The DOI matches its Crossref record; 61 of the 65 references it cites were found in a registry or an index; none of the 39 sources its claims cite is checked for support yet.
Artificial intelligence, transparency, and public decision-making: Why explanations are key when trying to produce perceived legitimacy
Open accessAbstract
The increasing use of Artificial Intelligence (AI) for making decisions in public affairs has sparked a lively debate on the benefits and potential harms of self-learning technologies, ranging from the hopes of fully informed and objectively taken decisions to fear for the destruction of mankind. To prevent the negative outcomes and to achieve accountable systems, many have argued that we need to open up the “black box” of AI decision-making and make it more transparent. Whereas this debate has primarily focused on how transparency can secure high-quality, fair, and reliable decisions, far less attention has been devoted to the role of transparency when it comes to how the general public come to perceive AI decision-making as legitimate and worthy of acceptance. Since relying on coercion is not only normatively problematic but also costly and highly inefficient, perceived legitimacy is fundamental to the democratic system. This paper discusses how transparency in and about AI decision-making can affect the public’s perception of the legitimacy of decisions and decision-makers and produce a framework for analyzing these questions. We argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient ground for perceived legitimacy without producing the harms full transparency would bring.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper argues that for AI in public decision-making, full transparency about decision-making processes is not necessarily legitimacy-enhancing and may even backfire. It proposes that a limited transparency focused on justifications—transparency in rationale—can better support public perceptions of legitimacy.
Key findings
- The paper distinguishes transparency about final decisions, transparency in process, and transparency in rationale; it argues decision-makers should generally opt for transparency in rationale, meaning justifications for decisions plus information on who is accountable.
- It identifies four arguments for full transparency—public scrutiny, public understanding, feelings of control, and fairness perceptions—and argues each can fail or harm perceived legitimacy due to problems such as information overload, the “transparency paradox,” the “myth of hidden politics,” and disappointment with real-world “muddling through.”
- A justifications approach is argued to support accountability, higher-quality decisions through the giving-reasons requirement, public understanding, and perceptions of fair treatment; AI assistants should explain decisions in accessible language and make clear who is accountable or where to appeal.
- The paper offers a framework dividing AI public decision-making into three phases: goal-setting, coding, and implementation; transparency in rationale can be applied in each phase.
Methods
- Conceptual/theoretical paper; no empirical data, sample, or statistical analysis are reported.
- It synthesizes social science literature on transparency, legitimacy, principal-agent theory, deliberative democracy, and explainable AI to build an argument and framework.
- The main analysis is normative and argumentative, proposing a framework for transparency and perceived legitimacy in AI decision-making in the socio-technical system.
Limitations
- The authors state that their analysis “builds on a considerable number of assumptions and guesses” and call for theoretical and empirical work on justifications, normatively acceptable reasons, how justifications should be designed and presented, empirically grounded policy design, and how decisions are perceived when made by humans versus AIs.
- The text makes evident that the argument is not empirically tested and relies on literature-based reasoning and examples rather than new data.
- The paper’s main focus is public policymaking and authoritative public decisions, though the authors say the general argument should also apply to AI use in more private operations.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
58 Own interpretation · 25 Not checked yet · 3 No basis given
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, since AI assistants work quickly, thereby producing potentially positive and negative outcomes in a short timeframe, quick and efficient decision-making may be vital when implementing AI assistants (Phases 1 and 2) and adjusting them (Phase 3).”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This suggests that making Phases 1 and 2 available is not necessarily beneficial.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
hedged- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“assuming that the public needs a chance to actually understand the code, the code would likely have to be much simpler than it is in today's systems.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
- The authors state this as an assumption: The public needs a chance to actually understand the code for transparency to have positive effects.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient ground for perceived legitimacy without producing the harms full transparency would bring.”
- Also stated in: § 1 Introduction · hedged
“Based on our reading of the literature, we argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient grounds for perceived legitimacy in AI decision-making.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > Abstract
plainconditional- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Relying on coercion is normatively problematic, costly, and highly inefficient, and perceived legitimacy is fundamental to the democratic system.
- Also stated in: § 1 Introduction · hedged
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Intuitively, however, not all forms of transparency lead to greater perceived legitimacy.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 2 Full transparency in AI and public decision-making
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When the process is made transparent, however, decision-makers may be more concerned with signaling loyalty to their constituents or adhering to the special interests they represent than with finding a solution to an emerging problem.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Based: unclear — It is unclear why the paper says it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we discuss why transparency in AI decision-making can harm legitimacy in the eyes of the public.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 1 Introduction
plainconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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- Rests on an assumption: Relying on coercion is normatively problematic, costly, and highly inefficient, and perceived legitimacy is fundamental to the democratic system.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“there may also be good reason in favor of also demanding transparency in decision-making processes in the workings of AI when it comes to other agents, such as the decision-makers themselves.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
hedgedconditional- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, this does not imply that there are good reason in favor of sharing this information with the general public if perceived legitimacy is the goal.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In many cases, a policy of justifications may even fulfill this role better than more demanding forms of transparency, e.g., when the whole process is made visible.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 29
- It may state two separate claims as one. 32
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, if explicability means that we actually make the decisionmaking processes fully transparent, then we do not believe it suitable in relation to the production of perceived legitimacy; but, if decision-makers should provide an explanation in the form of a narrative where it is explained how the decision has been made, then this might be applicable for perceived legitimacy.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 2 Full transparency in AI and public decision-making
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, in this sense, though, demands on transparency would probably produce a lower degree of quality in the decisions and thereby less perceived legitimacy.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
hedgedconditional- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“This would arguably make it easier to game the system, and it would provide the industry with fewer incentives to innovate (e.g., Zarsky 2016; Lepri et al. 2017;De Laat 2018; Carabantes 2019).”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
What the sources say
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Thus, the”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 2 Full transparency in AI and public decision-making
What the sources say
- Not checked yetcites Warren and Mansbridge 2013
plaincorrelational- Asserted: fails — The authors may not put it forward as true.
