1 citations · 1 across the 4 of their papers we have counts for
4 papers
ReVise: A Human-AI Interface for Incremental Algorithmic Recourse
Kaustav Bhattacharjee, Jun Yuan, Aritra Dasgupta
The recent adoption of artificial intelligence in socio-technical systems raises concerns about the black-box nature of the resulting decisions in fields such as hiring, finance, a…
Fooling SHAP with Output Shuffling Attacks
Jun Yuan, Aritra Dasgupta
Explainable AI~(XAI) methods such as SHAP can help discover feature attributions in black-box models. If the method reveals a significant attribution from a ``protected feature'' (…
TRIVEA: Transparent Ranking Interpretation using Visual Explanation of Black-Box Algorithmic Rankers
Jun Yuan, Kaustav Bhattacharjee, Akm Zahirul Islam +1
Ranking schemes drive many real-world decisions, like, where to study, whom to hire, what to buy, etc. Many of these decisions often come with high consequences. For example, a uni…
Rankers, Rankees, & Rankings: Peeking into the Pandora's Box from a Socio-Technical Perspective
Jun Yuan, Julia Stoyanovich, Aritra Dasgupta
Algorithmic rankers have a profound impact on our increasingly data-driven society. From leisurely activities like the movies that we watch, the restaurants that we patronize; to h…