Q&A and Examples: AI in the Workplace – Your Most Important Legal Questions Answered (UK 2025)
By Natalie Popova, Legal Consultant | Express Law Solutions
Disclaimer: This article is for general information only and does not constitute legal advice. For specific guidance, contact Express Law Solutions.
Q&A: AI in the Workplace – Your Most Important Legal Questions Answered (UK 2025)
Is it legal for employers in the UK to use AI in hiring and HR decisions?
Yes – but only under strict conditions.
Employers must comply with the Equality Act 2010, UK GDPR, and the Employment Rights Act 1996.
AI cannot replace human judgment in decisions that have a “legal or similarly significant effect” (UK GDPR, Art. 22).
Hiring, dismissal, redundancy, or promotion cannot be fully automated.
Human oversight must always be present.
Can an employer rely solely on AI recommendations to reject a candidate?
No.
A fully automated rejection may breach:
- UK GDPR Article 22
- Equality Act 2010 (if the AI’s decision is discriminatory)
A human must meaningfully review, approve, and be accountable for the final decision.
If an AI system discriminates (e.g., ranks women or older workers lower), who is legally responsible the employer or the vendor?
Always the employer.
Vendors can be contractually liable, but legally, under the Equality Act and UK GDPR, the employer is responsible for discriminatory outcomes produced by the tools they deploy.
The employer may later sue the vendor, but employees can only sue the employer.
Does AI-powered productivity monitoring violate workers’ privacy?
It can, if not implemented correctly.
Monitoring must be:
- Necessary
- Proportionate
- Transparent
- Conducted with a lawful basis
Excessive monitoring (keystrokes, webcam tracking, message analysis) can breach:
- UK GDPR – Data minimisation & transparency
- Human Rights Act 1998 – Right to privacy
- ICO Employment Monitoring Guidance (2023–2024)
Are employers allowed to dismiss employees based on AI performance scores?
Not directly.
A dismissal based purely on algorithmic output is likely unfair under the Employment Rights Act 1996.
The employer must:
- Independently verify the concerns
- Ensure a fair procedure
- Provide clear reasoning
- Allow the employee to challenge the data
AI alone does not satisfy the Burchell test (“reasonable belief”).
Do employers need to conduct audits or assessments before using AI tools?
Yes – legally required.
Before deploying workplace AI, employers must conduct:
- DPIA (Data Protection Impact Assessment) – required under UK GDPR.
- EIA (Equality Impact Assessment) – required under Equality Act principles.
- Health & Safety stress risk assessments when AI affects workloads.
Failure to do so exposes the employer to fines and litigation.
Can employees refuse to have their data processed by AI tools?
Employees can object if:
- The processing lacks a lawful basis
- The monitoring is excessive
- Decisions are automated without human review
- The employer fails to provide transparency
Employees cannot refuse necessary processing (e.g., payroll), but can challenge unjustified AI monitoring.
What happens if an AI tool generates biased results that the employer did not anticipate?
The law is clear:
“Lack of knowledge” is not a defence.
Under Essop v Home Office (2017), indirect discrimination applies even when the exact cause is unknown.
Thus, employers must regularly test and audit AI for bias.
Does AI-generated content belong to the employer or the employee?
Under Copyright, Designs and Patents Act 1988, the employer generally owns content created:
- In the course of employment
- Using employer-authorised tools
- For business purposes
BUT: If employees use external AI tools, ownership may be unclear and confidentiality may be breached.
Employers need clear AI Usage Policies.
Are unions entitled to be consulted when AI is introduced in the workplace?
Yes – if AI significantly affects:
- Job roles
- Productivity expectations
- Workload
- Shift allocation
- Redundancy selection
Failure to consult may breach TULRCA 1992, s. 188, resulting in Protective Awards up to 90 days’ pay per employee.
Is it risky for employees to upload work documents into AI tools (ChatGPT, Gemini, etc.)?
Yes – extremely.
This can result in:
- Data breaches
- Confidentiality violations
- Loss of IP rights
Employers must prohibit uploading sensitive documents unless using enterprise-grade, privacy-compliant systems.
