3 résultats pour « safety »
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This paper examines the rise of algorithmic harms from AI, such as privacy erosion and inequality, exacerbated by accountability gaps and algorithmic opacity. It critiques existing legal frameworks in the US, EU, and Japan as insufficient, and proposes refined impact assessments, individual rights, and disclosure duties to enhance AI governance and mitigate harms.
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“Using cybersecurity certification as the basis for providing a complete defense to liability may not prevent every harm from occurring. However, if organizations invest in certification to avoid legal liability, this should collectively improve the resilience and quality of technology products in the United States and beyond.”
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The paper explores the challenges of building a #safetyculture for #ai, including the lack of consensus on #risk prioritization, a lack of standardized #safety practices, and the difficulty of #culturalchange. The authors suggest a comprehensive strategy that includes identifying and addressing #risks, using #redteams, and prioritizing safety over profitability.
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