Since personalized recommender systems have the power to influence what we think and do, we need to understand the risks they pose to our society. The DSA’s new transparency regime is a promising step forward, but we still need external, adversarial audits by independent research facilities.
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Assessing algorithmic systems is an indispensable step towards the protection of democracy, human rights, and the rule of law. At the moment, it is largely unclear how it can and should be done. With the support of Alfred Landecker Foundation, we are developing ideas, procedures, and best practices to effectively assess algorithms for systemic risks, test them in practice, and advocate for their adoption.
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Recommender systems, such as a social network’s news feed or a streaming service’s recommendations, are notoriously difficult to audit. Despite high hurdles, practitioners from journalism and academia are pushing forward.
Read moreMichele Loi und Matthias Spielkamp analyze the regulatory content of 16 guideline documents about the use of AI in the public sector, by mapping their requirements to those of our philosophical account of accountability, and conclude that while some guidelines refer processes that amount to auditing, it seems that the debate would benefit from more clarity about the nature of the entitlement of auditors and the goals of auditing, also in order to develop ethically meaningful standards with respect to which different forms of auditing can be evaluated and compared.
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