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Nesta lanceert AI Governance map 2.0

Throughout the year, organisations of various types and sizes have continued to produce ethics guidelines, which now number over 100, and many have established ethics advisory boards. In order to create some clarity in this crowded space, multiple attempts have emerged that seek to catalog and analyse (Berkman Klein Center project; ETH Zurich project) the norms that are being put forward. While there is an emerging consensus around certain high-level principles that should underpin AI development, such as transparency, fairness and privacy, there is much less agreement about how these principles should be interpreted and operationalised in practice. In addition, concerns have been raised that the principle-based approach may be largely unsuited to AI governance.

Ethics, as the predominant mechanism of AI governance, has itself been profoundly challenged. Amid a wave of controversy, Google dismantled its ethics advisory board in April 2019, the European Union’s High-Level Expert Group on AI came under heavy criticism for the dominance of industry voices, the crowding out of civil society and downright ethics-washing, and revelations of the undue influence of Silicon Valley interests on the global AI ethics discourse shook the community. However, it would be a mistake to conclude that ethical reflection is superfluous. There is a lot that ethicists can contribute to teasing out the various ways in which high-level norms may be interpreted and implemented in computer code development or organisational processes. Yet, in the absence of regulation or practical mechanisms for the enforcement of principles, ethical codes inevitably play little role in shaping behaviour or incentives. Ethical guidelines can often feel like tweaking at the surface of more profound issues that exist at the level of business models and incentive structures.

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