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Probably not a substantial claim. The authors may not put it forward as true. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Decision-makers and professionals might also become more interested in avoiding blame than in finding optimal solutions (Hood 2007), meaning that the open discussion may become shallow (Chambers 2004(Chambers , 2005) ) and fail to produce the best possible outcomes.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
What the sources say
- Not checked yetcites Hood 2007
- Not checked yetcites Chambers 2004
- Not checked yetcites Chambers 2005
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“One reason for this is that transparency about a process imposes incentives on the decision-makers to look credible in the eyes of the observers during their deliberations, but this is not necessarily positive in relation to the prospect of reaching a good decision.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Bok (1989, p. 175) argues that all organizations need ⋯some shelter in order to be able to arrive at choices and to carry them out.The processes of reasoning, planning, accommodation, and choice are hampered if fully exposed from the outset, no matter how great the corresponding dangers of secrecy.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
What the sources say
- Not checked yetcites Bok (1989, p. 175)
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Asking”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“In front of an audience, it may be difficult to change an opinion (even with a good argument), present dissenting opinions, or ask necessary but seemingly”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
What the sources say
- Not checked yetcites Elster 1998
- Not checked yetcites Meade and Stasavage 2006
- Not checked yetcites Mansbridge 2009 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finding the relevant information would be as difficult as finding the proverbial needle in a haystack-something that decision-makers and the public will understand and adapt to.Hence, we will not witness the positive outcomes of making the information available.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“That decision-makers are given some shelter for discussion is especially important in Phases 1 and 2, since these phases are likely to contain both technically complex and morally controversial tradeoffs.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
hedgedconditional- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Transparency is generally associated with the”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
What the sources say
- Not checked yetcites Fenster 2006, p. 931
- Not checked yetcites West and Sanders 2003
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“in Phases 1 and 2, the public eye monitors the reasons on which the decisions are based.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A cooperative working climate that can spark efficiency and problem-solving when it comes to AI in decision-making is crucial, as algorithms can potentially cause considerable damage if they are not properly adjusted when necessary.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
plainconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Thus, decision-makers are likely to properly weigh the pros and cons when making decisions (Shapiro 1992), thus leading to higher-quality decisions.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
What the sources say
- Not checked yetcites Shapiro 1992
hedgeddirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As well as this, no general framework exists to analyze the perceived legitimacy of AI in the broader context of the socio-technological system to elucidate the issues of today and tomorrow regarding transparency and perceived legitimacy.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 1 Introduction
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, though a lot of interesting work has been done in the area of transparency, far less attention has been devoted to the role of transparency in terms of how those who are ultimately affected (i.e., the general public) come to perceive AI decision-making as being legitimate and worthy of acceptance.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 1 Introduction
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, it may be difficult to establish goals and guarantee that their respective importance is sufficiently precise for programmers' needs.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 2 Full transparency in AI and public decision-making
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Even though this is persuasive, it may be just a story.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
hedged- Asserted: passes — The authors put it forward as true.
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In the paper
“These qualities can increase both quality and efficiency in decision-making (Mansbridge 2009).”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
What the sources say
- Not checked yetcites Mansbridge 2009 source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“a policy of justifications of decisions will inform the public of what the decision is, on which grounds it has been made, and in doing so, identify who the responsible actor is.”
- Also stated in: § 4 The benefits of a justifications approach to AI transparency · plain
“A policy of justification will tell the public what the decision is, on which grounds it has been made, and who the responsible actor is.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
plaindirect- Asserted: passes — The authors put it forward as true.
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- Also stated in: § 4 The benefits of a justifications approach to AI transparency · plain
In the paper
“The widespread myth of secret power is sparked by the fact that modern popular culture generally tells us that we should be suspicious of anyone in a powerful position (Brin 1998).”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
What the sources say
- Not checked yetcites Brin 1998
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As such, due to the staggering amount of information available, it may be difficult to understand who to hold accountable for what.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
hedgedconditional- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, the decision-makers will have incentives to behave better when it comes to producing such reasons, thus producing higher-quality decisions.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Of course, the notion that AI assistants should be able to provide justifications or explanations for their decisions is not novel.In fact, it fits nicely with the core components of the rapidly evolving research field of explainable AI (XAI) (e.g., Gunning 2017Gunning , 2019;;Thelisson et al. 2017).”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 2 Full transparency in AI and public decision-making
What the sources say
- Not checked yetcites Gunning 2017
- Not checked yetcites Gunning 2019
- Not checked yetcites Thelisson et al. 2017
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Similarly, the source code and data that the AI has been trained and tested on are often too complex for most individuals to fully understand, meaning that full transparency might actually make the public less prone to believing that they have more control over the AI than they did before they had access to the data.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The general public will not understand the source code.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For a general public that is not particularly interested in political matters, this means that justifications may actually be a more attractive policy than full transparency.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: The general public is not particularly interested in political matters.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly, they are likely to get sufficient information to understand the different considerations and perspectives underlying the decision, thus making them feel included.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the same time, when the actual decision-making process remains comparatively secret, decision-makers can ask important but seemingly”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, when code and data are made transparent, the public might think this release is hiding the”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The general public will not understand the source code.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, there is a constant intermingling between Phases 1-3, with all phases deeply connected to each other.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 2 Full transparency in AI and public decision-making
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“according to principal-agent terminology, the agent (the people) will know who to hold accountable and for what, and this appears true for all phases.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is because they might use this information to make more well-informed decisions, thereby leading to higher-quality decisions that result in higher perceived legitimacy among the public.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In particular, Binns et al. (2018) have examined how different kinds of explanations affect the fairness judgments of the general public.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 2 Full transparency in AI and public decision-making
What the sources say
- Not checked yetcites Binns et al. (2018) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, the public will receive enough information to evaluate the desirability of the decision and, if they so like, demand accountability.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
- The authors state this as an assumption: Sufficient mechanisms for accountability are in place.
