System to Integrate Fairness Transparently: An Industry Approach
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There have been significant research efforts to address the issue of unintentional bias in Machine Learning (ML). Many well-known companies have dealt with the fallout after the deployment of their products due to this issue. In an industrial context, enterprises have large-scale ML solutions for a broad class of use cases deployed for different swaths of customers. Trading off the cost of detecting and mitigating bias across this landscape over the lifetime of each use case against the risk of impact to the brand image is a key consideration. We propose a framework for industrial uses that addresses their methodological and mechanization needs. Our approach benefits from prior experience handling security and privacy concerns as well as past internal ML projects. Through significant reuse of bias handling ability at every stage in the ML development lifecycle to guide users we can lower overall costs of reducing bias.
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