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Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI
Open accessAbstract
In this paper, we argue for a paradigm shift from the current model of explainable artificial intelligence (XAI), which may be counter-productive to better human decision making. In early decision support systems, we assumed that we could give people recommendations and that they would consider them, and then follow them when required. However, research found that people often ignore recommendations because they do not trust them; or perhaps even worse, people follow them blindly, even when the recommendations are wrong. Explainable artificial intelligence mitigates this by helping people to understand how and why models give certain recommendations. However, recent research shows that people do not always engage with explainability tools enough to help improve decision making. The assumption that people will engage with recommendations and explanations has proven to be unfounded. We argue this is because we have failed to account for two things. First, recommendations (and their explanations) take control from human decision makers, limiting their agency. Second, giving recommendations and explanations does not align with the cognitive processes employed by people making decisions. This position paper proposes a new conceptual framework called Evaluative AI for explainable decision support. This is a machine-in-the-loop paradigm in which decision support tools provide evidence for and against decisions made by people, rather than provide recommendations to accept or reject. We argue that this mitigates issues of over- and under-reliance on decision support tools, and better leverages human expertise in decision making.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This position paper argues that recommendation-driven explainable AI ("recommend and defend") is counterproductive for decision support because it removes user agency and mismatches human cognitive processes, and proposes instead a conceptual framework called Evaluative AI, in which tools provide evidence for and against the decision maker's own hypotheses rather than recommendations. The authors frame this as a machine-in-the-loop, hypothesis-driven paradigm that retains explainable AI's tools while shifting control to the human.
Key findings
- The paper's central claim is that current XAI decision support fails because (1) recommendations and their explanations take control from decision makers, limiting agency, and (2) they do not align with the cognitive processes people actually use when deciding.
- An informal survey conducted by the author during a talk found "just three out of over 100 people preferred Bluster" (the recommendation-giving friend), with the remaining 100+ preferring Prudence (who gives evidence for and against the user's own proposals).
- The authors report that experimental studies indicate most participants do not cognitively engage with explainability tools, and that those who do may become over-confident, even when explanations contain no useful information; they also cite Buçinca et al. and Gajos and Mamykina showing explainability did not mitigate over-reliance and in some cases increased it.
- Evaluative AI is claimed to support options, judgement, understanding, and trade-offs (per Table 4), whereas recommendation, XAI, and interpretability paradigms provide only partial or no support for options and trade-offs; cognitive forcing is described as an improvement but still a "recommend and defend" approach.
Methods
- Design: a position/conceptual paper presenting a new framework (Evaluative AI) rather than an empirical study; it reviews related work on cognitive decision making, abductive reasoning, the Data/Frame theory of sensemaking, and four dominant decision-support paradigms (recommendations; recommendations with explanations; interpretable models; cognitive forcing).
- Data/sample: no primary dataset; the only original data reported is an informal audience survey (3 of 100+ preferring the recommendation-giving "Bluster"), plus a referenced but unpublished observational study of expert board gamers using an AI recommendation system.
- Analysis: conceptual comparison of the five paradigms against six criteria for good decision support derived from Yates and Potworowski's 10 "cardinal decision issues," summarized in Tables 2 and 4, plus an illustrative skin-cancer diagnosis example (ISIC 2018, seven disease categories).
Limitations
- Stated by the paper: if people dismiss recommendations and explainability information, they may equally fail to engage with evidence-based support; the framework makes the same assumption that people care what a machine says.
- Stated by the paper: evaluative AI will likely increase cognitive load and force more engagement, which decision makers may prefer less (citing Buçinca et al.), and balancing information reduction against engagement is a challenge.
- Evident from the text: the framework is conceptual and unevaluated — no empirical test of Evaluative AI is reported, the supporting survey is informal and non-peer-reviewed, and one cited observational study is described as "currently unpublished."
