Nonmaleficence, the principle of mitigating harm, complements the ethic of beneficence, the principle of doing good. Nonmaleficence, emphasizes the professional and moral obligation to avoid causing intentional or unintentional harm. It calls for safety, security, and risk management to ensure AI systems do not inflict foreseeable harm on individuals or society. Nonmaleficence aligns with harm-prevention measures like avoiding discrimination, protecting privacy, and preventing physical or psychological harm. It also addresses broader societal risks, such as loss of trust, erosion of skills, and adverse impacts on social well-being or infrastructure.
Implementing nonmaleficence in AI requires both technical and governance strategies. Technically, approaches like privacy-by-design, security features, and robust data quality evaluations are critical to mitigating risks and preventing harmful outcomes. Governance strategies include oversight mechanisms such as audits, tests, and continuous monitoring by internal teams, independent third parties, or governmental entities. Collaborative efforts across disciplines and stakeholders are essential for establishing ethical standards, regulatory frameworks, and risk assessment protocols. Ethical guidelines often discourage harmful applications of AI, such as military use or cyberwarfare, emphasizing the need to safeguard AI against misuse.
While nonmaleficence strives to prevent harm, it acknowledges that some risks are unavoidable. In such cases, ethical AI development requires robust risk assessment, reduction, and mitigation strategies, as well as clear mechanisms for attributing liability. This principle is particularly relevant to the dual-use nature of AI technologies, where innovations designed for positive purposes, such as medical diagnostics, may also be exploited for harmful ends.
By embedding nonmaleficence into AI research, design, and deployment, developers and policymakers can foster trust and ensure AI technologies prioritize safety, security, and societal well-being, serving humanity responsibly and ethically.
Recommended Reading
Anna Jobin, Marcello Ienca, and Effy Vayena. "The Global Landscape of AI Ethics Guidelines." Nature Machine Intelligence 1 (2019): 389–399.
Edition 1.0 Research: This article is in initial research development. We welcome your input to strengthen the foundation. Please share your feedback in the chat below.
