ISO/IEC 24027 — Bias in AI Systems and AI-Assisted Decision-Making

This topic is part of the SG Systems Global regulatory & operations glossary.

Updated November 2025 • ISO/IEC 23894, ISO/IEC TR 24028, ISO/IEC 22989 • Governance, Quality, IT, Manufacturing, Compliance

ISO/IEC 24027 provides guidance on understanding, identifying, evaluating and mitigating bias in AI systems and AI-assisted decision-making. It does not eliminate bias—no standard can—but it forces organisations to treat bias as a structured, documented risk rather than an afterthought. For regulated industries such as pharmaceuticals, medical devices, food and beverage, cosmetics and chemicals, this is critical. Bias in AI can translate into biased sampling, skewed quality decisions, inconsistent supplier oversight, or inequitable treatment of patients and products. ISO/IEC 24027 gives governance, quality and technical teams a common framework and vocabulary for dealing with that risk.

“Bias in AI is inevitable; unmanaged bias is unacceptable. ISO/IEC 24027 is about moving from denial to control.”

1) Purpose & Scope of ISO/IEC 24027

ISO/IEC 24027 exists to help organisations work systematically with bias in AI. Its scope includes bias in data, bias in models and algorithms, and bias arising from how AI outputs are used by humans in decision-making. Rather than positioning bias solely as an ethical concern, the standard treats bias as a technical and governance issue that can be analysed, discussed and mitigated. It is strongly aligned with risk-based thinking in ISO/IEC 23894, where bias becomes one of the risk factors influencing harm likelihood and impact. In regulated manufacturing, this means that AI projects are not allowed to proceed on the assumption that “if the accuracy is high, we are safe.” Instead, teams must understand who or what might be disadvantaged, misclassified, over-sampled or under-sampled by the AI system, and what that means for product quality, safety, compliance and equity.

2) Relationship to 23894, 24028, 23053 & 22989

ISO/IEC 24027 sits alongside other AI standards rather than replacing them. ISO/IEC 23894 defines risk management for AI, and bias is one key risk driver feeding into that process. ISO/IEC TR 24028 treats fairness and non-discrimination as part of AI trustworthiness, with ISO/IEC 24027 providing more detailed guidance on how to reason about bias in practice. ISO/IEC 23053 defines the AI system lifecycle; bias considerations from 24027 must appear at multiple lifecycle stages—concept, data, modelling, validation, deployment and monitoring. Terminology from ISO/IEC 22989 ensures that teams have a shared vocabulary for datasets, models, stakeholders and decision types when they discuss bias. Together these standards encourage organisations to treat bias not as a siloed “ethics” project but as an integrated dimension of AI design, risk assessment and lifecycle management.

3) Types & Sources of Bias Considered in ISO/IEC 24027

ISO/IEC 24027 identifies multiple sources and types of bias that can affect AI systems. These include data bias (unrepresentative, skewed or systematically missing data), measurement bias (systems that encode errors or variability differently across groups or conditions), sampling bias (who or what is included in data collection), algorithmic bias (how models and training objectives amplify certain patterns) and user or context bias (how AI outputs are interpreted, trusted or overridden by humans). In regulated operations, these can map onto process realities: certain shifts, sites, equipment or suppliers might be over-represented; certain defect types or deviations might be under-reported; manual sampling patterns might inadvertently create biased datasets. ISO/IEC 24027 does not attempt to exhaustively classify all bias types; instead it gives organisations a structured way to identify biases relevant to each AI use case and trace them from cause to impact.

4) Data Bias, Representativeness & Dataset Design

Data is where bias often starts. ISO/IEC 24027 emphasises that organisations should examine data sources, collection processes and labelling practices to identify where bias may be introduced. This includes analysing whether all relevant segments (suppliers, shifts, lines, patient groups, product variants, process states) are adequately represented, and whether historical data embodies past inequities or process errors. For instance, in quality control, historical records may under-record certain defect types if inspectors were not trained to recognise them or if economic incentives discouraged reporting. In a clinical support context, historical treatment patterns may reflect biases against certain populations. Under ISO/IEC 24027, these realities must be surfaced and documented before data is used to train models. Organisations are encouraged to link these analyses to data governance and integrity controls described elsewhere in their QMS and to the data-focused lifecycle stages in ISO/IEC 23053.

5) Model, Algorithmic & Systemic Bias

Even if data were perfectly balanced, model design choices can still introduce or amplify bias. ISO/IEC 24027 explains that training objectives, loss functions, regularisation strategies, feature selections and threshold choices can favour some groups, conditions or outcomes over others. For example, optimising purely for overall accuracy may hide systematic misclassification of rare but critical events, such as low-frequency defects in sterile manufacturing or rare adverse events in pharmacovigilance. Algorithmic bias can also appear when proxy variables correlate with sensitive attributes (geography, language patterns, purchasing patterns) even if those attributes are not explicitly present. Systemic bias emerges when these algorithmic effects interact with organisational processes, incentives and constraints. The standard encourages teams to review model design decisions explicitly for bias impact and to include this review in model documentation and model-risk assessment under 23894, rather than treating bias as a post-hoc concern addressed only at the metric level.