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Claims the paper makes
52 Own interpretation · 43 Not checked yet · 3 Own result · 3 No basis given
A result the paper reports from its own data — 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 a study where expert board gamers used an AI-based recommendation system, we saw that many players did not truly engage with recommendations or explanatory information either.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and 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
“'Recommend and defend' approaches, which we call recommendation-driven decision support, do little to help us critique its answers or our own ideas.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Current AI decision aids are designed to tell the user what the authors think the best answer is and explain why that is considered the best answer.
None of the sources cited for this claim have been checked yet.
In the paper
“research found that people often ignore recommendations because they do not trust them”
- Also stated in: § 3.1 Giving recommendations with no explanatory information · plain
“people tend to ignore this due to unwarranted distrust [9,10,13,26,37,39]”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Abstract
What the sources say
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- Also stated in: § 3.1 Giving recommendations with no explanatory information · plain
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In the paper
“We argue that the reason this Prudence-like approach is more effective for decision support because it aligns with the cognitive decisionmaking process that people use when making judgements and decisions [11,17,30].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
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In the paper
“or perhaps even worse, people follow them blindly, even when the recommendations are wrong.”
- Also stated in: § 3.1 Giving recommendations with no explanatory information · plain
“accept wrong decisions due to unwarranted trust [1,6,29]”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Abstract
What the sources say
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- Also stated in: § 3.1 Giving recommendations with no explanatory information · 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
“First, recommendations (and their explanations) take control from human decision makers, limiting their agency.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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- Based: unclear — It is unclear why the paper says it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Second, giving recommendations and explanations does not align with the cognitive processes employed by people making decisions.”
- Also stated in: § 1 INTRODUCTION · plain
“We argue that the reason this Prudence-like approach is more effective for decision support because it aligns with the cognitive decisionmaking process that people use when making judgements and decisions [11,17,30].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Abstract
What the sources say
- Not checked yetcites [11]
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- Also stated in: § 1 INTRODUCTION · 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
“and better leverages human expertise in decision making.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Abstract
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
“likely because providing recommendations makes them fixate on that recommendation.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
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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
“The assumption that people will engage with recommendations and explanations has proven to be unfounded.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“However, people find it difficult to correctly calibrate their trust in a decision aid [10,13,21,37].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
What the sources say
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In the paper
“Second, Bluster reduces our locus of control [36], because we cannot control which options we received feedback for.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
What the sources say
- Not checked yetcites [36]
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In the paper
“The ability to assess the strengths and weaknesses of judgements and decisions is key to expert decision making [18,20].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
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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
“This helps with trust calibration because the machine does not give recommendations”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“However, recent research shows that people do not always engage with explainability tools enough to help improve decision making.”
- Also stated in: § 2.3 Explainable AI and decision making · hedged
“Some experimental studies [2,5,42] indicate that most participants do not cognitively engage with explainability tools.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Abstract
What the sources say
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- Also stated in: § 2.3 Explainable AI and decision making · 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
“We argue that this mitigates issues of overand under-reliance on decision support tools”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Abstract
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 well as over/underreliance because there is no recommendation to follow.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
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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
“We conclude that for decision making, the recommendationdriven paradigm of explainable AI is 'dead' (for some situations)”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 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
“In this paper, we argue for a paradigm shift from the current model of explainable artificial intelligence (XAI), which may be counterproductive to better human decision making.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Abstract
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In the paper
“For example, Coderre et al. [3] found that intuition significantly outperformed hypotheticodeductive reasoning in gastroenterology clinical diagnoses.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
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- Not checked yetcites [3] source
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In the paper
“Some experimental studies [2,5,42] indicate that most participants do not cognitively engage with explainability tools.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
What the sources say
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In the paper
“and (5) does not provide understanding of the machine decision.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.1 Giving recommendations with no explanatory information
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“For example, many experiments showing biases system 1 thinking, such as base-rate fallacies, could be partly attributed to the experiment design [35]”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
