing energy demand peaks, rather than contributing to more sustainable ways of living (Strengers 2012).”
BackgroundStreet-Level Algorithms: A Theory at the Gaps Between Policy and Decisions
Alkhatib, A., & Bernstein, M. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300760 · Cited by 188 · cited 1× · see citing works
“One example is to introduce ways for AI systems to exercise “discretion”, to diverge from baked-in policy in cases where user dissatisfaction with system behavior is detected (Alkhatib and Bernstein 2019).”
BackgroundUser attitudes towards algorithmic opacity and transparency in online reviewing platforms
Eslami, M., Vaccaro, K., Lee, M. K., Elazari Bar On, A., Gilbert, E., & Karahalios, K. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300724 · Cited by 184 · cited 1× · see citing works
“A level of obfuscation is necessary to prevent bad actors from gaming the system, whereas a lack of transparency reduces people’s sense of control and makes them unsure about how their behavior might impact outcomes (Alvarado and Waern 2018; Jhaver et al. 2018; Eslami et al. 2019).”
BackgroundKim, B., Park, J., & Suh, J. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113302 · Cited by 182 · cited 1× · see citing works
“When models are developed with techniques producing opaque or “black box” models resisting human interpretation, explanations can still be produced in a post-hoc fashion by means of a supplemental explanation model (Zhang and Chen 2020; Kim et al. 2020).”
MethodCommunicating Algorithmic Process in Online Behavioral Advertising
Eslami, M., Krishna Kumaran, S. R., Sandvig, C., & Karahalios, K. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174006 · Cited by 171 · cited 2× · see citing works
“Increasing transparency by providing explanations can improve people’s trust in AI systems (Kizilcec 2016; Eslami et al. 2018; Binns et al. 2018).”
BackgroundAn Informational Right to the City? Code, Content, Control, and the Urbanization of Information
Shaw, J., & Graham, M. (2017) · Antipode · doi:10.1111/anti.12312 · Cited by 161 · cited 3× · see citing works
“…relying on exit alone is problematic because citizens should have a say in the operation of these systems, and limiting participation to “voting with your feet” infringes on people’s right to the city (Shaw and Graham 2017; Sadowski and Pasquale 2015; Cardullo and Kitchin 2017; Foth et al. 2015).”
BackgroundThe Impact of Placebic Explanations on Trust in Intelligent Systems
Eiband, M., Buschek, D., Kremer, A., & Hussmann, H. (2019) · Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290607.3312787 · Cited by 152 · cited 2× · see citing works
“There is some evidence explanations need not even be truthful to increase user trust (Eiband et al. 2019).”
BackgroundTokenism or Political Activism? Some Reflections on Participatory Planning
Monno, V., & Khakee, A. (2012) · International Planning Studies · doi:10.1080/13563475.2011.638181 · Cited by 149 · see citing works
How do people judge the credibility of algorithmic sources?
Shin, D. (2021) · AI & Society · doi:10.1007/s00146-021-01158-4 · Cited by 143 · cited 1× · see citing works
“User literacy of AI systems may mediate the degree to which explanations increase trust (Shin 2021).”
BackgroundNudging citizens through technology in smart cities
Ranchordás, S. (2019) · International Review of Law Computers & Technology · doi:10.1080/13600869.2019.1590928 · Cited by 140 · cited 1× · see citing works
“However, it is now widely recognized such AI systems may lead to unfair outcomes, even if they have been designed with the best intentions (Eubanks 2018; Ranchordás 2020).”
BackgroundTowards Algorithmic Experience
Alvarado, O., & Wærn, A. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173860 · Cited by 139 · cited 4× · see citing works
“However, researchers have started to point out theoretical and practical limitations of the transparency ideal (Ananny and Crawford 2018), and the importance of considering the human experience of AI transparency (Alvarado and Waern 2018; Vakarelov and Rogerson 2020).”
BackgroundInfluentialHolmquist, L. E. (2017) · interactions · doi:10.1145/3085571 · Cited by 133 · cited 1× · see citing works
“AI presents challenges for traditional HCI design in general (Holmquist 2017), and participatory design approaches in particular (Bratteteig and Verne 2018).”
BackgroundArtificial intelligence and the value of transparency
Walmsley, J. (2021) · AI & SOCIETY · doi:10.1007/s00146-020-01066-z · Cited by 131 · see citing works
Algorithmic Anxiety and Coping Strategies of Airbnb Hosts
Jhaver, S., Karpfen, Y., & Antin, J. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173995 · Cited by 121 · cited 3× · see citing works
“The tension between transparency and the possibility of “gaming” behavior, as well as concerns over exposure of intellectual property has been noted previously (Alvarado and Waern 2018; Jhaver et al. 2018).”
