Non-maleficence

Non-maleficence is a core ethical principle originating from the Hippocratic oath, 'first, do no harm.' In the context of Artificial Intelligence, it mandates an obligation to prevent AI systems from causing harm to individuals, groups, or society throughout their lifecycle. This includes physical, psychological, financial, and social harm. The European Commission's 'Ethics Guidelines for Trustworthy AI' identifies it as a key principle. It is distinct from beneficence, which is a positive duty to do good, whereas non-maleficence is a negative duty to avoid harm. Within risk management frameworks like ISO/IEC 42001, this principle drives the identification, assessment, and mitigation of potential harms, such as algorithmic bias or privacy infringements, forming the bedrock of trustworthy AI.

Enterprises can apply non-maleficence through a structured, three-step process. First, conduct a systematic impact assessment, such as an Algorithmic Impact Assessment (AIA) or a Data Protection Impact Assessment (DPIA) under GDPR Article 35, to identify potential harms. Second, implement mitigation measures during the design phase based on frameworks like the NIST AI Risk Management Framework (RMF). This includes using fairness toolkits to address bias and integrating privacy-by-design principles. Third, establish continuous monitoring and auditing post-deployment to track performance and detect emergent harmful behaviors. A global bank implementing this approach reduced biased lending decisions by 40% and achieved a 100% pass rate in regulatory audits.

Taiwanese enterprises face three primary challenges. First, a lack of specific domestic AI regulation creates uncertainty. The solution is to proactively adopt international standards like ISO/IEC 42001 and the EU AI Act's risk-based approach. Second, there is a shortage of interdisciplinary talent skilled in AI ethics, law, and technology. This can be mitigated by forming a cross-functional AI ethics committee and investing in targeted training. Third, addressing inherent bias in training data is a significant technical hurdle. Implementing robust data governance and utilizing bias detection tools are crucial. The priority should be to establish an internal governance framework, followed by talent development and tool implementation.

Winners Consulting specializes in Non-maleficence for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact