Towards Integrating Fairness Transparently in Industrial Applications
This addresses the need for companies to manage bias in ML processes to ensure product integrity and protect brand image, though it appears incremental by building on existing concepts of fairness and transparency.
The paper tackles the problem of integrating fairness and transparency into industrial machine learning applications by proposing a systematic approach with mechanized and human-in-the-loop components for bias detection and mitigation, illustrated through a real-world use case of the SIFT system.
Numerous Machine Learning (ML) bias-related failures in recent years have led to scrutiny of how companies incorporate aspects of transparency and accountability in their ML lifecycles. Companies have a responsibility to monitor ML processes for bias and mitigate any bias detected, ensure business product integrity, preserve customer loyalty, and protect brand image. Challenges specific to industry ML projects can be broadly categorized into principled documentation, human oversight, and need for mechanisms that enable information reuse and improve cost efficiency. We highlight specific roadblocks and propose conceptual solutions on a per-category basis for ML practitioners and organizational subject matter experts. Our systematic approach tackles these challenges by integrating mechanized and human-in-the-loop components in bias detection, mitigation, and documentation of projects at various stages of the ML lifecycle. To motivate the implementation of our system -- SIFT (System to Integrate Fairness Transparently) -- we present its structural primitives with an example real-world use case on how it can be used to identify potential biases and determine appropriate mitigation strategies in a participatory manner.