ISO/IEC TR 24027

ISO/IEC TR 24027: A Comprehensive Approach to Bias Mitigation in AI Systems

As artificial intelligence (AI) systems become increasingly embedded in the fabric of modern society, their influence on governmental, commercial, and social decision-making grows ever more profound. The promise of AI lies in its ability to process vast amounts of data and deliver insights or automate decisions at a scale and speed unattainable by humans alone. However, this promise is shadowed by the risk of unwanted bias—systematic and unfair discrimination that can be inadvertently encoded into AI models, leading to inequitable outcomes. Recognizing this, the international standard ISO/IEC TR 24027 was developed to provide organizations with a robust framework for identifying, assessing, and mitigating bias throughout the AI system lifecycle.

 

Understanding the Scope and Purpose of ISO/IEC TR 24027

ISO/IEC TR 24027 is a technical report that addresses the multifaceted nature of bias in AI systems. It offers a structured approach for organizations to proactively manage bias-related vulnerabilities, ensuring that AI-aided decision-making remains equitable, trustworthy, and aligned with ethical standards. The standard is particularly relevant in 2025, as regulatory landscapes such as the EU AI Act are coming into force, demanding greater accountability and transparency from AI developers and deployers.

 

 

The report delineates three primary categories of bias that can affect AI systems: human cognitive bias, data bias, and engineering bias. Each of these can manifest at different stages of the AI lifecycle, from initial design and data collection to model training, deployment, and ongoing monitoring.

 

Types of Bias in AI: A Deeper Dive

 

Human Cognitive Bias:

Human cognitive biases are deeply ingrained mental shortcuts or tendencies that can influence the way AI systems are designed and developed. For example, confirmation bias may lead data scientists to favor information that supports their pre-existing beliefs, while automation bias can result in over-reliance on AI outputs, even when they are flawed. These biases can subtly shape the selection of features, the framing of problems, and the interpretation of results, embedding human prejudices into ostensibly objective algorithms.

 

Data Bias:

Data bias arises when the datasets used to train AI models are not representative of the real-world populations or scenarios the system will encounter. This can occur due to underrepresentation of certain groups, historical inequities, or flawed sampling methods. For instance, if a facial recognition system is trained predominantly on images of lighter-skinned individuals, it may perform poorly on people with darker skin tones—a phenomenon well-documented in recent years and highlighted by organizations such as the National Institute of Standards and Technology (NIST).

 

Engineering Bias:

Engineering bias refers to the technical decisions made during system development that can inadvertently favor certain outcomes. Choices about which features to include, how to tune algorithms, or how to define success metrics can all introduce bias. For example, optimizing a hiring algorithm solely for efficiency might overlook fairness considerations, leading to discriminatory outcomes.

 

Identifying and Measuring Bias: Key Metrics

ISO/IEC TR 24027 emphasizes the importance of rigorous, quantitative assessment of bias using well-established metrics. These metrics enable organizations to evaluate how their AI models perform across different demographic groups and to detect patterns of unequal treatment. Some of the most widely used metrics include:

 

Demographic Parity:

This metric assesses whether a decision-making system is equally likely to assign a positive outcome—such as loan approval or job selection—to individuals from different groups. Achieving demographic parity means that the probability of a favorable decision is independent of sensitive attributes like race, gender, or age.

 

Equalized Odds:

Equalized odds require that a classifier’s false positive and false negative rates are equal across different groups. This ensures that no group is disproportionately burdened by errors, promoting equal opportunity in outcomes. The concept is particularly relevant in high-stakes domains such as criminal justice or healthcare, where unequal error rates can have serious consequences.

 

Thematic Fairness (Counterfactual Fairness):

Thematic fairness examines whether an AI system would have made a different decision if an individual’s sensitive attributes were changed, holding all other factors constant. This approach helps to uncover hidden biases that may not be apparent through aggregate statistics alone.

Recent advancements in AI fairness research, as reported by Nature, have led to the development of even more nuanced metrics and auditing tools, enabling organizations to detect subtle forms of bias that traditional methods might miss.

 

Mitigating Bias: A Lifecycle Approach

Bias mitigation is not a one-off task but an ongoing commitment that spans the entire AI lifecycle. ISO/IEC TR 24027 advocates for a holistic, lifecycle-based approach, integrating bias assessment and mitigation into every phase of system development and operation.

 

Design and Data Collection:

At the outset, organizations should establish clear guidelines for data collection, ensuring that datasets are diverse, representative, and free from historical prejudices. This may involve oversampling underrepresented groups, removing sensitive attributes, or employing synthetic data generation techniques. The NIST Risk Management Framework (RMF) provides valuable guidance on risk assessment and control implementation during this phase.

 

Model Development and Testing:

During model development, teams should employ fairness-aware algorithms and conduct rigorous testing using the metrics described above. Techniques such as adversarial debiasing, reweighting, and post-processing adjustments can help to reduce bias in model outputs. It is also essential to document all design decisions and their rationale, fostering transparency and accountability.

 

Deployment and Monitoring:

Once deployed, AI systems must be continuously monitored for signs of emerging bias. This requires the establishment of robust auditing processes, regular performance evaluations, and mechanisms for stakeholder feedback. As highlighted in Nemko’s insights on transparency in AI, transparent communication about system limitations and ongoing bias mitigation efforts is crucial for maintaining public trust.

 

Governance and Accountability:

Effective bias mitigation also depends on strong governance structures. Organizations should appoint dedicated teams or officers responsible for AI ethics and compliance, establish clear escalation procedures for bias-related incidents, and ensure that all stakeholders are trained in responsible AI practices. The AI Maturity & Compliance Readiness Webinar offers practical advice for organizations seeking to enhance their AI governance frameworks.

 

The Regulatory and Business Imperative

The imperative to address AI bias is not merely ethical—it is increasingly a matter of regulatory compliance and business competitiveness. In 2025, the regulatory environment is evolving rapidly, with new laws and standards emerging worldwide. The European Union’s AI Act, for example, imposes strict requirements on high-risk AI systems, including mandatory bias assessments and transparency obligations. Non-compliance can result in significant financial penalties and reputational damage.

Moreover, organizations that proactively address bias are better positioned to unlock the full potential of AI. Fair and trustworthy AI systems can enhance customer satisfaction, reduce legal risks, and open new markets. As noted by the World Economic Forum, organizations that lead in AI ethics are more likely to attract top talent, foster innovation, and build lasting stakeholder trust.

 

The Path Forward: Building Equitable AI Systems

 

 

ISO/IEC TR 24027 provides a comprehensive roadmap for organizations seeking to build equitable, effective, and socially responsible AI systems. By systematically identifying, measuring, and mitigating bias, organizations can ensure that their AI solutions serve the interests of all stakeholders, not just a privileged few.

The journey toward bias-free AI is complex and ongoing. It demands a commitment to continuous improvement, cross-disciplinary collaboration, and transparent engagement with regulators, customers, and the broader public. As AI continues to shape the future of business and society, standards like ISO/IEC TR 24027 will play a pivotal role in guiding organizations toward responsible innovation.

For organizations looking to strengthen their AI assurance capabilities, Nemko’s insights on AI assurance offer practical strategies and case studies. By embracing the lifecycle approach recommended by ISO/IEC TR 24027, businesses can not only comply with emerging regulations but also set new benchmarks for fairness, effectiveness, and social progress in the age of AI.