In the paper
“He considers the example of European Union lobbyists, who can be expected to have incentives to behave in a civilized manner when it comes to political matters.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
What the sources say
- Not checked yetcites Naurin (2007)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“This holds that if we received all of the information from Phases 1 and 2, we would have a lot of information to assimilate, and this problem becomes even worse in Phase 3.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
What the sources say
- Not checked yetcites Eppler and Mengis 2004
- Not checked yetcites Richards and King 2013
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Giving these groups access may yield higherquality decisions and a higher degree of perceived legitimacy.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
In the paper
“The problem is that full transparency reveals the actual reality of decision-making and that real-world decision-making rarely or never lives up to the democratic or professional ideal (e.g., Tsoukas 1997).”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
What the sources say
- Not checked yetcites Tsoukas 1997
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“However, even though it is intuitive that transparent institutions are preferred over non-transparent ones and that they yield higher perceived legitimacy, recent empirical research (e.g., Grimmelikhuijsen 2012; de Fine Licht 2014) has shown that it is far from evident that increased transparency generates trust or acceptance of public policies.In some cases, the effect can even be negative.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
What the sources say
- Not checked yetcites Grimmelikhuijsen 2012
- Not checked yetcites de Fine Licht 2014
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In most cases, this information is sufficient for members of the public to form an opinion about the desirability of the decision and, if they so like, demand that the responsible actor be accountable”
- Also stated in: § 4 The benefits of a justifications approach to AI transparency · plain
“In most cases, this information is enough for members of the public to form an opinion about the desirability of the decision and, if they so like, demand accountability of the responsible actor”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Sufficient mechanisms for accountability are in place.
- Also stated in: § 4 The benefits of a justifications approach to AI transparency · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is again because the public will get the information they need in a manageable way, meaning that they will not suffer from the information overload discussed in Sect.3.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although our main focus is the use of AI technology in public policymaking, authoritative public decision-making in relation to individuals as citizens, and the intermingling of these actors (i.e., the sociotechnical system), the general argument should also apply to cases of AI use in more private operations.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 5 Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In Phases 1 and 2, when decision-makers are transparent about the decision and the reasons for taking it, the public should have greater understanding of why these decisions were made, since the information about the decision will be more condensed, thus making it more accessible.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“As argued by McGraw et al. (1995), several conditions must be met for an explanation/justification to have the intended effect: It must be exposed to the intended audience, the audience must pay attention to it, the audience must comprehend it, and the audience must accept the explanation/justification as legitimate.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 5 Conclusions
What the sources say
- Not checked yetcites McGraw et al. (1995) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These appear to be necessary conditions that make the receiver feel included and respected.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“To provide reasons for decisions signals that the decision-makers respect and care about the affected (e.g., Tyler and Lind 1992) and may lead to more favorable interpretations regarding the motives and intentions of the decision-makers (e.g., Bies and Shapiro 1988;Shapiro et al. 1994;Colquitt 2001;Shaw et al. 2003).”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
What the sources say
- Not checked yetcites Tyler and Lind 1992 source
- Not checked yetcites Bies and Shapiro 1988
- Not checked yetcites Shapiro et al. 1994
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, we argue that there may be a reasonable level of public transparency in relation to the design of AI and the decisions made by AI.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 5 Conclusions
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“They must”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
What the sources say
- Not checked yetcites Naurin (2007)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the same time, when keeping the process more secret, we can avoid many of the potentially negative effects of full transparency previously discussed such as disappointment regarding the decision-makers' behavior in decisionmaking situations.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This will then lead to fairer decisions of higher quality, eventually leading to a higher degree of perceived legitimacy.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, justifications need to be understandable and relevant to the decision at hand.”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper relies on this without support.
Relying on coercion is normatively problematic, costly, and highly inefficient, and perceived legitimacy is fundamental to the democratic system.
“Since relying on coercion is not only normatively problematic but also costly and highly inefficient, perceived legitimacy is fundamental to the democratic system.”
The authors state this as an assumption.
The public needs a chance to actually understand the code for transparency to have positive effects.
“assuming that the public needs a chance to actually understand the code”
The paper relies on this without support.
The general public will not understand the source code.
“even though the general public will not understand it”
The paper relies on this without support.
Perceived legitimacy is the goal of transparency in AI decision-making.
“if perceived legitimacy is the goal”
“When realizing perceived legitimacy, we should, as a default, opt for having our AI assistants explain themselves rather than open up their code, etc. for public scrutiny.”
The authors state this as an assumption.
Sufficient mechanisms for accountability are in place.
“given, of course, that sufficient mechanisms for accountability are in place”
“given of course that sufficient mechanisms for accountability are in place”
The paper relies on this without support.
The general public is not particularly interested in political matters.
“For a general public that is not particularly interested in political matters”
The paper relies on this without support.
Decision-makers are servants of the public.
“their position as servants of the public”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In a conceptual piece on the legitimacy of algorithmic public decisions, they argue that providing a decision justification is needed to sustain government legitimacy.”
From Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making (2023) · cites this paper as De Fine Licht and De Fine Licht (2020)
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Citizens want to receive clear reasons for a (negative) decision to assess the fairness of a decision (De Fine Licht and De Fine Licht 2020; Tyler 2006) or to be able to contest an algorithmic decision (Mittelstadt et al. 2019).”
From Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making (2023) · cites this paper as De Fine Licht and De Fine Licht 2020
The cited source says what the claim attributes to it.
In the citing paper
“This distinction accords with research showing that reasons-focused transparency can be more intelligible and legitimacy-enhancing than indiscriminate disclosure of code or process detail (de Fine Licht & de Fine Licht, 2020).”