What are the biggest legal mistakes UK employers make with AI in 2024–2025?
The most common errors are:
- Using AI to manage staff with no human oversight
- Automating hiring decisions
- Conducting excessive monitoring
- Using AI without DPIAs
- Allowing staff to upload confidential files to external tools
- Relying on biased historical data
- Failing to update privacy notices and contracts
- Not training managers on AI compliance
- Assuming “vendor compliance” equals employer compliance
- Not consulting unions where required
What is the #1 legal rule employers must remember about AI?
Automation does not remove responsibility, it increases it.
If AI makes a mistake, the employer is fully accountable.
How can employers reduce legal risk when using AI in 2025?
Key steps include:
- Implementing AI governance frameworks
- Ensuring transparency with employees
- Maintaining human review in high-impact decisions
- Conducting bias and privacy assessments
- Creating internal AI usage policies
- Reviewing vendor contracts
- Training HR and managers
- Regularly auditing AI systems
Will the UK introduce an AI Act like the EU?
A dedicated AI Act is under discussion.
However, even without one, existing UK laws already impose strict duties.
The trend for 2025–2026 is clear:
- More enforcement
- More audits
- Higher expectations of transparency and fairness
Real-World Cases & Practical Examples (UK/EU/US): AI at Work
Amazon – Algorithmic Dismissals (USA & UK
Problem: An automated productivity-scoring system triggered warnings and dismissals without human review.
What happened:
• In Amazon warehouses, algorithms generated individual “productivity scores.”
• These scores automatically sent warnings and, in some cases, termination notices.
• Employees were dismissed without any meaningful human assessment of context, disability, or operational issues.
Legal risk:
• Potential breach of fair-process requirements under the Employment Rights Act 1996 (unfair dismissal).
• Possible infringement of UK GDPR Article 22, which restricts solely automated decision-making with significant effects.
Practical lesson:
Employers must ensure genuine and substantive human oversight, not a box-ticking “review” of AI decisions.
Uber – Algorithmic Management & Worker Status (UK Supreme Court, 2021)
Problem:
Work allocations, pricing, and deactivations were controlled entirely by algorithmic systems.
What the Court decided:
In Uber BV v Aslam, the UK Supreme Court held that:
• Uber’s algorithmic control over drivers amounted to employer-level control.
• Drivers were therefore “workers” (not independent contractors) with legal protections.
Legal effect:
• When AI exercises control over work patterns, performance ratings or discipline → the company carries full employer obligations.
• Includes rights to minimum wage, holiday pay, and protection from unfair treatment.
Practical lesson:
AI-driven management tools essentially operate as supervisors — and this triggers the same duties as traditional human management.
HireVue – Biometric AI and Discriminatory Candidate Screening (USA)
Problem:
An AI hiring tool analysed facial expressions, voice patterns and micro-movements to score candidates.
Outcome:
• Class-action complaints and an investigation by the Electronic Privacy Information Center (EPIC).
• Claims included discrimination against individuals with neutral facial expressions or disabilities affecting speech or expression.
If this occurred in the UK:
• Likely breach of the Equality Act 2010 (age, race, disability).
• Possible unlawful processing of biometric data under UK GDPR — a high-risk category requiring explicit safeguards.
Practical lesson:
AI using facial or behavioural analytics is extremely high risk and often produces discriminatory outcomes.
General Motors – AI Shift Allocation Bias (USA)
Problem:
An AI scheduling system assigned shifts based on “efficiency metrics.”
What happened:
• The model prioritised younger employees, who statistically took fewer sick days.
• Older workers were systematically given fewer shifts — a clear pattern of indirect age discrimination.
Practical lesson:
AI that mirrors historic data often reproduces historic discrimination.
Netherlands – Government Anti-Fraud Algorithm Declared Unlawful (SyRI Case, 2020)
Problem:
An AI system intended to detect welfare fraud disproportionately flagged individuals from poorer, migrant-dense neighbourhoods.
Result:
• The court banned the system.
• Findings: systemic discrimination, disproportionality, and violations of GDPR.