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In the paper
“Second, there is now sufficient research questioning whether the system 1 vs system 2 distinction is so clear [25,35].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
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In the paper
“However, issues such as warranted and unwarranted distrust [13] mean that AI systems are often deployed and then largely ignored [9,37].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
What the sources say
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In the paper
“As such, any technique that aims to 'override' intuitive thinking risks losing the benefits of expertise and prior knowledge.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
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In the paper
“In either case, a poor decision can often be attributed to fixation [17] -searching for evidence to support one hypothesis without considering others.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
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In the paper
“Contrastive explanations [27] partially support trade-offs when they answer 'Why 𝐴 instead of 𝐵?', where 𝐴 is the output and 𝐵 a foil.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.2 Giving recommendations with explanatory information
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In the paper
“These studies show that the recommendation-driven XAI does little to mitigate over-and under-reliance.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
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In the paper
“Good decision makers search for evidence that both supports and refutes a hypothesis.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Acceptability
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In the paper
“First, research in Naturalistic Decision Making (NDM) demonstrates that 'system 1' thinking, which they call intuition, is a powerful source of problem solving that often produces better and/or faster decisions than system 2 [16].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
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In the paper
“(4) does not support making trade-offs”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.1 Giving recommendations with no explanatory information
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In the paper
“Klein et al. show that people make decisions by first using their intuition to narrow down to a set of likely options (the frames), and then going through each option one-by-one, searching for evidence (data) to make judgements.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Acceptability
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In the paper
“(3) does not help determine stakeholder values”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.1 Giving recommendations with no explanatory information
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In the paper
“The model's options, possibilities, and trade-offs would have to be calculated by the decision maker.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.3 Giving recommendations with an interpretable model
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In the paper
“Fourth, from the earlier example, it is clear that many existing tools will play a part in evaluative AI: constrastive explanation [27], Weights of Evidence (WoE) [24], feature importance, case-based reasoning techniques [38], etc., all generate evidence.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.4 Long live explainable AI!
What the sources say
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In the paper
“However, this still defends the recommendation.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.2 Giving recommendations with explanatory information
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In the paper
“(2) provides evidence both for and against each option, irrelevant of the judged likelihood of that option.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.1.3 Trade-off support.
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In the paper
“However, interpretable models are not designed with decision support in mind.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.3 Giving recommendations with an interpretable model
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In the paper
“(4) partially supports making trade-offs”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.2 Giving recommendations with explanatory information
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In the paper
“(3) does not help determine stakeholder values”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.1 Giving recommendations with no explanatory information
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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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
“However, evaluative AI is not a panacea for DAs.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 5 CONCLUSION
plain- 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
“(3) does not help determine stakeholder values;”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.3 Summary
plain- 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
“However, following a model of abductive reasoning can still reduce information compared to having no decision support; e.g.presenting only the most likely hypotheses; prioritising access to the most important information, etc.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.5 Challenges and Limitations
plainconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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
“The commonality between these three approaches is that decisions are initially withheld from the decision maker, but explanatory information is provided, forcing the decision maker to engage with the explanatory information.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.4 Cognitive forcing
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Cognitive forcing takes a similar approach, but current cognitive forcing techniques are recommendation-driven [1,6].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.1.1 Options.
plain- 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
“(1) explains trade-offs between any two sets of options; and”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.1.3 Trade-off support.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: unclear — It is unclear why the paper says it holds.
- Probably not a substantial claim. 36
- It may state two separate claims as one. 34
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An informal survey conducted by the author during a recent talk showed that just three out of over 100 people preferred Bluster; the remaining 100+ people preferring Prudence.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
plainn = 100- Asserted: passes — The authors put it forward as true.
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- 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 result the paper reports from its own data — 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 they did, we saw some behavioural changes, but not enough to see a statistically significant improvement in decision making.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
plain- 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.