From Responsible AI in Polish Higher-Education Administration under the EU AI Act: High-Risk Use Cases, Fundamental-Rights Impact Assessment, and Institutional Accountability (2026) · cites this paper as de Fine Licht & de Fine Licht, 2020
In the citing paper
“Public dissatisfaction with responses to the black box problem may undermine the adoption of AI/ML technologies, especially when these technologies cause harm, invade privacy, or produce biased claims and results [60, 85, 134, 175].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [134]
In the citing paper
“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).”
From Tensions in transparent urban AI: designing a smart electric vehicle charge point (2023) · cites this paper as Fine Licht and Fine Licht 2020
In the citing paper
“The need to provide appropriate justifications of judicial decisions has also been analyzed by several authors [26, 29, 73] 24 .”
From Beyond explainability: justifiability and contestability of algorithmic decision systems (2022) · cites this paper as [29]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 4 The benefits of a justifications approach to AI transparency
“according to principal-agent terminology, the agent (the people) will know who to hold accountable and for what”
§ Artificial intelligence, transparency, and public decision-making Why explanations are key when trying to produce perceived legitimacy > 3 Transparency: why more is not necessarily better
“transparency allows the principal (i.e., the public) to overcome the information asymmetry regarding the agent's (i.e., the representative's) workings”
Citations
Cites
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The Science of “Muddling Through”
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Moral Hazard and Observability
Holmstrom, B. R. (1979) · The Bell Journal of Economics · doi:10.2307/3003320 · Cited by 8,159 · cited 1×
“In principal–agent terminology (Holmström 1979; Fearon 1998; Manin et al.”
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Colquitt, J. (2001) · Journal of Applied Psychology · doi:10.1037/0021-9010.86.3.386 · Cited by 6,043 · cited 1×
“1994; Colquitt 2001; Shaw et al.”
Fenichel Pitkin, H. (1967) · doi:10.1525/9780520340503 · Cited by 5,162 · cited 1×
“Thereby, actors receiving justifications might be motivated to act as good losers, to accept that they have lost, and move on (e.g., Pitkin 1967; Gutmann and Thompson 1996).”
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774
The Black Box Society: The Secret Algorithms That Control Money and Information
Pasquale, F. (2015) · doi:10.4159/harvard.9780674736061 · Cited by 3,796
Governing in Europe: Effective and Democratic?
Scharpf, F. W. (1999) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198295457.001.0001 · Cited by 3,294
The Concept of Representation, by Hanna F. Pitkin
Mansfield, H. C. (1969) · Political Science Quarterly · doi:10.2307/2147153 · Cited by 3,092
Research works printed but not linked: 5
- Fearon J (1998) Deliberation and discussion. In: Elster J (ed) Deliberative democracy. Cambridge University Press, Cambridge
- Fenster M (2006) The opacity of transparency. Iowa Law Rev 91(3):885–949
- Lepri B, Oliver N, Letouzé E, Pentland A, Vinck P (2017) Fair, transparent, and accountable algorithmic decision-making processes. Philos Technol 2017:1–17
- Thelisson E, Padh K, Celis LE (2017) Regulatory mechanisms and algorithms towards trust in AI/ML. In: Proceedings of the IJCAI 2017 workshop on explainable artificial intelligence (XAI), Melbourne, Australia
- Warren ME, Mansbridge J (2013) Deliberative Negotiation. In: Mansbridge J, Martin CJ (eds) Negotiating agreement in politics: report of the task force on negotiating in politics. American Political Science Association, Washington
Cited by
Sorted by citations
Notions of explainability and evaluation approaches for explainable artificial intelligence
Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598
Durán, J. M., & Jongsma, K. R. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-106820 · Cited by 446
AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development
Radanliev, P. (2025) · Applied Artificial Intelligence · doi:10.1080/08839514.2025.2463722 · Cited by 422
“This paper is structured to systematically dissect these topics, providing an in-depth exploration of AI ethics and its implications for future AI development and governance (de Fine Licht and de Fine Licht 2020).”
Grimmelikhuijsen, S. (2023) · Public Administration Review · doi:10.1111/puar.13483 · Cited by 259
Resnik, D. B., & Hosseini, M. (2025) · AI and Ethics · doi:10.1007/s43681-024-00493-8 · Cited by 228
“A recent study found that while ChatGPT can pass medical boards, it can give dangerous medical advice due to its tendency to make factual errors and its lack of understanding of the meaning and context of language [51].”
Grimmelikhuijsen, S., & Meijer, A. (2022) · Perspectives on Public Management and Governance · doi:10.1093/ppmgov/gvac008 · Cited by 189
ED-AI Lit: An Interdisciplinary Framework for AI Literacy in Education
Allen, L. K., & Kendeou, P. (2023) · Policy Insights from the Behavioral and Brain Sciences · doi:10.1177/23727322231220339 · Cited by 217
“As generative AI continues to develop, the broader debate around transparency, accountability, and ethical applications of AI in education becomes even more salient, pressing the urgency for comprehensive AI literacy (de Fine Licht & de Fine Licht, 2020).”
BackgroundArtificial Intelligence in Cyber Physical Systems
Radanliev, P., Roure, D. D., Nurse, J. R. C., Nicolescu, R., Huth, M., Cannady, S., & Montalvo, R. M. (2020) · AI & Society · doi:10.1007/s00146-020-01049-0 · Cited by 233
Bokhari, S. A. A., & Myeong, S. (2022) · Sustainability · doi:10.3390/su14020620 · Cited by 170
“For instance, [25] argued that transparency should be seen both in designing AI assistants and the decision-making process, ensuring more legitimacy in the public eye rather than in the process.”
