A survey of algorithmic recourse: contrastive explanations and consequential recommendations
Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …
where decisions have consequential effects on individuals' lives. In these settings, in …
Data-centric ai: Perspectives and challenges
The role of data in building AI systems has recently been significantly magnified by the
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …
[HTML][HTML] The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making
Governments look at explainable artificial intelligence's (XAI) potential to tackle the criticisms
of the opaqueness of algorithmic decision-making with AI. Although XAI is appealing as a …
of the opaqueness of algorithmic decision-making with AI. Although XAI is appealing as a …
Problems with Shapley-value-based explanations as feature importance measures
Game-theoretic formulations of feature importance have become popular as a way to"
explain" machine learning models. These methods define a cooperative game between the …
explain" machine learning models. These methods define a cooperative game between the …
Algorithmic recourse: from counterfactual explanations to interventions
As machine learning is increasingly used to inform consequential decision-making (eg, pre-
trial bail and loan approval), it becomes important to explain how the system arrived at its …
trial bail and loan approval), it becomes important to explain how the system arrived at its …
Counterfactual explanations can be manipulated
Counterfactual explanations are emerging as an attractive option for providing recourse to
individuals adversely impacted by algorithmic decisions. As they are deployed in critical …
individuals adversely impacted by algorithmic decisions. As they are deployed in critical …
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Machine learning is increasingly used to inform decision-making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …
where decisions have consequential effects on individuals' lives. In these settings, in …
The hidden assumptions behind counterfactual explanations and principal reasons
Counterfactual explanations are gaining prominence within technical, legal, and business
circles as a way to explain the decisions of a machine learning model. These explanations …
circles as a way to explain the decisions of a machine learning model. These explanations …
How explainability contributes to trust in AI
We provide a philosophical explanation of the relation between artificial intelligence (AI)
explainability and trust in AI, providing a case for expressions, such as “explainability fosters …
explainability and trust in AI, providing a case for expressions, such as “explainability fosters …
Disentangling fairness perceptions in algorithmic decision-making: the effects of explanations, human oversight, and contestability
Recent research claims that information cues and system attributes of algorithmic decision-
making processes affect decision subjects' fairness perceptions. However, little is still known …
making processes affect decision subjects' fairness perceptions. However, little is still known …