IBM Research | ICLR Paper: Training Individual Fair ML Models with Sensitive Substance Robustness @ibmresearch | Uploaded June 2020 | Updated October 2024, 4 days ago.
Written in collaboration with University of Michigan researchers, describes a positive step forward in the design of machine learning models that verify an algorithm is provably fair when evaluating data. Fairness is a characteristic that is obviously essential for establishing trust—would any company entrust, for example, an AI-based resume screening system that might potentially be biased against certain candidates based on their name or other superficial factors? The researchers trained ML models that were fair in the sense that the models’ performance was consistent and unbiased, regardless of the data they were applied to.
Written in collaboration with University of Michigan researchers, describes a positive step forward in the design of machine learning models that verify an algorithm is provably fair when evaluating data. Fairness is a characteristic that is obviously essential for establishing trust—would any company entrust, for example, an AI-based resume screening system that might potentially be biased against certain candidates based on their name or other superficial factors? The researchers trained ML models that were fair in the sense that the models’ performance was consistent and unbiased, regardless of the data they were applied to.