BackgroundYu, L., & Li, Y. (2022) · Behavioral Sciences · doi:10.3390/bs12050127 · Cited by 203
“Referencing earlier studies on transparency and AI decision-making transparency, this paper defines AI decision-making non-transparency as informing employees only of the AI decision-making results, whereas AI decision-making transparency is defined as informing employees of the AI decision-making result, rationale, a…”
BackgroundInfluentialEthical considerations on artificial intelligence in dentistry: A framework and checklist
Rokhshad, R., Ducret, M., Chaurasia, A., Karteva, T., Radenković, M., Roganović, J., Hamdan, M. H., Mohammad‐Rahimi, H., Krois, J., Lahoud, P., et al. (2023) · Journal of Dentistry · doi:10.1016/j.jdent.2023.104593 · Cited by 149
In AI we trust? Citizen perceptions of AI in government decision making
Ingrams, A., Kaufmann, W., & Jacobs, D. (2022) · Policy & Internet · doi:10.1002/poi3.276 · Cited by 133
Martin, K. E., & Waldman, A. E. (2022) · Journal of Business Ethics · doi:10.1007/s10551-021-05032-7 · Cited by 169
“For example, de Fine Licht and de Fine Licht (2020) theorize that providing justifications for algorithmic decisions could provide grounds for perceived legitimacy, whereas, Leicht-Deobald et al. (2019) suggest that algorithm-based decisions may be perceived as more legitimate because it is…”
BackgroundGuenduez, A. A., & Mettler, T. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101719 · Cited by 159
Starke, C., & Lünich, M. (2020) · Data & Policy · doi:10.1017/dap.2020.19 · Cited by 107
“The extent of transparency of self-learning systems, however, is a major driver of public perceptions of legitimacy (De Fine Licht and De Fine Licht, 2020).”
BackgroundPrivacy, ethics, transparency, and accountability in AI systems for wearable devices
Radanliev, P. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1431246 · Cited by 132
Varieties of transparency: exploring agency within AI systems
Andrada, G., Clowes, R. W., & Smart, P. R. (2022) · AI & Society · doi:10.1007/s00146-021-01326-6 · Cited by 112
Too Late to be Creative? AI-Empowered Tools in Creative Processes
Hwang, A. H.-C. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3503549 · Cited by 103
AI and society: a virtue ethics approach
Farina, M., Zhdanov, P., Karimov, A. R., & Lavazza, A. (2022) · AI & Society · doi:10.1007/s00146-022-01545-5 · Cited by 112
“Nevertheless, some form of control in terms of responsibility for the consequences of the decisions taken by an AI system seems to be necessary (de Fine Licht and de Fine Licht 2020; Felzmann et al. 2020).”
Park, K., & Yoon, H. Y. (2024) · Public Relations Review · doi:10.1016/j.pubrev.2024.102507 · Cited by 137
Cabitza, F., Campagner, A., Malgieri, G., Natali, C., Schneeberger, D., Stoeger, K., & Holzinger, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118888 · Cited by 89
Hassan, N., Abdelraouf, M., & El-Shihy, D. (2025) · Future Business Journal · doi:10.1186/s43093-025-00476-z · Cited by 96
“…information on how the particular AI-based recommendation method works, for instance, the outreach programs that show logical rationalization of each recommendation made by systems like Spotify—reduces the perceived risk and tension and increases user participation and confidence [36, 38].”
Artificial intelligence ethics has a black box problem
Bélisle-Pipon, J.-C., Monteferrante, E., Roy, M.-C., & Couture, V. (2023) · AI & SOCIETY · doi:10.1007/s00146-021-01380-0 · Cited by 85
“And this can be very problematic because it risks alienating the popu‑ lation towards AI’s benefits due to a lack of social accept‑ ability and mistrust of AI actors (Rees et al. 2020; Fine and Fine 2020).”
BackgroundBerman, A., Licht, K. D. F., & Carlsson, V. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102471 · Cited by 137
Self-citationWho needs explanation and when? Juggling explainable AI and user epistemic uncertainty
Jiang, J., Kahai, S. S., & Yang, M. H. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102839 · Cited by 92
Investigating the impact of artificial intelligence on consumer’s purchase intention in e-retailing
Bhagat, R., Chauhan, V. S., & Bhagat, P. (2022) · foresight · doi:10.1108/fs-10-2021-0218 · Cited by 101
Fairness Perceptions of Artificial Intelligence: A Review and Path Forward
Narayanan, D., Nagpal, M., McGuire, J., Schweitzer, S., & Cremer, D. D. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2210890 · Cited by 99
Australian public understandings of artificial intelligence
Selwyn, N., & Cordoba, B. G. (2021) · AI & Society · doi:10.1007/s00146-021-01268-z · Cited by 71
“For this reason alone, it has been argued that it is important that the general public (as “those who are ultimately affected” by most forms of AI) come to perceive the ways that AI technologies are used in society as acceptable and legitimate (Licht and Licht 2020).”
BackgroundWaldman, A. E., & Martin, K. E. (2022) · Big Data & Society · doi:10.1177/20539517221100449 · Cited by 84
How do citizens perceive the use of Artificial Intelligence in public sector decisions?
Haesevoets, T., Verschuere, B., Van Severen, R., & Roets, A. (2024) · Government Information Quarterly · doi:10.1016/j.giq.2023.101906 · Cited by 74
Beyond explainability: justifiability and contestability of algorithmic decision systems
Henin, C., & Le Métayer, D. (2022) · AI & SOCIETY · doi:10.1007/s00146-021-01251-8 · Cited by 71
Ahn, S., Park, J., & Ye, S. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.104207 · Cited by 85
Lünich, M. (2022) · Springer eBooks · doi:10.1007/978-3-658-36368-0 · Cited by 68
Tal, A. S., Kuflik, T., & Kliger, D. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2095705 · Cited by 73
“Transparency and users ’ transparency perceptions can be related to higher satisfaction (Shin & Park, 2019), higher perceived system legitimacy (de Fine Licht & de Fine Licht, 2020, Shin, 2021), more position perceptions regarding the system ’ s accuracy (Springer & Whittaker, 2018), higher levels…”
BackgroundToo much light blinds: The transparency-resistance paradox in algorithmic management
Hu, P., Zeng, Y., Wang, D., & Teng, H. Q. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108403 · Cited by 76
Explainable AI lacks regulative reasons: why AI and human decision-making are not equally opaque
Peters, U. (2022) · AI and Ethics · doi:10.1007/s43681-022-00217-w · Cited by 78
Algorithmic 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 62
“Still others, such as Feller et al. (2016) and Corbett-Davies et al.”
