  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 algorithmic systems. Our framework presents a circular
arrangement of values with two bipolar dimensions that make common motivations
and potential tensions explicit. In order to operationalize these high-level
principles, values are then broken down into specific criteria and their
manifestations. However, some of these value-specific criteria are mutually
exclusive and require negotiation. As opposed to some other auditing frameworks
that merely rely on ML researchers' and practitioners' input, we argue that it
is necessary to include stakeholders that present diverse standpoints to
systematically negotiate and consolidate value and criteria tensions. To that
end, we map stakeholders with different insight needs, and assign tailored
means for communicating value manifestations to them. We, therefore, contribute
to current ML auditing practices with an assessment framework that visualizes
closeness and tensions between values and we give guidelines on how to
operationalize them, while opening up the evaluation and deliberation process
to a wide range of stakeholders.