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
“It is more suitable for medium-and high-stakes decisions when the decision maker is ultimately accountable, and low frequency decision making where the decision maker has time to explore options.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
plaincorrelational- 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 may give no reason why it holds. It may state two separate claims as one.
In the paper
“They mitigate fixation by either allowing the decision maker to determine which options are best or helping them to narrow down to a manageable set of options [34].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
What the sources say
- Not checked yetcites [34]
plaindirect- 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.
In the paper
“Both under-and overreliance have negative consequences [29].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
What the sources say
- Not checked yetcites [29] source
plaindirect- Asserted: passes — The authors put it forward as true.
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- 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
“The effect of system 1 thinking may differ between contexts such as the experience of the decision maker and the stakes of the decision.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
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.
Probably not a substantial claim.
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 indicates that Evaluative AI could prove useful by not fixating people on particular recommendations, but allowing people to assess whether evidence supports their hypotheses, rather than trying to understand the DA's reasoning.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
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
“Giving only recommendations: (1) does not help to provide new options or to filter out unlikely options”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.1 Giving recommendations with no explanatory information
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Participants who do seem to believe that they understand explanatory information, leading to over-confidence [2], even when presented with explanations that contain no useful information [5].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Interestingly, despite cognitive forcing functions being more effective, they were least preferred by participants.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Similarly, in experiments on a general task with general participants, Buçinca et al. [1] and Gajos and Mamykina [6] show that explainability did not mitigate over-reliance, and in some cases it increased over-reliance.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
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
“but that the evaluative AI paradigm is still a form of explainable AI”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
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 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
“and many of the current paradigm will play a part in this new conceptualisation; so, long live explainable AI!”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 1 INTRODUCTION
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- 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.
In the paper
“This is a common criteria for AI-based DAs [9], and is important to calibrate trust & reliance, and to find mistakes.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2 AI-ASSISTED DECISION SUPPORT 2.1 Decision making and decision support
What the sources say
- Not checked yetcites [9] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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- 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.
None of the sources cited for this claim have been checked yet.
In the paper
“However, recent research has shown that the recommendation-driven explainable/interpretable AI has little effect on decision making [1, 6-8, 28, 32], although this is not always the case [22,23,41].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
What the sources say
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.
In the paper
“Klein et al. [17] argue that abductive reasoning plays a central role in this process.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Acceptability
What the sources say
- Not checked yetcites [17]
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.
Probably not a substantial claim.
In the paper
“Overreliance means decision makers accept a machine recommendations even when it is wrong, caused by unwarranted trust [13].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
What the sources say
- Not checked yetcites [13] source
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: 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.
None of the sources cited for this claim have been checked yet.
In the paper
“Their results showed that cognitive forcing slightly mitigated overreliance, compared to feature-based and uncertainty judgements, particularly disregarding incorrect AI recommendations, but with no statistically significant differences in task performance.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
plainconditional- 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. 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
“So, explainability provides understanding.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.2 Giving recommendations with explanatory information
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.
In the paper
“Under-reliance means decision makers reject a machine recommendation even when it is correct, caused by unwarranted distrust [13].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.3 Explainable AI and decision making
What the sources say
- Not checked yetcites [13] source
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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Abductive reasoning is the process of forming hypotheses and judging their likelihood for the purpose of explaining observations or facts [30].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2.2 Cognitive processes for decision making
What the sources say
- Not checked yetcites [30]
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
“Decision makers need to 'invert' the explainable AI process to align with the decisionmaking process.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > Acceptability
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.
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
“(2) does not consider possibilities or judgements beyond one option”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.1 Giving recommendations with no explanatory information
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.
Probably not a substantial claim.
In the paper
“However, as a model, it misses some important aspects of decision support; importantly: that decision making is a process, not a point in time.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 2 AI-ASSISTED DECISION SUPPORT 2.1 Decision making and decision support
What the sources say
- Not checked yetcites [12] source
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 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
“Second, it does not provide helpful decision-making information for options other than the recommendation.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.4 Cognitive forcing
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: fails — The paper may give no reason why it holds.