Lesson for UK employers:
Risk-profiling systems built on postcode or socio-economic data may breach GDPR and the Equality Act.
UK Local Councils – Faulty AI in Welfare Assessments (2023)
Problem:
Councils used AI to evaluate social-benefit applications.
What happened:
• The system wrongly rejected applications from people with disabilities.
• Several councils suspended the tool after legal threats from advocacy groups.
Legal assessment:
• Likely disability discrimination under the Equality Act 2010.
• Potential breach of the GDPR requirement for transparency and the “right to explanation.”
HR Chatbots Leaking Sensitive Employee Data (UK/EU, 2023–2024)
Problem:
HR teams used generative AI tools such as ChatGPT for:
• employee letters,
• disciplinary summaries,
• payroll drafting.
What went wrong:
They entered personal details including:
• employee names,
• contract numbers,
• health information.
This data was then processed and stored on external servers outside the employer’s control.
Legal consequences:
• GDPR breaches → investigations and fines by the ICO.
• Breach of confidentiality obligations under employment contracts and NDAs.
Lesson:
AI tools must never be used to process sensitive HR data without strong controls, restricted environments, and specialised policies.
Sector-Specific Examples (Short, High-Impact)
Healthcare – AI Misdiagnosis
Healthcare – AI Misdiagnosis in Minority Patients
AI diagnostic tools produced disproportionately inaccurate results for patients from ethnic minority backgrounds.
→ Legal risk: potential breach of the Equality Act 2010 and exposure to claims of professional negligence.
Banking – Biased AI Fraud Detection
Automated fraud-detection systems disproportionately blocked payment cards belonging to members of certain ethnic groups.
→ Legal risk: indirect racial discrimination under the Equality Act 2010 and potential FCA compliance breaches.
Real Estate – AI Rental Screening Bias
Automated tenant-screening tools rejected applicants solely based on postcode, resulting in disadvantaged outcomes for specific demographic groups.
→ Legal risk: unlawful indirect discrimination in the UK, as postcode-based criteria can act as a proxy for race or socioeconomic status.
Three UK-Centric Hypothetical Examples (Useful for Your Article)
Example 1 — The “Perfect Candidate” Ranking System
A London-based technology company introduces an AI tool to score and rank job applicants.
The system downgrades candidates who:
- have maternity-related career breaks,
- are over the age of 45,
- were educated outside the UK.
Legal Impact
This creates immediate exposure under the Equality Act 2010, amounting to potential discrimination on the grounds of sex, age, and race/ethnicity.
The result may include:
- Significant compensation awards for unlawful discrimination
- Regulatory scrutiny
- Serious reputational damage to the employer
Even if the bias comes from the algorithm, the employer remains legally accountable.
Example 2: AI Productivity Monitoring
A company implements AI-driven productivity tracking, including keystroke logging and real-time activity analysis.
An employee with a recognised medical condition (e.g., arthritis) records fewer keystrokes and receives lower performance scores.
Legal Consequences
This scenario triggers multiple legal vulnerabilities:
→ Disability discrimination under the Equality Act 2010
→ Potential breach of GDPR and the Data Protection Act 2018 due to disproportionate and intrusive monitoring
→ Failure to make reasonable adjustments for disabled employees
Employers cannot rely on AI metrics that disadvantage workers with protected characteristics and must ensure all monitoring is necessary, proportionate, and justified.
Example 3: Automating Redundancy Selection
An employer deploys an AI system to “recommend” which employees should be selected for redundancy.
The algorithm prioritises cost-saving and productivity metrics, and as a result it identifies employees who:
- work part-time,
- have recently taken maternity leave,
- have higher absence levels due to illness.
Legal Risk
This creates a significant danger of indirect discrimination under the Equality Act 2010, particularly on the grounds of sex, pregnancy/maternity, and disability as well as a high likelihood of unfair dismissal under the Employment Rights Act 1996.
Even though the decision is made by software, the employer remains fully liable. AI cannot justify discriminatory criteria.
