EY Global Study Finds Disconnect on Ethical Artificial Intelligence (AI) Priorities Between Public and Private Sector

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EY releases findings from a global survey finding significant differences in how the public and private sectors view the future of ethics, governance, privacy, policy and regulation of artificial intelligence (AI) technologies. According to the Bridging AI’s trust gaps report, developed in collaboration with The Future Society, AI discrepancies exist in four key areas: fairness and avoiding bias, innovation, data access and privacy and data rights.

In the survey of 71 policymakers and more than 280 global organizations, respondents ranked ethical principles by importance for 12 different AI use cases, and sentiment was measured around the risk and regulation of AI.

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Policymakers align around specific priorities, while private sector lacks consensus

Policymakers’ responses show widespread agreement on the ethical principles most relevant for different applications of AI. For example, on the use of AI for facial recognition policymakers rated “fairness and avoiding bias” and “privacy and data rights” as the two top concerns by a wide margin. Yet private sector priorities on the same question were relatively undifferentiated. In fact, the private sector responses across use cases and principles were more evenly distributed, with narrow margins defining the top choices. And the private sector’s top choices were principles prioritized by existing regulations, such as GDPR, rather than emerging issues such as fairness and non-discrimination.

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Disagreement about the future direction of governance poses risks  

While both policymakers and companies agree a multi-stakeholder approach is needed to guide the direction of AI governance, results show disagreement on what form it will take: thirty-eight percent of organizations surveyed expect the private sector to lead a multi-stakeholder framework, and only 6% of policymakers agree. This disconnect poses potential challenges for both groups in driving governance forward, and it also presents market and regulatory risks for companies developing AI products while governance approaches are still under discussion.

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