BackgroundParticipatory urban informatics: towards citizen-ability
Foth, M. (2018) · Smart and Sustainable Built Environment · doi:10.1108/sasbe-10-2017-0051 · Cited by 117 · cited 1× · see citing works
“This suggests efforts to make urban AI more transparent should be paired with more participatory and collaborative approaches to city governance and policy-making (Dourish 2010; Foth 2018; Frauenberger et al. 2018).”
BackgroundDesigning Contestability: Interaction Design, Machine Learning, and Mental Health
Hirsch, T., Merced, K., Narayanan, S., Imel, Z. E., & Atkins, D. C. (2017) · Proceedings of the 2017 Conference on Designing Interactive Systems · doi:10.1145/3064663.3064703 · Cited by 110 · cited 2× · see citing works
“Another is to include means for decision subjects to contest AI system decisions, to make them responsive to requests for human intervention (Walms-ley 2021; Vaccaro et al. 2019; Sarra 2020; Almada 2019; Hirsch et al. 2017).”
BackgroundAre explanations always important?: a study of deployed, low-cost intelligent interactive systems
Bunt, A., Lount, M., & Lauzon, C. (2012) · Proceedings of the 2012 ACM international conference on Intelligent User Interfaces · doi:10.1145/2166966.2166996 · Cited by 106 · cited 2× · see citing works
“Furthermore, when users feel they are able to form an adequate mental model from simply interacting with systems, explanations are less likely to be considered beneficial, because they take attention away from primary tasks (Bunt et al. 2012).”
BackgroundThe Ethics of Algorithms: Key Problems and SolutionsPreprint
Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3662302 · Cited by 101 · see citing works
Human intervention in automated decision-making: Toward the construction of contestable systems
Almada, M. (2019) · Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law · doi:10.1145/3322640.3326699 · Cited by 95 · cited 1× · see citing works
“Another is to include means for decision subjects to contest AI system decisions, to make them responsive to requests for human intervention (Walms-ley 2021; Vaccaro et al. 2019; Sarra 2020; Almada 2019; Hirsch et al. 2017).”
BackgroundAn Action-Oriented AI Policy Toolkit for Technology Audits by Community Advocates and Activists
Krafft, P. M., Young, M., Katell, M. A., Lee, J. E., Narayan, S., Epstein, M., Dailey, D., Herman, B., Tam, A., Guetler, V., et al. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445938 · Cited by 87 · cited 1× · see citing works
“However, a small but growing body of work seeks to bridge the gap between advocating for abstract principles and supporting design choices situated in context (Aizenberg and Hoven 2020) and opening up AI development processes to a broader range of stakeholders (Krafft et al. 2021).”
BackgroundBeing a ‘citizen’ in the smart city: Up and down the scaffold of smart citizen participationPreprint
Cardullo, P., & Kitchin, R. (2017) · doi:10.31235/osf.io/v24jn · Cited by 80 · see citing works
On scale, dialectics, and affect
Frauenberger, C., Foth, M., & Fitzpatrick, G. (2018) · Participatory Design Conference · doi:10.1145/3210586.3210591 · Cited by 74 · cited 1× · see citing works
“This suggests efforts to make urban AI more transparent should be paired with more participatory and collaborative approaches to city governance and policy-making (Dourish 2010; Foth 2018; Frauenberger et al. 2018).”
BackgroundBeyond engagement theatre: challenging institutional constraints of participatory planning practice
Kamols, N., Foth, M., & Guaralda, M. (2021) · Australian Planner · doi:10.1080/07293682.2021.1920993 · Cited by 67 · cited 1× · see citing works
“Pub-lishing (non-actionable) information in an effort to merely appear transparent can be a form of “tokenism” or “engage-ment theatre” in that it does not actually increase democratic control over urban AI systems (Kamols et al. 2021; Monno and Khakee 2012).”
BackgroundBratteteig, T., & Verne, G. (2018) · Participatory Design Conference · doi:10.1145/3210604.3210646 · Cited by 66 · cited 1× · see citing works
“AI presents challenges for traditional HCI design in general (Holmquist 2017), and participatory design approaches in particular (Bratteteig and Verne 2018).”
BackgroundReal-Time Smart Charging Based on Precomputed Schedules
Frendo, O., Gaertner, N., & Stuckenschmidt, H. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2914274 · Cited by 62 · cited 1× · see citing works
“Smart charging allows for dynamic management of demand by curtailing the rate and amount of electricity EVs can charge when connected to a charge point (Wang et al. 2016; Mehta et al. 2018; Moghaddam et al. 2018; Frendo et al. 2019).”
