Artificial Intelligence Policy Tracker
Voluntary guidance Governance and accountability United Kingdom Guidance

Use AI in ways that are fair and do not discriminate unlawfully

Download: JSON · MarkdownUnder UK AI regulation framework, Principle 3, Part 3

Source-linked · linked to the official source, not yet confirmed by a reviewer (a factual check against the official source, not a legal review or legal advice)
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No change recorded as of 26 Sep 2026 · official source last checked
On this page 9 sections

In brief

Use AI in ways that are fair and do not discriminate unlawfully is a voluntary measure under the UK AI regulation framework in the United Kingdom (Principle 3, Part 3). AI systems should not undermine legal rights, discriminate unfairly or create unfair market outcomes. The record carries 1 evidence example and mappings to 1 framework.

Jurisdiction
United Kingdom
Nature
Voluntary guidance
Source reference
Principle 3, Part 3
Who it binds
Provider / developer, Deployer / user organisation
Evidence examples
1
Framework mappings
NIST AI Risk Management Framework 1.0
Controls that meet it
3
Official source
Open official text
Verification
Source-linked

What does it require?

AI systems should not undermine legal rights, discriminate unfairly or create unfair market outcomes. The Equality Act 2010 and UK GDPR fairness principle make key parts of this binding.

Practical action

Test for disparate outcomes across protected characteristics before and after deployment.

Who does it apply to?

All AI uses affecting people; binding under equality and data-protection law.

Which controls meet this duty?

Satisfies: the control, operated properly, does the work the duty asks for. Supports: it contributes but the duty needs more. Each control page lists every other duty it serves, so work done once can be counted once.

  • satisfiesTechnical measureQuality or testing lead · at launch and on material change
    Accuracy, robustness, fairness and security testing

    Serves 15 recorded duties · evidence: Pre-release test report, Test plan and acceptance criteria, Release test sign-off

    Disparate-outcome testing across protected characteristics before and after deployment.

  • supportsProcessData governance lead · once per ai system
    Data governance and dataset documentation

    Serves 8 recorded duties · evidence: Dataset documentation sheet, Data quality and bias check report, Dataset approval for use

    Bias checks on training data.

  • supportsProcessAI system owner · once per ai system
    AI impact and fundamental-rights impact assessment

    Serves 11 recorded duties · evidence: AI impact assessment, Impact assessment approval, Impact assessment procedure and template

    Identifies groups at risk of unfair treatment.

Templates that cover this duty

Generated from the records. Free · no account · links emailed to your work address. This duty is cited in each file with its source reference and a link back here.

All 8 templates that cover it →

What evidence would a reviewer expect?

Evidence examples
EvidenceTypeNotes
Bias and fairness testing reportreport

Framework mappings

Original editorial crosswalks. They cite clause numbers only and reproduce no standard text; confidence reflects how direct the mapping is.

See every United Kingdom duty mapped this way →

Framework mappings table
FrameworkReferenceNoteConfidence
NIST AI RMF 1.0MEASURE 2.11Fairness and bias evaluation.medium

Frequently asked questions

Is this a legal requirement?

No. It is voluntary guidance under UK AI regulation framework; following it is expected or recommended rather than required by law.

Who must comply with this obligation?

All AI uses affecting people; binding under equality and data-protection law.

What evidence shows compliance?

A reviewer would expect: Bias and fairness testing report.

Which frameworks map to this obligation?

It is mapped to NIST AI Risk Management Framework 1.0 MEASURE 2.11. These are editorial crosswalks with a stated confidence, not official mappings.

Which controls satisfy this obligation?

Accuracy, robustness, fairness and security testing; Data governance and dataset documentation; AI impact and fundamental-rights impact assessment.

What should an organisation do in practice?

Test for disparate outcomes across protected characteristics before and after deployment.

Cite this record

AIPolicyTracker (2026). “Use AI in ways that are fair and do not discriminate unlawfully (UK AI regulation framework)” [AIPT-OBL-0074, verified 26 September 2026 by Bhaskar Bhatt]. https://aipolicytracker.org/id/AIPT-OBL-0074 (accessed 27 October 2026). Data licensed CC BY 4.0.

Permanent id AIPT-OBL-0074: it never changes or gets reused, and its address keeps working if this page moves.

Cite the official text alongside it: A pro-innovation approach to AI regulation, UK Government, Department for Science, Innovation and Technology, https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach.

BibTeX
@misc{aipolicytracker2026_useaiinwaysthatarefairan,
  author       = {{AIPolicyTracker}},
  title        = {{Use AI in ways that are fair and do not discriminate unlawfully (UK AI regulation framework)}},
  year         = {2026},
  howpublished = {\url{https://aipolicytracker.org/obligations/uk-principles-fairness}},
  url          = {https://aipolicytracker.org/obligations/uk-principles-fairness},
  urldate      = {2026-10-27},
  note         = {Data licensed CC BY 4.0. Accessed 27 October 2026}
}

Change history

No change to this record's tracked fields has been recorded since 15 October 2026, when field-level history began.

Similar obligations in other instruments

Disclaimer: informational only, not legal advice. Verify every claim against the linked official sources and consult a qualified lawyer before acting.