Overview
CEN/CLC ISO/IEC/TR 24027:2023, titled Information technology - Artificial intelligence (AI) - Bias in AI systems and AI aided decision making, is a technical report that addresses the issue of bias throughout the lifecycle of AI systems. This European-adopted ISO/IEC report serves as a vital resource for organizations developing or deploying AI, providing guidance on understanding, measuring, and mitigating unwanted bias in both AI models and AI-assisted decision-making processes. The document encompasses all phases-data collection, design, training, validation, evaluation, deployment, and ongoing use-and highlights best practices for accurate assessment and treatment of bias to promote fairness, ethical use, and reliability in AI systems.
Key Topics
- Conceptual Foundations: Defines key terms such as bias, fairness, human cognitive bias, automation bias, data bias, and statistical bias within the context of AI and machine learning.
- Sources of Bias: Identifies potential sources of bias entering the AI pipeline, including human assumptions, societal influences, non-representative data sampling, labeling errors, engineering decisions, feature selection, and algorithmic choices.
- Types of Bias:
- Human cognitive biases (confirmation bias, group attribution bias, etc.)
- Data and statistical biases (non-representative sampling, processing errors)
- Engineering and design choices that can reinforce unwanted biases
- Assessment Techniques: Reviews measurement methodologies for detecting and evaluating bias, including:
- Confusion matrix analysis
- Fairness metrics (e.g., equalized odds, demographic parity, equality of opportunity)
- Predictive equality and other quantitative measures
- Lifecycle Approach: Stresses that bias risk exists across the full AI system lifecycle, from conception and data sourcing to continual learning and system updates.
- Mitigation Strategies: Offers approaches to reduce bias, such as diverse stakeholder engagement, transparent documentation, validation/testing processes, continuous monitoring, and re-evaluation after deployment.
Applications
CEN/CLC ISO/IEC/TR 24027:2023 is designed for broad applicability wherever artificial intelligence is developed, evaluated, or in use. Relevant use cases include:
- Healthcare: Ensuring diagnostic or triage algorithms do not disadvantage specific patient groups due to hidden data or design biases.
- Human Resources: Mitigating bias in AI-assisted recruitment, where training data may reflect historical discrimination.
- Finance: Avoiding unfair outcomes in credit scoring, risk assessment, or fraud detection systems.
- Public Sector and Law Enforcement: Reducing the risk of discrimination in automated decision systems for social services, predictive policing, or legal applications.
- Retail and Marketing: Creating fair recommendation systems and targeted advertising, minimizing unintentional exclusion of certain customer segments.
- Autonomous Systems: Ensuring equitable performance in AI-powered vehicles or robotics, where data collection or coding choices can lead to unexpected biases.
The standard serves data scientists, AI engineers, system designers, quality managers, and policymakers aiming for trustworthy, transparent, and fair AI adoption.
Related Standards
- ISO/IEC 22989: Artificial intelligence - Concepts and terminology
- ISO/IEC 23053: Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)
- Other AI ethics and fairness standards developed by ISO/IEC JTC 1 SC 42
The guidance within CEN/CLC ISO/IEC/TR 24027 complements broader frameworks on trustworthy AI, responsible AI lifecycle management, and machine learning validation.
By applying CEN/CLC ISO/IEC/TR 24027:2023, organizations across industries can systematically identify, assess, and mitigate bias in AI systems, striving for increased fairness, compliance, and societal trust in artificial intelligence solutions. This standard is a cornerstone for anyone implementing responsible AI and is essential for achieving regulatory, ethical, and operational goals in information technology.