Jabagi, N., Croteau, A., Audebrand, L. K., & Marsan, J. (2024) · The International Journal of Human Resource Management · doi:10.1080/09585192.2024.2441448 · Cited by 80
“This assumption is supported by de Fine Licht and de Fine Licht (2020) who found transparency to be an important lynchpin in people’s perceptions of the legitimacy of algorithmic decision-makers.”
Employees’ Appraisals and Trust of Artificial Intelligences’ Transparency and Opacity
Yu, L., Li, Y., & Fan, F. (2023) · Behavioral Sciences · doi:10.3390/bs13040344 · Cited by 69
Varanasi, R. A., & Goyal, N. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580903 · Cited by 52
“A similar movement within the RAI’s purview focused on FATE values (Fairness, Accountability, Transparency, and Ethics/Explainability) [18, 66, 75, 93].”
BackgroundMaiti, M., Kayal, P., & Vujko, A. (2025) · Future Business Journal · doi:10.1186/s43093-025-00462-5 · Cited by 107
“…processes to build trust and accountability, emphasizing the importance of understanding how AI systems arrive at specific conclusions [16–18], (iii) privacy: exploration of the privacy implications of AI technologies involves questions about consent, data ownership, and security,…”
A Critical Survey on Fairness Benefits of Explainable AI
Deck, L., Schoeffer, J., De‐Arteaga, M., & Kühl, N. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658990 · Cited by 107
Artificial Intelligence and Decision-Making: the question of Accountability
Gualdi, F., & Cordella, A. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.281 · Cited by 52
“The lack of accountability of AI decision-making systems have raised concerns on the use of AI to support public sector service delivery [21].”
BackgroundIs Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant
He, G., Aishwarya, N., & Gadiraju, U. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712133 · Cited by 87
Koenig, P. D. (2023) · AI & Society · doi:10.1007/s00146-024-01987-z · Cited by 56
“Several contributions also explicitly mention this model as a suitable framework for understanding AI acceptance (De Fine Licht and De Fine Licht 2020; Krafft et al. 2020; Wieringa 2020).”
Revisiting the bullwhip effect: how can AI smoothen the bullwhip phenomenon?
Weisz, E., Herold, D. M., & Kummer, S. (2022) · The International Journal of Logistics Management · doi:10.1108/ijlm-02-2022-0078 · Cited by 54
Alfrink, K., Keller, I., Doorn, N., & Kortuem, G. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580984 · Cited by 56
“Some of the issues explored include: how transparency, explanations and justifications may affect citizens’ trust, acceptance and perceived legitimacy of public AI [16, 21, 24]; the politics of measurement, the human subjective choices that go into data collection, what does and does not get counted, and in what way […”
BackgroundExamining embedded apparatuses of AI in Facebook and TikTok
Grandinetti, J. J. (2021) · AI & Society · doi:10.1007/s00146-021-01270-5 · Cited by 64
Liu, Y., & Sun, X. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108006 · Cited by 46
From politics to ethics: Transformations in EU policies on digital technology
Carlsson, V., & Rönnblom, M. (2022) · Technology in Society · doi:10.1016/j.techsoc.2022.102145 · Cited by 45
Aldemir, C., & Uysal, T. U. (2025) · Administrative Sciences · doi:10.3390/admsci15020058 · Cited by 77
A Systematic Review of Fairness in Artificial Intelligence Algorithms
Xivuri, K., & Twinomurinzi, H. (2021) · IFIP International Conference on e-Business, e-Services, and e-Society · doi:10.1007/978-3-030-85447-8_24 · Cited by 26
“There is a need for theoretical and empirical research around justifications of AI decision making [37].”
Relative explainability and double standards in medical decision-making
Kempt, H., Heilinger, J., & Nagel, S. K. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09646-x · Cited by 38
A Transparency Index Framework for AI in EducationPreprint
Chaudhry, M. A., Cukurova, M., & Luckin, R. (2022) · EdArXiv (OSF Preprints) · doi:10.35542/osf.io/bstcf · Cited by 77
Danry, V., Pataranutaporn, P., Groh, M., & Epstein, Z. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713408 · Cited by 35
Borrego-Díaz, J., & Galán-Páez, J. (2022) · Minds and Machines · doi:10.1007/s11023-022-09603-z · Cited by 34
Artificial intelligence and democratic legitimacy. The problem of publicity in public authority
Beckman, L., Rosenberg, J. H., & Jebari, K. (2022) · AI & Society · doi:10.1007/s00146-022-01493-0 · Cited by 47
How do patients perceive the AI-radiologists interaction? Results of a survey on 2119 responders
Ibba, S., Tancredi, C., Fantesini, A., Cellina, M. I., Presta, R., Montanari, R., Papa, S., & Alì, M. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110917 · Cited by 30
Nandutu, I., Atemkeng, M., & Okouma, P. (2021) · Ai & Society · doi:10.1007/s00146-021-01285-y · Cited by 24
“The laws must support the disclosure of information such as conservation laws and policies, data sets, and algorithms, which lead to perceived legitimacy (de Fine Licht and de Fine Licht 2020), since it minimises harm and improves AI (Ienca and Vayena 2019).”
BackgroundFilgueiras, F. D. B. (2021) · AI & Society · doi:10.1007/s00146-021-01201-4 · Cited by 35
“This process involves not only technical questions, but also political questions (Fine Licht and Fine Licht 2020; Binns 2018).”
