ISO/IEC TR 24027:2021, titled “Information Technology — Artificial Intelligence — Bias in AI Systems and AI-Aided Decision Making,” is a technical report published by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) in November 2021. This document provides comprehensive guidelines for identifying, assessing, and mitigating bias in Artificial Intelligence (AI) systems, particularly those involved in decision-making processes. It emphasizes the importance of addressing bias throughout the entire AI system lifecycle to ensure fairness, transparency, and reliability in AI applications.
Scope and Purpose
The primary objective of ISO/IEC TR 24027:2021 is to offer a structured approach to detect and manage bias in AI systems and AI-assisted decision-making. Recognizing that bias can originate from various sources—including data collection, model training, and human cognitive biases—the report outlines measurement techniques and assessment methods aimed at identifying and mitigating these biases. It covers all phases of the AI system lifecycle, including data collection, training, continual learning, design, testing, evaluation, and deployment.
Understanding Bias in AI Systems
Bias in AI systems can manifest in multiple ways, often reflecting existing societal prejudices or systemic disparities. While some bias may be intentional to meet specific objectives (desired bias), unintended or unwanted bias can lead to unfair or discriminatory outcomes. The report categorizes bias into several types:
• Data Bias: Arises from non-representative or skewed datasets that do not accurately reflect the target population.
• Automation Bias: The tendency of humans to favor suggestions from automated systems, potentially overlooking contradictory information.
• Cognitive Bias: Bias introduced by human prejudices and subjective judgments during system design or data interpretation.
Understanding these categories is crucial for developing strategies to identify and mitigate bias in AI systems.
Assessing and Measuring Bias
ISO/IEC TR 24027:2021 emphasizes the need for robust assessment and measurement techniques to detect bias in AI systems. The report suggests several approaches:
• Statistical Analysis: Utilizing statistical methods to identify disparities in data representation and model outcomes across different groups.
• Performance Metrics: Evaluating the performance of AI models to ensure consistent accuracy and reliability across diverse demographic groups.
• Continuous Monitoring: Implementing ongoing monitoring mechanisms to detect and address bias that may emerge during the AI system’s operation.
These methodologies enable organizations to quantify bias and assess the fairness of their AI systems effectively.
Mitigation Strategies
To address identified biases, the report recommends several mitigation strategies:
• Data Preprocessing: Ensuring datasets are representative and free from inherent biases by employing techniques such as data balancing and augmentation.
• Algorithmic Fairness: Incorporating fairness constraints and bias mitigation algorithms during the model development phase.
• Human Oversight: Establishing oversight mechanisms where human judgment complements AI decision-making, particularly in high-stakes scenarios.
Implementing these strategies can significantly reduce unwanted bias, leading to more equitable AI systems.
Lifecycle Considerations
The report underscores the importance of addressing bias at every stage of the AI system lifecycle:
• Design Phase: Incorporating fairness objectives and ethical considerations into the system’s design requirements.
• Development Phase: Applying bias detection and mitigation techniques during model training and validation.
• Deployment Phase: Monitoring AI systems in real-world environments to identify and rectify any emergent biases.
A proactive approach throughout the lifecycle ensures that bias is managed effectively, enhancing the system’s overall trustworthiness.
Conclusion
ISO/IEC TR 24027:2021 serves as a vital resource for organizations aiming to develop and deploy AI systems responsibly. By providing detailed guidelines on identifying, assessing, and mitigating bias, the report promotes the creation of AI applications that are fair, transparent, and aligned with societal values. Adhering to these guidelines not only enhances the ethical standards of AI systems but also fosters public trust and acceptance of AI-driven solutions.