6) Context, Use-Case & Human Decision Bias

ISO/IEC 24027 also recognises that bias does not live only inside data and models; it also arises from how humans interact with AI systems. Operators, supervisors, quality reviewers and clinicians may trust AI recommendations differently depending on their training, experience and workload. Some may over-rely on AI (“automation bias”), accepting outputs even when they conflict with process knowledge; others may under-rely on AI, discarding useful alerts. Decision policies may apply AI recommendations unevenly across sites or shifts. The standard therefore calls for analysis of the broader decision-making context: who uses AI, under what conditions, with what authority, and with which training? Provisions for human oversight in ISO/IEC 22989 and lifecycle integration in 23053 should be used to design oversight mechanisms that reduce, rather than reinforce, human bias. In practice, this often means making AI behaviour more transparent, providing clear guidance in SOPs on how to use AI outputs, and monitoring how human responses differ across users or sites.

7) Metrics & Evaluation Frameworks for Bias

ISO/IEC 24027 does not prescribe a single “correct” bias metric, because appropriate metrics depend on the use case and regulatory context. Instead it describes families of metrics—such as error-rate parity, calibration parity, coverage parity and outcome parity—and encourages organisations to select metrics whose meaning can be explained to both technical and non-technical stakeholders. In manufacturing and GxP contexts, this might mean tracking false negative rates for critical defects across sites, comparing sampling recommendations across product families, or analysing how often AI-driven risk scores trigger additional testing for different suppliers. The standard urges teams to integrate bias metrics into broader validation and performance testing rather than running them as informal side analyses. Evaluation should be documented, repeatable and linked to acceptance criteria within validation plans and risk assessments under 23894. Where bias trade-offs are unavoidable, those trade-offs must be recorded and justified, not silently embedded into the system.

8) Mitigation Strategies & Controls

Mitigating bias is rarely about a single technique; ISO/IEC 24027 presents mitigation as a layered strategy. At the data level, organisations can improve representativeness, rebalance sampling, adjust labelling processes, or conduct targeted data collection to fill gaps. At the model level, they can use fairness-aware training objectives, constraint-based optimisation, threshold adjustments or post-processing methods that correct for detectable disparities. At the process level, they can implement procedural safeguards: dual review for high-impact decisions, human-in-command oversight where AI is advisory, and clear escalation paths for contested decisions. Mitigations should be proportionate to risk and documented in the same way as other risk controls in the quality system. The standard emphasises that mitigation is not a one-time event; bias can re-emerge as data, models and contexts evolve. Controls therefore need to be revisited during periodic review, in line with lifecycle expectations in 23053 and governance reviews under ISO/IEC 42001.

9) Documentation, Transparency & Explainability

ISO/IEC 24027 treats documentation as a primary defence against unmanaged bias. Organisations should maintain records of bias analyses, datasets used for bias evaluation, metrics selected, results obtained, trade-offs accepted and mitigations implemented. This documentation should be part of the AI system dossier alongside requirements, validation and monitoring records. For regulated industries, documentation requirements align with expectations for transparency and traceability under CSV, 21 CFR Part 11 and Annex 11. Explainability, as described in TR 24028, is closely related: users should be able to understand why AI outputs may differ across cases and how potential bias is controlled. Even if full interpretability is not possible, organisations should at least be able to explain what was done to detect, quantify and mitigate bias and how residual risk is managed.

10) Operational Monitoring for Bias & Drift

Bias is not static; it can appear, disappear or reverse as processes, populations and data flows change. ISO/IEC 24027 therefore expects organisations to monitor bias during operational use, not just during development. This can include periodic recalculation of bias metrics, targeted audits of AI-influenced decisions, or stratified analysis of outcomes across sites, products, shifts or groups. Monitoring should be integrated into the same operational controls that handle other performance and drift signals described in 23053. When monitoring reveals new or worsening bias, change-control processes should be triggered: retraining with new data, recalibration of thresholds, revision of SOPs, or—in extreme cases—suspension or retirement of the AI feature. Organisations are encouraged to treat bias incidents similarly to other quality incidents, with investigation, root-cause analysis and CAPA as appropriate.

11) Governance, Roles & Accountability

ISO/IEC 24027 reinforces the idea that responsibility for managing bias cannot be left solely with data scientists. Governance structures defined under ISO/IEC 42001, and broader IT-governance principles in standards such as ISO/IEC 38507, must explicitly assign bias-related responsibilities. This includes defining who is accountable for bias assessments, who approves residual risk, who owns monitoring, and who leads remediation when issues are discovered. Quality and compliance teams are expected to review bias-related documentation as part of validation and change control; process owners need to confirm that mitigations are operationally realistic; legal and regulatory functions may need to evaluate whether bias profiles are compatible with anti-discrimination, data-protection and sector-specific regulations. In practice, organisations often embed bias considerations into existing AI risk committees or model-risk boards, ensuring that bias is visible whenever high-impact AI systems are discussed.

