The approach builds on the assumptions of the standard fairness model recently proposed by Pleˇcko and Bareinboim, enabling a principled evaluation of fairness in terms of causal effects, particularly as the complexity of the data structures increases.
A thorough literature review is provided to encapsulate prior research on bias identification and fairness auditing, categorizing the findings according to various stages of study and proposing a unified pipeline for dataset integration and a modular framework for bias auditing.
Nani Kartik Kaveti, T. Pattanshetti· Discover Artificial Intellig...· 0 citations
This work introduces Fairabel, a novel approach that repairs fairness by mutating the training data under the guidance of causal models and multi-objective optimization, and shows that Fairabel reduces ML software bias by 50% on average across three fairness metrics, outperforming the state-of-the-art with a 9% relativ...
Ying Xiao, Zhen-Peng Chen, Yepang Liu et al.· ACM Transactions on Software...· 0 citations
It is argued that BBQ-style multiple-choice abstention benchmarks measure a single structural cue, and a model that solves them does not thereby become fair, and is called for evaluation suites that cover a broader spectrum of fairness alignment.
Nai-Hao Deng, Samee Arif, Shuai-Chen Chang et al.· 0 citations
REMI, a framework for the automated localization, explanation, and mitigation of individual discrimination, is presented, which introduces a bidirectional relational explanation framework that learns over paired examples $(x, x')$ to identify regions of the input space where fairness is violated.
Ranit Debnath Akash, Ashish Kumar, Gang Tan et al.· Proceedings of the ACM on So...· 0 citations
It is found that large language models tend to prefer stricter fairness constraints than humans, show more self-interested behavior, are sensitive to how information is framed, and are difficult to align with human judgments using fine-tuning with current datasets.
Qi-Shen Han, Hadi Hosseini, Joshua Kavner et al.· 0 citations
Large language models are increasingly used in contexts where their outputs can affect people directly, including hiring, admissions, and lending. This growing role makes it important to consider not only how well these models perform, but also whether their behavior is fair. Although many fairness metrics, bias benchm...
Samah Alhazmi· International Journal of Adv...· 0 citations
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