ISO/IEC 42005 (Artificial intelligence — AI system impact assessment) is an emerging international standard providing guidelines for organizations to conduct a thorough and systematic impact assessment of their Artificial Intelligence (AI) systems. The standard is designed to help organizations of all sizes and sectors proactively identify, analyze, and evaluate potential effects—both positive and negative—that an AI system might have on individuals, groups, society, and the environment. This includes assessing impacts related to fundamental rights, ethical considerations, bias, privacy, security, and socio-economic factors. Adherence helps organizations implement responsible AI governance, manage risks, ensure trustworthiness, and comply with relevant regulations, thereby fostering responsible innovation and building stakeholder confidence in AI deployment.

Use Case

An international financial services corporation is developing a sophisticated, proprietary AI-driven credit scoring model intended to automate and expedite loan application decisions globally. To ensure responsible deployment and maintain regulatory compliance, the corporation mandates the use of ISO/IEC 42005 to conduct a comprehensive AI System Impact Assessment (AIA).

The AIA process begins with defining the scope and context, identifying the AI system's purpose (automated loan approval), its intended users (loan officers and applicants), and the operational environment. Stakeholder identification is crucial, including data scientists, legal teams, risk management, consumer advocates, and regulatory bodies.

Next, potential impacts are rigorously identified and analyzed. The team focuses on risks like algorithmic bias (e.g., disproportionately denying credit to specific demographic groups based on training data), data privacy breaches (handling sensitive financial information), lack of transparency and explainability (making it difficult for applicants to understand rejection reasons), and potential socio-economic impacts (e.g., financial exclusion). Positive impacts, such as increased efficiency and reduced human error, are also documented.

The risk evaluation phase quantifies the severity and likelihood of identified negative impacts. For instance, the risk of discriminatory lending practices is rated as high severity due to legal and reputational damage. Mitigation strategies are then developed, such as implementing a fairness audit pipeline to continuously monitor for disparate impact, establishing clear human-in-the-loop oversight for complex cases, and developing enhanced explainable AI (XAI) features to provide concise rejection rationales to applicants.

Finally, the corporation establishes a monitoring and review plan to periodically re-assess the AI system's impacts after deployment, ensuring the effectiveness of mitigation measures and adapting to changing regulatory landscapes or performance drift. The complete AIA documentation provides a solid foundation for AI governance and accountability, significantly increasing the trustworthiness of the model among regulators and consumers.