29 citations · 74 across the 9 of their papers we have counts for
9 papers · 1 filter
Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in Practice
Jiahao Chen, Victor Storchan, Eren Kurshan
We review practical challenges in building and deploying ethical AI at the scale of contemporary industrial and societal uses. Apart from the purely technical concerns that are the…
Seven challenges for harmonizing explainability requirements
Jiahao Chen, Victor Storchan
Regulators have signalled an interest in adopting explainable AI(XAI) techniques to handle the diverse needs for model governance, operational servicing, and compliance in the fina…
Counterfactual Explanations for Arbitrary Regression Models
Thomas Spooner, Danial Dervovic, Jason Long +3
We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models. Our method is a globally co…
Model-Based Counterfactual Synthesizer for Interpretation
Fan Yang, Sahan Suresh Alva, Jiahao Chen +1
Counterfactuals, serving as one of the emerging type of model interpretations, have recently received attention from both researchers and practitioners. Counterfactual explanations…
Provable Multi-Objective Reinforcement Learning with Generative Models
Dongruo Zhou, Jiahao Chen, Quanquan Gu
Multi-objective reinforcement learning (MORL) is an extension of ordinary, single-objective reinforcement learning (RL) that is applicable to many real-world tasks where multiple o…
Debiasing classifiers: is reality at variance with expectation?
Ashrya Agrawal, Florian Pfisterer, Bernd Bischl +5
We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse r…