With the rise of AI, algorithms have become better at learning underlying patterns from the training data including ingrained social biases based on gender, race, etc. Deployment of such algorithms to domains such as hiring, healthcare, law enforcement, etc. has raised serious concerns about fairness, accountability, trust and interpretability in machine learning algorithms. To alleviate this problem, we propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases from tabular datasets. It uses a graphical causal model to represent causal relationships among different features in the dataset and as a medium to inject domain knowledge. A user can detect the presence of bias against a group, say females, or a subgroup, say black females, by identifying unfair causal relationships in the causal network and using an array of fairness metrics. Thereafter, the user can mitigate bias by refining the causal model and acting on the unfair causal edges. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset based on the current causal model while ensuring a minimal change from the original dataset. Users can visually assess the impact of their interactions on different fairness metrics, utility metrics, data distortion, and the underlying data distribution. Once satisfied, they can download the debiased dataset and use it for any downstream application for fairer predictions. We evaluate D-BIAS by conducting experiments on 3 datasets and also a formal user study. We found that D-BIAS helps reduce bias significantly compared to the baseline debiasing approach across different fairness metrics while incurring little data distortion and a small loss in utility. Moreover, our human-in-the-loop based approach significantly outperforms an automated approach on trust, interpretability and accountability.
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FINS Auditing Framework: Group Fairness for Subset Selection
Subset selection is an integral component of AI systems that is increasingly affecting people’s livelihoods in applications ranging from hiring, healthcare, education, to financial decisions. Subset selections powered by AI-based methods include top- analytics, data summarization, clustering, and multi-winner voting. While group fairness auditing tools have been proposed for classification systems, these state-of-the-art tools are not directly applicable to measuring and conceptualizing fairness in selected subsets. In this work, we introduce the first comprehensive auditing framework, FINS, to support stakeholders in interpretably quantifying group fairness across a diverse range of subset-specific fairness concerns. FINS offers a family of novel measures that provide a flexible means to audit group fairness for fairness goals ranging from item-based, score-based, and a combination thereof. FINS provides one unified easy-to-understand interpretation across these different fairness problems. Further, we develop guidelines through the FINS Fair Subset Chart, that supports auditors in determining which measures are relevant to their problem context and fairness objectives. We provide a comprehensive mapping between each fairness measure and the belief system (i.e., worldview) that is encoded within its measurement of fairness. Lastly, we demonstrate the interpretability and efficacy of FINS in supporting the identification of real bias with case studies using AirBnB listings and voter records.
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- Award ID(s):
- 2007932
- NSF-PAR ID:
- 10338008
- Date Published:
- Journal Name:
- AAAI/ACM Conference on AI, Ethics, and Society (AEIS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3514094.3534160
