Bias

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AI Jargon

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AI Jargon


1. What is it?

2. What problem does it aim to address within governance and regulation?

3. Where does it typically appear in organisational practice?

4. What can go wrong if it is interpreted or applied incorrectly?

5. Who is accountable, and what oversight is required?

1. What is it?

Bias refers broadly to systematic distortions in AI outputs that result in unfair, unbalanced or discriminatory outcomes for certain individuals or groups. Bias can arise from multiple sources, including:

  • training data composition,

  • model design choices,

  • proxy variables,

  • deployment context and feedback loops.

There is no single “bias”; it is an umbrella term.

2. What problem does it aim to address within governance and regulation?

The term is used to highlight risks to fairness, equality and legitimacy in AI-driven decisions. Governance frameworks focus on bias because unchecked bias can:

  • harm individuals or groups,

  • violate legal or ethical norms,

  • undermine trust in automated systems,

  • expose organisations to regulatory and reputational risk.

The challenge is not acknowledging bias, but operationalising its management.

3. Where does it typically appear in organisational practice?

Bias appears:

  • in AI risk assessments and ethical reviews,

  • in model validation and testing discussions,

  • in regulatory, media or stakeholder scrutiny,

  • in internal debates about fairness and model acceptability.

It is often referenced in high-level terms, even when underlying causes differ substantially.

4. What can go wrong if it is interpreted or applied incorrectly?

If bias is treated as a single, generic issue, organisations may:

  • implement superficial mitigation measures,

  • overlook context-specific risks,

  • conflate legal discrimination with statistical imbalance,

  • declare systems “unbiased” without evidence.

The key risk is moral signalling without technical or governance substance.

5. Who is accountable, and what oversight is required?

Management is accountable for identifying, assessing and mitigating relevant forms of bias, rather than invoking the term abstractly. Boards and oversight bodies should ensure that:

  • bias risks are clearly specified and evidenced,

  • mitigation measures are proportionate and tested,

  • outcomes are monitored over time,

  • accountability for bias-related harm is clearly assigned.