12) Impact on Regulated Manufacturing, Quality & Safety

In life-science and food manufacturing, biased AI can create subtle but serious risks. For example, an AI model might recommend additional sampling more often for certain lines or sites because of historical deviation patterns, reinforcing past reporting biases rather than underlying risk. A supplier-risk model might systematically rank some regions or supplier types as riskier based on historical documentation quality rather than actual performance. A predictive-maintenance model might under-prioritise certain equipment types because failure data is sparse. ISO/IEC 24027 encourages organisations to think through these scenarios and treat them as part of their overall risk landscape. Bias management then becomes part of routine quality oversight, not an isolated ethical concern. When combined with GxP expectations, MES controls, eBR/eMMR review and Deviation/NCR workflows, bias controls help ensure that AI does not quietly distort quality signals or regulatory evidence.

13) Integration with Risk Management & Lifecycle (23894 & 23053)

ISO/IEC 24027 is most effective when tightly integrated with the AI risk-management process of ISO/IEC 23894 and the lifecycle stages of ISO/IEC 23053. In risk assessments, bias should be treated as a specific hazard with potential impacts on individuals, products, processes, and regulatory outcomes. Control measures—data, model, process and governance mitigations—should be tracked as formal risk controls with owners and review dates. In lifecycle documentation, bias-related tasks and decision points should appear in concept, data, design, validation, deployment and monitoring phases, not only in a one-time “fairness review.” This integration ensures that bias is neither overlooked nor inflated: some AI use cases may carry minimal bias risk and require only basic checks; others, especially those affecting safety or equity, may demand extensive analysis, cross-functional review and supervision at governance level. ISO/IEC 24027 provides the content; 23894 and 23053 provide the process into which that content is embedded.

14) Vendor, Third-Party & Procurement Considerations

Many organisations will consume AI via third-party tools, platforms or embedded features in MES, LIMS, QMS and ERP systems. ISO/IEC 24027 recognises that outsourced AI does not remove responsibility for bias. Procurement processes and supplier assessments should therefore include questions about bias: what datasets were used, what bias analyses were performed, which metrics were applied, what mitigations exist, and how the vendor will notify customers about significant changes. Contracts may need to specify transparency obligations, access to evaluation data, and roles in handling bias-related incidents. When combined with AI-governance structures from ISO/IEC 42001, this helps ensure that vendor-supplied AI is subject to the same scrutiny as internal models. For highly regulated contexts, organisations may need to run their own bias assessments on vendor models using their own data and operational scenarios, rather than relying solely on vendor statements.

15) FAQ

Q1. Does ISO/IEC 24027 require us to remove all bias from AI systems?
No. The standard recognises that some degree of bias is unavoidable. Its goal is to ensure that organisations can identify, measure, explain and mitigate bias in a risk-based way. The emphasis is on transparency and control, not on promising bias-free AI.

Q2. How is ISO/IEC 24027 different from ISO/IEC TR 24028?
ISO/IEC TR 24028 addresses AI trustworthiness broadly—covering robustness, security, reliability, privacy and fairness. ISO/IEC 24027 focuses specifically on bias as a component of fairness and non-discrimination, giving more detailed guidance on sources, metrics and mitigation strategies.

Q3. Where in the lifecycle should we consider ISO/IEC 24027?
Bias considerations appear across the lifecycle: during concept and requirements (defining who could be impacted), data preparation (analysing representativeness), model development (designing for fairness), validation (evaluating bias metrics), deployment (documenting oversight) and monitoring (tracking how bias evolves). This aligns with the lifecycle model in ISO/IEC 23053.

Q4. Is ISO/IEC 24027 only relevant for consumer-facing AI?
No. Although consumer use cases are a clear area of concern, industrial and regulated contexts are equally affected. Biased sampling, supplier scoring, anomaly detection or maintenance prioritisation can all lead to skewed outcomes that affect quality, safety or compliance—even if no individual consumer sees the AI directly.

Q5. What is a practical first step to apply ISO/IEC 24027?
A pragmatic starting point is to review one existing or planned AI use case and identify where bias might arise in its data, model and decision context. Document these findings, select a small set of meaningful bias metrics, and integrate them into validation and monitoring. From there, you can templatise the approach and roll it out across other AI systems under your AI Management System.

Related Reading
• AI Governance & Risk: ISO/IEC 42001 | ISO/IEC 23894 | ISO/IEC TR 24028 | ISO/IEC 23053 | ISO/IEC 22989
• Quality & Systems: ISO 9001 | ISO 13485 | CSV | VMP
• Execution & Records: MES | eBR | eMMR | Deviation/NCR | CAPA