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Characterizing Bluesky Content Moderation Service: From Automation of Service to Landscape of Harms
Empirical research on content moderation is fundamentally constrained by the opaque deployment of moderation systems on major social …
Pushpdeep Singh, Sayeh Jarollahi, Ayan Majumdar, Vabuk Pahari, Abhijnan Chakraborty, Krishna P. Gummadi, Ingmar Weber, Abhisek Dash
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Characterizing Bluesky Content Moderation Service: From Automation of Service to Landscape of Harms
From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recourse
Algorithmic recourse aims to provide actionable recommendations that enable individuals to change unfavorable model outcomes, and prior …
Lena Marie Budde, Ayan Majumdar, Richard Uth, Markus Langer, Isabel Valera
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Evaluating LLMs for Detecting Demographic-Targeted Social Bias: A Comprehensive Benchmark Study
Large-scale web-scraped text corpora used to train general-purpose AI models often contain harmful demographic-targeted social biases, …
Ayan Majumdar, Feihao Chen, Jinghui Li, Xiaozhen Wang
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Evaluating LLMs for Detecting Demographic-Targeted Social Bias: A Comprehensive Benchmark Study
CARMA: A practical framework to generate recommendations for causal algorithmic recourse at scale
Algorithms are increasingly used to automate large-scale decision-making processes, e.g., online platforms that make instant decisions …
Ayan Majumdar, Isabel Valera
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CARMA: A practical framework to generate recommendations for causal algorithmic recourse at scale
Do Invariances in Deep Neural Networks Align with Human Perception?
An evaluation criterion for safe and trustworthy deep learning is how well the invariances captured by representations of deep neural …
Vedant Nanda, Ayan Majumdar, Camila Kolling, John P. Dickerson, Krishna P. Gummadi, Bradley C. Love, Adrian Weller
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Do Invariances in Deep Neural Networks Align with Human Perception?
Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making
Decision making algorithms, in practice, are often trained on data that exhibits a variety of biases. Decision-makers often aim to take …
Miriam Rateike, Ayan Majumdar, Olga Mineeva, Krishna P. Gummadi, Isabel Valera
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Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making