AI2030 Responsible AI Tools Marketplace

Key Takeaways

Multiple explanations are better than one: Rather than providing a single counterfactual explanation for why an AI system made a decision, research shows that offering diverse sets of explanations helps users better understand the model's reasoning, since different users have different needs and preferences for what makes an explanation useful.
"Diversity" in AI explanations lacks a clear definition: The paper identifies that the field currently uses many conflicting definitions of what makes counterfactual explanations "diverse," making it difficult for developers to know which approach best serves users. This lack of standardization is a significant challenge for responsible AI implementation.
User needs should drive explanation design: The paper emphasizes that the properties considered "good" in an explanation should be selected based on actual user needs, which are often unknown and vary between individuals. This highlights the importance of involving diverse stakeholders in designing AI explanation systems rather than relying on technical assumptions alone.
Explanations can be optimized along multiple dimensions: Counterfactual explanations can be designed to vary across different dimensions—such as the types of changes suggested, the scope of modifications, or the realism of proposed scenarios—and the paper categorizes these approaches to help clarify which dimensions matter most for different use cases.
More research needed on practical diversity: The paper identifies that significant gaps remain in understanding how to effectively generate and evaluate diverse explanations in real-world settings, suggesting this is an active area where responsible AI practices are still being developed.

Abstract

In the field of Explainable Artificial Intelligence (XAI), counterfactual examples explain to a user the predictions of a trained decision model by indicating the modifications to be made to the instance so as to change its associated prediction. These counterfactual examples are generally defined as solutions to an optimization problem whose cost function combines several criteria that quantify desiderata for a good explanation meeting user needs. A large variety of such appropriate properties can be considered, as the user needs are generally unknown and differ from one user to another; their selection and formalization is difficult. To circumvent this issue, several approaches propose to generate, rather than a single one, a set of diverse counterfactual examples to explain a prediction. This paper proposes a review of the numerous, sometimes conflicting, definitions that have been proposed for this notion of diversity. It discusses their underlying principles as well as the hypotheses on the user needs they rely on and proposes to categorize them along several dimensions (explicit vs implicit, universe in which they are defined, level at which they apply), leading to the identification of further research challenges on this topic.

Key Takeaways

Multiple explanations are better than one: Rather than providing a single counterfactual explanation for why an AI system made a decision, research shows that offering diverse sets of explanations helps users better understand the model's reasoning, since different users have different needs and preferences for what makes an explanation useful.
"Diversity" in AI explanations lacks a clear definition: The paper identifies that the field currently uses many conflicting definitions of what makes counterfactual explanations "diverse," making it difficult for developers to know which approach best serves users. This lack of standardization is a significant challenge for responsible AI implementation.
User needs should drive explanation design: The paper emphasizes that the properties considered "good" in an explanation should be selected based on actual user needs, which are often unknown and vary between individuals. This highlights the importance of involving diverse stakeholders in designing AI explanation systems rather than relying on technical assumptions alone.
Explanations can be optimized along multiple dimensions: Counterfactual explanations can be designed to vary across different dimensions—such as the types of changes suggested, the scope of modifications, or the realism of proposed scenarios—and the paper categorizes these approaches to help clarify which dimensions matter most for different use cases.
More research needed on practical diversity: The paper identifies that significant gaps remain in understanding how to effectively generate and evaluate diverse explanations in real-world settings, suggesting this is an active area where responsible AI practices are still being developed.

Abstract

Achieving Diversity in Counterfactual Explanations: a Review and Discussion

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

Achieving Diversity in Counterfactual Explanations: a Review and Discussion

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links