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What’s the problem?

However, numerous challenges require participatory auditing by stake holders

Bias vs. Fairness Perception

Bias, as an AI technical concept, does not necessarily lead to unfairness as perceived by stakeholders or in law. Fairness is often only evaluated for protected characteristics, but stakeholders are also sensitive to other, non-protected features and wider issues of equity and justice in society as well as harms going beyond current technical definitions and mitigations. In predictive AI, assessing bias and fairness often relies on technical measures related to ground truth. However, for many domains and applications, ground truth might not be fully aligned with stakeholders’ perspectives of fairness. Further, there are currently no standards of what counts as ‘good enough’ to pass an audit.

Fairness and Performance Trade-offs

There is often a trade-off between measures of performance and fairness, and even within fairness measures. Stakeholders’ notions of what is ‘fair’ or ‘biased’ differ markedly between domains and application contexts. Even within stakeholder groups there is sometimes little consensus of how to define and achieve less biased, fairer outcomes.

Limitations of AI Auditing

We might not have access to the training data nor the model itself so auditing can only be conducted on input and outputs. Even if we have access to the data and model, we might not be able to expose the training data or the model during the audit without revealing sensitive aspects, such as personal data or confidential/privileged information.

Risks of Expert-Only Audits

If we leave audits to AI experts, they might not do this at all or choose measures that are technically easier to define and operationalise instead of fully capturing stakeholders’ perspectives. This could lead to accusations of ‘fairwashing’ when stakeholder involvement is lacking.

Empowering Stakeholders

However, giving stakeholders without AI expertise the power to audit is complex. Responsible AI-related concepts, technologies and approaches need to be communicated in an accessible way. Support needs to be given to stakeholders for auditing, and stakeholders need to be provided with ways to feedback on issues that need to be acted upon after an audit.
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