BackgroundLee, F. (2020) · Science & Technology Studies · doi:10.23987/sts.75323 · Cited by 26
Towards an effective transnational regulation of AI
Gervais, D. J. (2021) · AI & Society · doi:10.1007/s00146-021-01310-0 · Cited by 41
“This might explain why such obligations have “intuitive appeal” (Selbst and Barocas 2018; de Fine Licht and de Fine Licht 2020).”
Bond, A., Cilliers, D., Retief, F., Alberts, R. C., Roos, C., & Moolman, J. (2024) · Impact Assessment and Project Appraisal · doi:10.1080/14615517.2024.2320591 · Cited by 32
Using artificial intelligence to enhance patient autonomy in healthcare decision-making
Quiñones, J. L. G. (2024) · AI & Society · doi:10.1007/s00146-024-01956-6 · Cited by 39
“7 Furthermore, it is not obvious that total transparency completely eliminates problems associated with accessibility and comprehensibility of AI systems (de Fine Licht and de Fine Licht 2020).”
Enhancing Transparency in Smart Farming: Local Explanations for Crop Recommendations Using LIME
Akkem, Y., Biswas, S. K., Aruna, V., & Tripathi, D. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.450 · Cited by 35
Can medical algorithms be fair? Three ethical quandaries and one dilemma
Bærøe, K., Gundersen, T., Henden, E., & Rommetveit, K. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100445 · Cited by 22
Budaev, S. V., Kristiansen, T. S., Giske, J., & Eliassen, S. (2020) · Royal Society Open Science · doi:10.1098/rsos.201886 · Cited by 22
Principle-based recommendations for big data and machine learning in food safety: the P-SAFETY model
Sapienza, S., & Vedder, A. (2021) · Ai & Society · doi:10.1007/s00146-021-01282-1 · Cited by 15
“When this is the case, some authors suggest that transparency shall focus on the rationale underlying the decision rather than the process through which the decision has been taken, consistently with the principle of explicability (de Fine Licht and de Fine Licht 2020, p. 919).”
BackgroundCugurullo, F. (2024) · AI and Ethics · doi:10.1007/s43681-024-00476-9 · Cited by 28
Verma, A., Singh, A. D., Lughofer, E. D., Cheng, X., & Abualsaud, K. (2021) · Journal of Computing in Higher Education · doi:10.1007/s12528-021-09291-1 · Cited by 26
“This algorithm ensures trust among stakeholders in terms of transparency as the two-way handshaking method opened the ‘Black Box’ of AI algorithms (de Fine Licht and de Fine Licht, 2020) to improve transparency.”
MethodLünich, M., & Kieslich, K. (2022) · AI & Society · doi:10.1007/s00146-022-01412-3 · Cited by 26
“The high complexity of such systems may lead to a lack of comprehension, especially among those in the general public with little technical knowledge about the underlying technology (de Fine Licht and de Fine Licht 2020).”
The Adoption of Artificial Intelligence in Bureaucratic Decision-making: A Weberian Perspective
Presuel, R. C., & Sierra, J. M. M. (2023) · Digital Government Research and Practice · doi:10.1145/3609861 · Cited by 40
“‖ [122] Others argue that is better to focus on contextual importance and contextual utility in order to be able to provide at least a degree of explainability to citizens.”
BackgroundMy job is a game, and I am the owner: How gamification facilitates self-leadership for gig workers
Mao, M., Zhang, L., Li, J.-M., & Rivkin, W. (2024) · Journal of business research · doi:10.1016/j.jbusres.2024.114877 · Cited by 23
Yhee, Y., & Koo, C. (2025) · Tourism Management · doi:10.1016/j.tourman.2025.105305 · Cited by 22
Anscomb, C. (2024) · AI & Society · doi:10.1007/s00146-024-02083-y · Cited by 20
Radanliev, P. (2023) · The Review of Socionetwork Strategies · doi:10.1007/s12626-023-00139-x · Cited by 21
To err is human: Bias salience can help overcome resistance to medical AI
Isaac, M. S., Wang, R. J.-H., Napper, L. E., & Marsh, J. K. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108402 · Cited by 22
Artificial intelligence in corporate governance
Ustahaliloğlu, M. K. (2025) · Corporate Law & Governance Review · doi:10.22495/clgrv7i1p11 · Cited by 19
Tensions in transparent urban AI: designing a smart electric vehicle charge point
Alfrink, K., Keller, I., Doorn, N., & Kortuem, G. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01436-9 · Cited by 18
“…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).”
BackgroundGoverning with Intelligence: The Impact of Artificial Intelligence on Policy Development
Yar, M. A. E., Hamdan, M., Anshari, M., Fitriyani, N. L., & Syafrudin, M. (2024) · Information · doi:10.3390/info15090556 · Cited by 34
Public perception of military AI in the context of techno-optimistic society
Lillemäe, E., Talves, K., & Wagner, W. (2023) · AI & Society · doi:10.1007/s00146-023-01785-z · Cited by 22
“Public acceptance has been considered one of the key factors for the success of new technologies as perceived legitimacy is fundamental in democratic systems (Hoff and Bashir 2015; de Fine Licht and de Fine Licht 2020; Oth-man 2021; Selwyn and Cordoba 2021).”
InfluentialWang, R., & Ophir, Y. (2024) · Journalism · doi:10.1177/14648849241284575 · Cited by 19
“…the potential limitations and biases of AJ, they might inadvertently prime readers to think about such biases, which could increase public skepticism, particularly among those without who are not predisposed to perceive AI is objective (De Fine Licht & De Fine Licht, 2020; Schmidt et al., 2020).”