BackgroundAlgorithmic and human decision making: for a double standard of transparency
Günther, M., & Kasirzadeh, A. (2021) · AI & Society · doi:10.1007/s00146-021-01200-5 · Cited by 56 · see citing works
Contestability in Algorithmic Systems
Vaccaro, K., Karahalios, K., Mulligan, D. K., Kluttz, D., & Hirsch, T. (2019) · Companion Publication of the 2019 Conference on Computer Supported Cooperative Work and Social Computing · doi:10.1145/3311957.3359435 · Cited by 54 · cited 1× · see citing works
“Another is to include means for decision subjects to contest AI system decisions, to make them responsive to requests for human intervention (Walms-ley 2021; Vaccaro et al. 2019; Sarra 2020; Almada 2019; Hirsch et al. 2017).”
BackgroundDöbelt, S., Jung, M., Busch, M., & Tscheligi, M. (2015) · Energy Research & Social Science · doi:10.1016/j.erss.2015.08.022 · Cited by 52 · cited 1× · see citing works
“In this context, many stakeholders consider transparency an essential ingredient for ensuring public acceptance (Döbelt et al. 2015; Madhu et al. 2019; Fabianek et al. 2020).”
BackgroundExamining embedded apparatuses of AI in Facebook and TikTok
Grandinetti, J. J. (2021) · AI & Society · doi:10.1007/s00146-021-01270-5 · Cited by 49 · cited 1× · see citing works
“Indeed, large tech companies use trans-parency initiatives at least in part to stave off government regulation (Grandinetti 2021).”
BackgroundUnpacking Perceptions of Data-Driven Inferences Underlying Online Targeting and Personalization
Dolin, C., Weinshel, B., Shan, S., Hahn, C. M., Choi, E., Mazurek, M. L., & Ur, B. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174067 · Cited by 44 · cited 1× · see citing works
“This echoes others’ findings that people find AI useful, but crucially also that the AI systems people find most useful is not necessarily the one they find most fair (Dolin et al. 2018).”
BackgroundToward a Genuinely Humanizing Smart Urbanism
Kitchin, R. (2019) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-78769-139-120191014 · Cited by 43 · cited 1× · see citing works
“Some have argued transparency invokes a neoliberal model of agency (Ananny and Crawford 2018; Cardullo and Kitchin 2017; Kitchin 2019).”
BackgroundFabianek, P., Will, C., Wolff, S., et al. (2020) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2020.102504 · Cited by 40 · see citing works
The disciplinary power of predictive algorithms: a Foucauldian perspective
de Laat, P. B. (2019) · Ethics and Information Technology · doi:10.1007/s10676-019-09509-y · Cited by 38 · cited 2× · see citing works
“(De Laat 2019).”
BackgroundUrban informatics, governmentality and the logics of urban controlPreprint
Kitchin, R., Coletta, C., & McArdle, G. (2017) · doi:10.31235/osf.io/27hz8 · Cited by 37 · cited 2× · see citing works
“1 3 between users and those who design, develop and govern systems is different from commercial settings: These systems effectively enact policy (Shaw and Graham 2017; Kitchin et al. 2017), and users are not simply consumers, but also citizens who are entitled to democratic control over policy, AI-enacted or otherwise.”
BackgroundCentivany, A., & Glushko, R. J. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858516 · Cited by 35 · cited 1× · see citing works
“It could also be because people’s lack of “loyalty” to the organization deploying the system made them disinclined to go through the trouble of acting on their disagreements (Hirschman 1970; Centivany and Glushko 2016).”
BackgroundAI transparency: a matter of reconciling design with critique
Hollanek, T. (2023) · AI & SOCIETY · doi:10.1007/s00146-020-01110-y · Cited by 35 · cited 3× · see citing works
“In the language of Hirschman (1970), this is the “exit” option.”
BackgroundBlack and white transparency: contradictions of a moral metaphor
Menéndez-Viso, A. (2009) · Ethics and Information Technology · doi:10.1007/s10676-009-9194-x · Cited by 31 · cited 4× · see citing works
“However, what we see them do in our project is something quite different, and supports the notion that transparency requires active production of information (Menéndez-Viso 2009).”
BackgroundInfluentialSarra, C. (2020) · Global Jurist · doi:10.1515/gj-2020-0003 · Cited by 24 · cited 1× · see citing works
“Another is to include means for decision subjects to contest AI system decisions, to make them responsive to requests for human intervention (Walms-ley 2021; Vaccaro et al. 2019; Sarra 2020; Almada 2019; Hirsch et al. 2017).”