This Article is related to Legal Cases > AI at Work: Legal Risks Employers Can’t Afford to Ignore in 2025
Authoritative Sources & References (UK/EU/US) – AI at Work
UK Law & Regulatory Sources
Legislation
- UK GDPR & Data Protection Act 2018 – Official UK legislation: https://www.legislation.gov.uk/ukpga/2018/12/contents
- Equality Act 2010 – https://www.legislation.gov.uk/ukpga/2010/15/contents
- Employment Rights Act 1996 – https://www.legislation.gov.uk/ukpga/1996/18/contents
- Human Rights Act 1998 – https://www.legislation.gov.uk/ukpga/1998/42/contents
- UK ICO Guidance on AI & Data Protection (2023–2024) – Official Information Commissioner’s Office: https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/
UK Court Cases
- Uber BV v Aslam [2021] UKSC 5 (UK Supreme Court) – Full judgment: https://www.supremecourt.uk/cases/uksc-2019-0029.html
- R (Bridges) v South Wales Police [2020] EWCA Civ 1058 (Facial recognition unlawful) – https://www.judiciary.uk/wp-content/uploads/2020/08/Bridges-judgment.pdf
- Various ICO enforcement actions related to AI, monitoring, and workplace data – https://ico.org.uk/action-weve-taken/enforcement/
EU & International Cases Relevant to AI
Key EU Cases & Findings
- SyRI Case – District Court of The Hague (2020) (Netherlands) – Decision declaring the government’s AI anti-fraud system discriminatory – English summary via HRW: https://www.hrw.org/news/2020/02/05/netherlands-court-blocks-digital-welfare-surveillance-system
- European Data Protection Board (EDPB) Guidelines on AI and data processing – https://edpb.europa.eu
United States Cases (Very Relevant to AI Practice Globally)
Algorithmic Hiring & Discrimination
- HireVue Investigation (EPIC, 2019–2021), Led to the company discontinuing facial analysis. – https://epic.org/documents/hirevue/
- Amazon AI Recruiting Tool Bias (Reported by Reuters, 2018), – Amazon scrapped an AI hiring tool that was biased against women – https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G
AI Control in Gig Work
- US litigation & academic analysis on algorithmic management (esp. Uber, DoorDash) – NYU Stern Center for Business & Human Rights: https://bhr.stern.nyu.edu
- Amazon algorithmic productivity management – Investigative reporting by The Verge, Vice, The Guardian: – Example: https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-termination-productivity-score-algorithm
Academic & Professional Sources
Books & Reports
- Harvard Business Review – AI & Workforce Management – https://hbr.org
- CIPD Report: “People Management and AI” (2023) – https://www.cipd.org/uk/knowledge/reports/
- Stanford AI Index Report (2024) – https://aiindex.stanford.edu
- Oxford Internet Institute – Algorithmic Management Research – https://www.oii.ox.ac.uk
Professional Guidance
- ACAS Guidance on Employee Monitoring (2023) – https://www.acas.org.uk
- UK Government White Paper: “A Pro-Innovation Approach to AI Regulation” (2023) – https://www.gov.uk/government/publications/ai-regulation-white-paper
Case Law & Sources Related to Workplace Discrimination & Automated Decisions
- Even if these cases are not explicitly about AI, they remain legally relevant and are frequently cited in modern AI-related analyses.
- Indirect Discrimination Examples – Homer v Chief Constable of West Yorkshire Police [2012] UKSC 15 and – Essop v Home Office [2017] UKSC 27
- GDPR Automated Decision-Making Interpretation – Articles 22–23 UK GDPR (automated decisions & profiling).
Suggested Citation Style (for your article)
Legal Case: Uber BV v Aslam [2021] UKSC 5. – Available at: UK Supreme Court website.
Legislation
Equality Act 2010, s.13–19. – Available at legislation.gov.uk.
Regulator guidance: ICO (2023). Guidance on Artificial Intelligence and Data Protection.
For more comprehensive insights, explore our Legal Cases page and review the applicable UK legal framework.
Disclosure Notice: All names and identifying details in the following case studies have been changed to protect client confidentiality. These examples are based on real scenarios, but any resemblance to actual persons or entities is purely coincidental.
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