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Found 8 papers, 0 papers with code
On the Interdependence of Reliance Behavior and Accuracy in AI-Assisted Decision-Making
In AI-assisted decision-making, a central promise of putting a human in the loop is that they should be able to complement the AI system by adhering to its correct and overriding its mistaken recommendations.
Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making
In this work, we study the effects of feature-based explanations on distributive fairness of AI-assisted decisions, specifically focusing on the task of predicting occupations from short textual bios.
"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making
In this work, we conduct a human subject study to assess people's perceptions of informational fairness (i. e., whether people think they are given adequate information on and explanation of the process and its outcomes) and trustworthiness of an underlying ADS when provided with varying types of information about the system.
On the Relationship Between Explanations, Fairness Perceptions, and Decisions
It is known that recommendations of AI-based systems can be incorrect or unfair.
Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making
Automated decision systems (ADS) have become ubiquitous in many high-stakes domains.
Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems
It is often argued that one goal of explaining automated decision systems (ADS) is to facilitate positive perceptions (e. g., fairness or trustworthiness) of users towards such systems.
A Study on Fairness and Trust Perceptions in Automated Decision Making
Automated decision systems are increasingly used for consequential decision making -- for a variety of reasons.
A Ranking Approach to Fair Classification
In this paper, we focus on scenarios where only imperfect labels are available and propose a new fair ranking-based decision system based on monotonic relationships between legitimate features and the outcome.
