Unpacking and unboxing complexities Risk of bias As suggested in the previous part of this series, the use of AI in healthcare has the potential to improve care and efficiency in various ways. However, AI is notoriously prone to bias, which may be in terms of race, age, gender, disability, religion, sexual orientation, or socioeconomic background. In healthcare, bias could lead to significant harm to patients, including discrimination, poor care outcomes, and even the loss of life. Bias can exacerbate existing inequalities, underscoring the urgent need for organizations developing and deploying AI solutions in healthcare to prioritize mitigation measures and accountability. However, promisin
Insight
International: AI, privacy, and security - part three: Addressing bias, siloed governance, and data breach risks in healthcare
April 22, 2024
Summary
Part three of the insight series by ICT Legal Consulting addresses the risks of bias, siloed governance, and data breaches in healthcare AI systems, emphasizing the need for comprehensive mitigation strategies and interdisciplinary collaboration. AI systems in healthcare are prone to bias, which can lead to discrimination and poor care outcomes, necessitating accountability and documentation of algorithmic logic and data quality. Siloed governance in healthcare organizations can result in fragmented security postures and privacy shortcomings, highlighting the importance of cross-siloed governance and collaboration among cybersecurity, data privacy, and AI teams. The iterative nature of AI development poses risks of endless data and security remodeling, requiring strategic freeze points and ethical reviews to maintain responsible development practices. Effective AI implementation in healthcare demands proactive engagement with regulators and the use of regulatory sandboxes to manage risks and ensure compliance with data protection laws.