Interactive Fairness Auditing: Leveraging AVOIR for Dynamic Evaluation and Mitigation
Summary: Streamlit-based UI for AVOIR fairness monitoring: metric selection, dynamic visuals, and DSL-driven constraint refinement. Runtime bias detection with probabilistic guarantees via AVOIR inference/optimization, demonstrated across datasets, pairing declarative fairness specs with intuitive visuals for responsible ML. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Amin Meghrazi (Ohio State University)
- 2. Pranav Maneriker (Ohio State University)
- 3. Swati Padhee (Ohio State University)
- 4. Srinivasan Parthasarathy (Ohio State University)
BibTeX Citation
@inproceedings{meghrazi_sigmod25,
title = {{Interactive Fairness Auditing: Leveraging AVOIR for Dynamic Evaluation and Mitigation}},
author = {Meghrazi, Amin and Maneriker, Pranav and Padhee, Swati and Parthasarathy, Srinivasan},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725108},
url = {https://dl.acm.org/doi/10.1145/3722212.3725108},
year = {2025}
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