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AI Regulation in Life Sciences: The EU AI Act is a key regulatory development focusing on a risk-based approach to AI governance, particularly in high-risk areas like healthcare. Switzerland, a prominent AI hub, faces the challenge of balancing innovation with international regulatory alignment.
Managing AI Risks: The EY Trusted AI Framework helps organizations understand and mitigate AI risks across various dimensions, including performance, bias, resilience, explainability, and transparency. Integrating AI risk management with existing ERM programs is crucial.
Black Box Models & Challenges: While AI offers significant potential for life sciences, "black box" models raise concerns about interpretability, potential bias, data privacy, and ethical considerations, necessitating careful mitigation strategies.
Building Trustworthy AI: Life science companies must prioritize building trust and transparency into their AI systems. This involves addressing potential issues related to human oversight, data quality, modeling techniques, and comprehensive documentation. Collaboration with experts can help navigate the evolving regulatory landscape.
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AI Regulation in Life Sciences: The EU AI Act is a key regulatory development focusing on a risk-based approach to AI governance, particularly in high-risk areas like healthcare. Switzerland, a prominent AI hub, faces the challenge of balancing innovation with international regulatory alignment.
Managing AI Risks: The EY Trusted AI Framework helps organizations understand and mitigate AI risks across various dimensions, including performance, bias, resilience, explainability, and transparency. Integrating AI risk management with existing ERM programs is crucial.
Black Box Models & Challenges: While AI offers significant potential for life sciences, "black box" models raise concerns about interpretability, potential bias, data privacy, and ethical considerations, necessitating careful mitigation strategies.
Building Trustworthy AI: Life science companies must prioritize building trust and transparency into their AI systems. This involves addressing potential issues related to human oversight, data quality, modeling techniques, and comprehensive documentation. Collaboration with experts can help navigate the evolving regulatory landscape.

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