Towards a multi-stakeholder value-based assessment framework for algorithmic systems. (arXiv:2205.04525v1 [cs.LG])

In an effort to regulate Machine Learning-driven (ML) systems, current
auditing processes mostly focus on detecting harmful algorithmic biases. While
these strategies have proven to be impactful, some values outlined in documents
dealing with ethics in ML-driven systems are still underrepresented in auditing
processes. Such unaddressed values mainly deal with contextual factors that
cannot be easily quantified. In this paper, we develop a value-based assessment
framework that is not limited to bias auditing and that covers prominent
ethical principles for …

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