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
“Evaluative AI explicitly aims to provide support for options, judgement, understanding, and trade-offs.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.3 Summary
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.
None of the sources cited for this claim have been checked yet.
In the paper
“(2) Control: Evaluative AI explicitly hands control of which hypotheses are investigated and prioritised to the decision maker, resulting a machine-in-the-loop paradigm [7,8], rather than a human-in-the-loop paradigm.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.3 Summary
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
“Giving explanatory information from the start may help the decision maker to focus on useful information.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.4 Cognitive forcing
hedgedconditional- 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
“New models of XAI are needed, but existing work lays a solid foundation.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.4 Long live explainable AI!
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: 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’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
“Second, for a machine to judge decisions, we will need an underlying decision-making model, meaning that recommendation approaches such as machine learning, planning, and optimisation will be required, along with XAI techniques.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.4 Long live explainable AI!
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
“We conclude by repeating that the current paradigm of explainable AI as justified recommendations is 'dead'.But the new paradigm that includes hypothesis-driven explainability could take the throne, so long live explainable AI!”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 5 CONCLUSION
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: 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.
- The authors state this as an assumption: People care at all about what a machine has to say.
None of the sources cited for this claim have been checked yet.
In the paper
“However, one argument against this is that this evaluative AI provides decision makers with better control [36,43], and follows a process that they will naturally follow, rather than recommendation-driven approaches, which somewhat disrupt the decision-making process.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.5 Challenges and Limitations
What the sources say
- Not checked yetcites [36]
- Not checked yetcites [43] source
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: 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.
In the paper
“(1) Cognitive processes: Evaluative AI is built on the Data/Frame model of sensemaking [17], so supports the decision maker's cognitive process by allowing them to explore hypotheses,”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.3 Summary
What the sources say
- Not checked yetcites [17]
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.
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
“Further, there are applications where recommendation-driven approaches may be the best way to improve decisions, such as making decisions at scale.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.4 Long live explainable AI!
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.
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
“Third, evaluative AI is explainable AI.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.4 Long live explainable AI!
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.
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 this reason, we assert that evaluative AI obtains the benefits of cognitive forcing, while giving control to the decision maker to explore the strengths and weaknesses of any option, rather than just the strengths recommendations and the weaknesses of alternatives.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.3 Summary
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.
- The authors state this as an assumption: People care at all about what a machine has to say.
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
“(1) partially helps to provide new options or to filter out unlikely options by forcing the decision maker to do so”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.4 Cognitive forcing
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
“and (5) does provide understanding of the machine decision.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.2 Giving recommendations with explanatory information
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.
Probably not a substantial claim.
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
“Bluster can be useful; for example, in low stakes decisions or if time is limited; but overall, Bluster does not support our cognitive process as well as Prudence.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 5 CONCLUSION
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.
In the paper
“More recently, Drury et al. [4] proposed a framework in which machines consider their own option awareness to support human decision makers.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.1.3 Trade-off support.
What the sources say
- Not checked yetcites [4] 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.
None of the sources cited for this claim have been checked yet.
In the paper
“Our friend Prudence makes us do more work, which people prefer to avoid [19], so we may find that participants prefer less work, but with worse results Buçinca et al. [1].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 5 CONCLUSION
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
“Withholding recommendations 'forces' the decision maker to cognitively engage with the decision and therefore, to consider different options and make tradeoffs.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.4 Cognitive forcing
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
“Our friends Bluster and Prudence can both be useful in supporting our decisions, but Prudence puts us in control and is better at helping us weigh up different options.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 5 CONCLUSION
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: People care at all about what a machine has to say.
In the paper
“The cognitive and social aspects of good explainability apply to evaluative AI: [26]: (1) explanations are contrastive; (2) explanations are selected; (3) explanations are interactive; and (4) explanations are causal.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.4 Long live explainable AI!