Knowledge and support for AI in the public sector: a deliberative poll experiment
Arnesen, S., Broderstad, T. S., Fishkin, J. S., Johannesson, M. P., & Siu, A. (2024) · AI & Society · doi:10.1007/s00146-024-02104-w · Cited by 29
“Several investigations on citizens’ perspectives have been undertaken in recent years to uncover how and where (see e.g., (De Fine Licht and De Fine Licht 2020; Binns 2018; Arnesen and Johannesson 2022; Grgic-Hlaca et al. 2018).”
Integrating Large Language Models into Higher Education: Guidelines for Effective Implementation
Licht, K. D. F. (2023) · International Society for the Study of Information Summit (IS4SI Summit) · doi:10.3390/cmsf2023008065 · Cited by 19
Self-citationAI Usage Cards: Responsibly Reporting AI-Generated Content
Wahle, J. P., Ruas, T., Mohammad, S. M., Meuschke, N., & Gipp, B. (2023) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl57899.2023.00060 · Cited by 28
“, public decision-making [3], robotics [17]), they are still limited as they do not specify to which aspects of the scientific work AI systems contributed.”
BackgroundSandoval‐Almazán, R., Millán-Vargas, A. O., & García-Contreras, R. (2024) · Government Information Quarterly · doi:10.1016/j.giq.2024.101986 · Cited by 16
Public perceptions of the use of artificial intelligence in Defence: a qualitative exploration
Hadlington, L., Karanika‐Murray, M., Slater, J., Binder, J. F., Gardner, S. E., & Knight, S. (2024) · AI & Society · doi:10.1007/s00146-024-01871-w · Cited by 16
“Other researchers have noted the importance of transparency when it comes to AI-decision-making particularly in critical areas if negative outcomes are to be avoided (de Fine Licht and de Fine Licht 2020).”
Business intelligence tools in finance: A review of trends in the USA and Africa
Adewumi, A., Oshioste, E. E., Asuzu, O. F., Ndubuisi, N. L., Awonuga, K. F., & Daraojimba, O. H. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.3.0333 · Cited by 16
“However, the adoption of AI and ML has also raised concerns about the potential negative consequences of self-learning technologies, emphasizing the need for transparency and accountability in public decision-making processes (Licht & Licht, 2020).”
Lünich, M., & Keller, B. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658953 · Cited by 27
Filgueiras, F. D. B. (2021) · Reforma y democracia. · doi:10.69733/clad.ryd.n79.a221 · Cited by 24
Burgess, P. (2021) · AI & Society · doi:10.1007/s00146-021-01170-8 · Cited by 24
“1 In 2020 alone, a number of articles were published both on which the argument in this article is based and which are extended by the argument in this article (Araujo et al. 2020; Boyles and Joaquin 2020; Cristianini and Scantamburlo 2020; de Fine Licht and de Fine Licht 2020; Hagendorff and Wezel 2020; Leyva and Bec…”
BackgroundInfluentialDecoding the Black Box: A Comprehensive Review of Explainable Artificial Intelligence
Embarak, O. H. (2023) · International Conference on Information Technology Trends (ITT) · doi:10.1109/itt59889.2023.10184238 · Cited by 14
Sun, T., & Li, Y. (2024) · American Journal of Industrial and Business Management · doi:10.4236/ajibm.2024.144024 · Cited by 17
“…or biased, users can claim responsibility for manipulators based on disclosed algorithms and better monitor platform decision-making processes (de Fine Licht & de Fine Licht, 2020); on the other hand, algorithmic transparency upholds the right of workers to be informed and provides them with…”
BackgroundLünich, M., Keller, B., & Marcinkowski, F. (2023) · Technology Knowledge and Learning · doi:10.1007/s10758-023-09698-y · Cited by 29
Tanksley, T., Smith, A. D. R., Sharma, S., & Huff, E. W. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713510 · Cited by 19
The Challenges of Artificial Judicial Decision-Making for Liberal Democracy
Winter, C. K. (2022) · Economic analysis of law in European legal scholarship · doi:10.1007/978-3-031-11744-2_9 · Cited by 17
Social trust and public digitalization
van Kersbergen, K., & Svendsen, G. T. (2022) · AI & Society · doi:10.1007/s00146-022-01570-4 · Cited by 19
“However, there has also been much attention for the considerable risks and challenges involved in the deployment of ADMS (see Pasquale 2015; de Fine Licht and de Fine Licht 2020; Robinson 2020; Eschenbach 2021; Filgueiras 2021).”
Chiappetta, A. (2023) · Internet Policy Review · doi:10.14763/2023.4.1733 · Cited by 15
The Impact of Artificial Intelligence on Digital Media Content Creation
Hussain, A. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24jul927 · Cited by 17
Information that matters: Exploring information needs of people affected by algorithmic decisions
Schmude, T., Koesten, L. M., Möller, T. B., & Tschiatschek, S. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103380 · Cited by 17
“Our proposed XAI Novice Question Bank (Figure 2) extends the well-known XAI Question Bank [59]by including questions on thesystem context and usage.”
BackgroundMethod
Metadata
- Authors
- de Fine Licht, K., & de Fine Licht, J.
- Venue
- AI & SOCIETY
- Published
- 2020-03-19
- Volume
- 35
- Issue
- 4
- Pages
- 917-926
- ISSN
- 0951-5666 (ISSN-L)
- 1435-5655
- OpenAlex
- W3011363941
- Semantic Scholar
- 02471925d8cd9cc14bfb575bd8ef2ded74d2e86f
- Cited by
- 334
- References
- 65
- Citations by year
- 2026: 83 · 2025: 78 · 2024: 56 · 2023: 49 · 2022: 41 · 2021: 21 · 2020: 6
- FWCI
- 39.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- AI & Society (publishedVersion, cc-by)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Funders
- Chalmers Tekniska Högskola
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Keywords
- transparency, perceived legitimacy, black box problem, accountability, justification of decisions, democratic legitimacy, public acceptance of AI
About these checks
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.
A language model drew the claims from the paper on 2026-10-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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