4 citations · 11 across the 8 of their papers we have counts for
8 papers
Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution
Falaah Arif Khan, Nivedha Sivakumar, Yinong Oliver Wang +5
Large language models (LLMs) have achieved impressive performance, leading to their widespread adoption as decision-support tools in resource-constrained contexts like hiring and a…
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs
Yinong Oliver Wang, Nivedha Sivakumar, Falaah Arif Khan +6
The recent rapid adoption of large language models (LLMs) highlights the critical need for benchmarking their fairness. Conventional fairness metrics, which focus on discrete accur…
Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation
Falaah Arif Khan, Denys Herasymuk, Nazar Protsiv +1
Data missingness is a practical challenge of sustained interest to the scientific community. In this paper, we present Shades-of-Null, an evaluation suite for responsible missing v…
The Unbearable Weight of Massive Privilege: Revisiting Bias-Variance Trade-Offs in the Context of Fair Prediction
Falaah Arif Khan, Julia Stoyanovich
In this paper we revisit the bias-variance decomposition of model error from the perspective of designing a fair classifier: we are motivated by the widely held socio-technical bel…
An Epistemic and Aleatoric Decomposition of Arbitrariness to Constrain the Set of Good Models
Falaah Arif Khan, Denys Herasymuk, Nazar Protsiv +1
Recent research reveals that machine learning (ML) models are highly sensitive to minor changes in their training procedure, such as the inclusion or exclusion of a single data poi…
Fairness as Equality of Opportunity: Normative Guidance from Political Philosophy
Falaah Arif Khan, Eleni Manis, Julia Stoyanovich
Recent interest in codifying fairness in Automated Decision Systems (ADS) has resulted in a wide range of formulations of what it means for an algorithmic system to be fair. Most o…
The State of AI Ethics Report (January 2021)
Abhishek Gupta, Alexandrine Royer, Connor Wright +9
The 3rd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in AI Ethics since October 2020. It aims to help anyone, from m…
The State of AI Ethics Report (October 2020)
Abhishek Gupta, Alexandrine Royer, Victoria Heath +12
The 2nd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in the field of AI Ethics since July 2020. This report aims to…