What the sources say
- Not checked yetcites [26]
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.
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
“First, there are applications of XAI beyond decision support, such as verification, regulation, scientific discovery, generating insights about underlying data, etc.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.4 Long live explainable AI!
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Pfaff et al. [31] show that using visual analytics to allow decision makers to explore options increases their option awareness, resulting in more accurate and faster decisions.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.1.3 Trade-off support.
What the sources say
- Not checked yetcites [31] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether 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
“(2) does not consider possibilities or judgements beyond one option”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.1 Giving recommendations with no explanatory information
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
“Evaluative AI supports both, rather than aiming to persuade that the machine is correct.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.1.3 Trade-off support.
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
“Importantly, the decision maker to maintains control over which hypotheses to explore.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.1 Conceptual framework
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: fails — The paper may give no reason why it holds.
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 model of Evaluative AI, which aligns with decision making processes, keeping the decision maker in control and asking users to rely on evidence instead of recommendations.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 5 CONCLUSION
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.
In the paper
“Evaluative AI will likely result in designs that force more engagement with the decision, but less preferred by decision makers [1].”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.5 Challenges and Limitations
What the sources say
- Not checked yetcites [1] source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether 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
“Rudin [33] argues that for high-stakes decisions, people should avoid using black-box models altogether, and instead should use interpretable models.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.3 Giving recommendations with an interpretable model
What the sources say
- Not checked yetcites [33] 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. 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
“Evaluative AI does not (necessarily) provide recommendations, but instead offers support to filter out unlikely options, generate new hypotheses, or both.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 4.1 Conceptual framework
hedged- 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.
In the paper
“Interpretable models use a small set of features with a low complexity of interaction between them.”
§ Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI > 3.3 Giving recommendations with an interpretable model
What the sources say
- Not checked yetcites [33] source
plaincorrelational- 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. It may state two separate claims as one.
The authors state this as an assumption.
The initial assumption of AI-assisted decision aids was that people would follow recommendations when required, leading to better decisions.
“The initial assumption of AI-assisted DAs was that people would follow recommendations when required, leading to better decisions.”
The authors state this as an assumption.
For situations in which decision aids can handle many more factors simultaneously, they can provide insight when the assumptions of the underlying modelling theory hold.
“The assumption here is that for situations in which DAs can handle many more factors simultaneously, it can provide insight when the assumptions of the underlying modelling theory hold.”
The authors state this as an assumption.
Decision makers will carefully consider recommendations.
“This assumes that decision makers will carefully consider recommendations.”
The authors state this as an assumption.
Because an interpretable model is interpretable, it contains most of the explanatory information that is required.
“The assumption is that because the model is interpretable, it contains most of the explanatory information that is required.”
The authors state this as an assumption.
Forcing people to engage with the decision cognitively can mitigate over-reliance.
“The assumption of cognitive forcing is that forcing people to engage with the decision cognitively can mitigate over-reliance.”
The paper relies on this without support.
Current AI decision aids are designed to tell the user what the authors think the best answer is and explain why that is considered the best answer.
“AI decision aids are designed to tell the user what they think the best answer is (e.g. a recommendation), and explain why that is considered the best answer.”
The authors state this as an assumption.
People care at all about what a machine has to say.
“The evaluative AI framework makes the same assumptions as earlier work: that people care at all about what a machine has to say.”
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?
In the citing paper
“Explainable artificial intelligence mitigates to help people to understand how and why models give certain recommendations [90].”
From Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review (2023) · cites this paper as [90]
Citations
Cites
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · cited 1×
“First, it is hard for people to know how much to trust recommendations [Jacovi et al., 2021, Hoffman, 2017, Lee and See, 2004, Sivaraman et al., 2023].”
BackgroundHumans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554 · cited 2×
“Both under-and over-reliance have negative consequences [Parasuraman and Riley, 1997].”
