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Exploring Methods for Impact Quantification

Ryan JenkinsPI: Ryan Jenkins

Associate Professor of Philosophy
California Polytechnic State University

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Framework component: Evaluation

While successful machine learning applications have proliferated in the last decade, there have also been storied failures, generating worries that ML applications can be biased or unfair, opaque or unaccountable. Understanding these impacts, prioritizing them, and assessing the success of practices in computer science to address them requires that we can assign precise and concrete values to the impacts of ML applications. However, there are currently no widely used, uncontroversial quantitative impact metrics (QIMs) for concretely measuring the human impacts of machine learning, a gap which this project seeks to remedy. Having such metrics in hand will help to: (1) ensure machine learning applications demonstrate the appropriate sensitivity to ethical concerns such as privacy, transparency, bias, and others; (2) prioritize interventions at various stages of the application development and deployment pipeline to achieve greatest leverage; and (3) compare the success of mitigation strategies, which will be essential for driving towards a set of empirically-grounded best practices in machine learning.

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