Regulating the algorithm: Employment law risks in the age of AI
AI now sits inside everyday HR and recruitment workflows. It delivers speed, consistency, and lower costs. It also creates new legal exposure if left unchecked. The question for HR and legal teams: how do you regulate the algorithm without losing the benefits?
Discrimination and bias: the hidden risk in automated decisions
AI learns from historical data. If that data reflects past inequality, the tool can repeat or even amplify it. In recruitment and promotion, that can lead to unlawful discrimination under the Equality Act 2010, and employers remain liable for outcomes-tool or no tool.
- Audit AI tools for bias before and after deployment
- Understand how decisions are generated (inputs, features, thresholds)
- Keep human oversight central to every high-impact decision
Transparency and explainability: can you justify AI-driven decisions?
Many AI systems are hard to interpret, while processing large volumes of personal data. That creates privacy, fairness, and accountability issues-especially for recruitment, performance ratings, and disciplinary outcomes. Employees have a right to know how decisions affecting them were made.
- Provide clear privacy notices describing AI use and data sources
- Explain the core logic behind automated decision-making in plain language
- Offer a route to human review where decisions have significant effects (UK GDPR)
If an employee challenges an outcome, be ready to show how the AI informed the decision and why it was fair. Relying on opaque outputs you can't explain undermines compliance and trust. For guidance, see the ICO's resources on AI and data protection here.
Fairness in disciplinary and performance processes
Even if AI flags misconduct or performance concerns, the process must still meet the ACAS Code of Practice standards: fairness, transparency, and the right to respond. Treat AI as an assistant-not an arbiter.
- Ensure human decision-makers review AI-generated evidence
- Allow employees to challenge or contextualise findings
- Avoid solely automated decisions, particularly where outcomes are significant
Skipping a fair process can render a dismissal unfair, regardless of the technology involved. Review the ACAS Code of Practice here.
Managing employee use of generative AI
Set a clear, accessible policy on generative AI. Define approved tools, acceptable use cases, and who can use what, where, and when. Make it easy for teams to do the right thing without guessing.
- Prohibit inputting confidential or proprietary data into public tools
- Clarify monitoring, data security, and handling of personal data
- Require human review of AI outputs for accuracy, bias, and compliance
- Document limits of AI tools, including potential inaccuracies or hallucinations
The regulatory picture
The UK has not introduced a standalone AI law, but expectations from regulators are rising. The ICO has issued guidance, and tribunals are beginning to see AI-related disputes. Employers should keep governance frameworks current as case law and guidance develop.
Practical steps for employers
- Due diligence: assess vendors' training data, performance metrics, and explainability
- Impact assessments: run DPIAs and AI impact assessments before deployment
- Bias and accuracy: audit models regularly; monitor adverse impact across demographics
- Human-in-the-loop: require human oversight for any decision with significant effects
- Policies: update recruitment, monitoring, and data protection policies to reflect AI use
- Training: upskill HR, managers, and legal teams on risks, safeguards, and escalation paths
- Recordkeeping: document rationale, data sources, thresholds, and human reviews
- Vendor controls: bake audit rights, transparency, and remediation into contracts
AI can support better, faster decisions-but it also raises the stakes. Teams that build consistent governance, bias controls, and clear accountability will protect employees and the business while keeping the benefits of automation.
Further practical guidance for teams: AI for Human Resources and AI for Legal.
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