BackgroundDARPA's Explainable Artificial Intelligence Program
Gunning, D., & Aha, D. W. (2019) · AI Magazine · doi:10.1609/aimag.v40i2.2850 · Cited by 1,360
Designing the user interface: strategies for effective human-computer interaction
Briggs, J. S. (1987) · Information and Software Technology · doi:10.1016/0950-5849(87)90040-1 · Cited by 1,153
Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021) · arXiv · doi:10.48550/arxiv.2102.09692 · Cited by 1,124
Designing Theory-Driven User-Centric Explainable AI
Wang, D., Yang, Q., Abdul, A., & Lim, B. Y. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300831 · Cited by 906
Zhang, Y., Liao, Q. V., & Bellamy, R. K. E. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372852 · Cited by 895
Manipulating and Measuring Model Interpretability
Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445315 · Cited by 728
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Jacovi, A., Marasović, A., Miller, T., & Goldberg, Y. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445923 · Cited by 545
Self-citation
Research works printed but not linked: 10
- Robert R Hoffman. 2017. A taxonomy of emergent trusting in the human–machine relationship. Cognitive systems engineering: The future for a changing world (2017), 137–164.
- Robert R Hoffman, Tim Miller, and William J Clancey. 2022. Psychology and AI at a Crossroads: How Might Complex Systems Explain Themselves?The American journal of psychology 135, 4 (2022), 365–378.
- Daniel Kahneman. 2011. Thinking Fast and Slow. Penguin UK.
- Harmanpreet Kaur, Eytan Adar, Eric Gilbert, and Cliff Lampe. 2022. Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory. (May 2022). arxiv:2205.05057 [cs.HC] http://arxiv.org/abs/2205.05057 · arXiv
- Gary Klein, Jennifer K Phillips, Erica L Rall, and Deborah A Peluso. 2007. A data–frame theory of sensemaking. In Expertise out of context. Psychology Press, 118–160.
- 10.31219/osf.io · DOI
- David Alvarez Melis, Harmanpreet Kaur, Hal Daumé, III, Hanna Wallach, and Jennifer Wortman Vaughan. 2021. From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 35–47. https://ojs.aaai.org/index.php/HCOMP/article/view/18938
- Hugo Mercier and Dan Sperber. 2017. The Enigma of Reason. Harvard University Press.
- Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence (2019). https://www.sciencedirect.com/science/article/pii/S0004370218305988
- J W Rudolph. 2003. Into the big muddy and out again: Error persistence and crisis management in the operating room. Ph. D. Dissertation.
Cited by
Sorted by citations
Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023) · Education Sciences · doi:10.3390/educsci13121216 · Cited by 980
Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. D., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102301 · Cited by 626
Adversarial attacks and defenses in explainable artificial intelligence: A survey
Baniecki, H., & Biecek, P. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102303 · Cited by 234
Mehrotra, S., Degachi, C., Vereschak, O., Jonker, C. M., & Tielman, M. L. (2024) · ACM Journal on Responsible Computing · doi:10.1145/3696449 · Cited by 219
Natali, C., Marconi, L., Dias Duran, L. D., & Cabitza, F. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11352-1 · Cited by 143
Metadata
- Authors
- Miller, T.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Miller, Tim
- ORCID
- Miller, Tim
- Venue
- 2023 ACM Conference on Fairness Accountability and Transparency
- Publisher
- ACM
- Published
- 2023-06-12
- Pages
- 333-342
- OpenAlex
- W4380366109
- Cited by
- 172
- References
- 38
- Citations by year
- 2026: 55 · 2025: 64 · 2024: 47 · 2023: 6
- FWCI
- 30.53
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Funders
- Australian Research Council (DP190103414)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Keywords
- explainable AI, decision support system, human decision-making, recommendation trust, human agency, cognitive processes in decision making, overreliance on AI, human expertise
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.
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