BackgroundMaking the black box society transparent
Innerárity, D. (2021) · AI & Society · doi:10.1007/s00146-020-01130-8 · Cited by 24 · cited 1× · see citing works
“Socio-technical complexity of urban AI systems may exceed individual capacity, in which case it can only be understood collectively (Innerarity 2021).”
BackgroundDescampe, A., Massart, C., Poelman, S., Standaert, F., & Standaert, O. (2021) · AI & Society · doi:10.1007/s00146-021-01159-3 · Cited by 24 · cited 1× · see citing works
“Transparency can also not account for cases in which a system’s behavior deviates from design intent due to adversarial attacks (Descampe et al. 2021).”
BackgroundMadhu, G. M., Vyjayanthi, C., & Modi, C. (2019) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt45670.2019.8944636 · Cited by 17 · cited 1× · see citing works
“In this context, many stakeholders consider transparency an essential ingredient for ensuring public acceptance (Döbelt et al. 2015; Madhu et al. 2019; Fabianek et al. 2020).”
BackgroundThe Transparency Game: Government Information, Access, and Actionability
Vakarelov, O., & Rogerson, K. (2020) · Philosophy & Technology · doi:10.1007/s13347-019-0340-z · Cited by 7 · cited 3× · see citing works
“For an explanation to be actionable, it must be usable as “currency” in a person’s decision-making process (Vakarelov and Rogerson 2020).”
BackgroundTargets in the Cloud: On Transparency and Other Shadows
Caduff, C. (2017) · Science, Technology, & Human Values · doi:10.1177/0162243916687644 · Cited by 3 · cited 2× · see citing works
“In our current age, it is not a lack of information but a shear abundance of data obscuring our understanding (Caduff 2017).”
Background
Flagged first, then by citations
1 of the 78 references couldn’t be matched to a known paper.
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Mehrotra, S., Degachi, C., Vereschak, O., Jonker, C. M., & Tielman, M. L. (2024) · ACM Journal on Responsible Computing · doi:10.1145/3696449 · Cited by 184 · see citing works
A Survey on Trustworthy Recommender Systems
Ge, Y., Liu, S., Fu, Z., Tan, J., Li, Z., Xu, S., Li, Y., Xian, Y., & Zhang, Y. (2024) · ACM Transactions on Recommender Systems · doi:10.1145/3652891 · Cited by 144 · see citing works
Yurrita, M., Draws, T., Balayn, A., Murray-Rust, D., Tintarev, N., & Bozzon, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581161 · Cited by 73 · see citing works
“Further research is necessary to translate those conceptualizations into actual design guidelines [2, 60] and validate designs of contestable algorithmic systems.”
BackgroundPublics’ views on ethical challenges of artificial intelligence: a scoping review
Machado, H., Silva, S., & Neiva, L. (2023) · AI and Ethics · doi:10.1007/s43681-023-00387-1 · Cited by 60 · see citing works
“(…) We can also see that not only decisions, but motivations for them must be made transparent [98] Accountability (31 articles) g : illustrative quotes Some participants indicated media as a key mechanism for accountability.”
InfluentialAlfrink, K., Keller, I., Doorn, N., & Kortuem, G. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580984 · Cited by 48 · see citing works
“An overlapping but distinct area of research focuses on the role of AI in the built environment, so-called urban AI [1, 42, 56, 57, 67, 73, 79].”
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“Clear and transparent information provides factual data to address concerns and build trust [103,104].”
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Multilevel Collaborative Governance to Foster Responsible AI Deployment in Cities
Jieutsa, L., & Koseki, S. A. (2025) · doi:10.1093/9780198945215.003.0034 · Cited by 2 · see citing works
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- Authors
- Alfrink, K., Keller, I., Doorn, N., & Kortuem, G.
- Affiliations
- Delft University of Technology (NL)
- Corresponding author
- Alfrink, Kars
- Keller, Ianus
- Doorn, Neelke
- Kortuem, Gerd
- Venue
- AI & SOCIETY
- Published
- 2022-03-31
- Volume
- 38
- Issue
- 3
- Pages
- 1049-1065
- ISSN
- 0951-5666 (ISSN-L)
- 1435-5655
- OpenAlex
- W4220787911
- Semantic Scholar
- 6722fee8ba64985bd25d82dc183e5d5f3c639304
- Cited by
- 18
- References
- 78
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 5 · 2023: 3
- FWCI
- 4.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- AI & Society (publishedVersion, cc-by)
- Research Repository (Delft University of Technology) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Canadian Institute of Steel Construction
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (CISC.CC.018)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Keywords
- AI transparency, electric vehicle charging infrastructure
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-08.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-08.
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