Fired by a Robot? The Legality of AI-Driven Performance Management and Dismissals in UK Employment Law

The Rise of AI in the Workplace and the Legal Landscape in 2025

Artificial Intelligence has moved from science fiction to your office reality. Every day, more companies adopt AI systems to monitor productivity, evaluate performance, and even recommend dismissals. The technology promises efficiency and consistency, but it also raises a fundamental question about employment law: can a robot fairly fire you?

The benefits seem obvious at first glance. AI can process vast amounts of data without fatigue, apply consistent criteria across all employees, and eliminate human bias from decision-making. However, the reality proves more complex than the marketing promises suggest. These systems can perpetuate existing biases, operate without transparency, and make decisions that affect livelihoods without adequate human oversight.

Current UK employment law doesn't specifically address AI in the workplace, but existing legal principles still apply. The Employment Rights Act 1996, Equality Act 2010, and Data Protection Act 2018 all impose obligations on employers, regardless of whether decisions are made by humans or algorithms. The courts expect the same standards of fairness and transparency whether your manager or a machine recommends your dismissal.

What makes this particularly relevant for business owners, HR professionals, and employees is the ongoing legislative discussions. Parliament is considering new regulations that could significantly change how AI can be used in employment decisions. The proposed Artificial Intelligence (Regulation and Employment Rights) Bill could introduce specific protections for workers subject to automated decision-making.

Are you prepared for a future where your performance review comes from an algorithm?

This article explores the legal tests that AI-driven HR decisions must pass to remain fair and lawful. You'll discover how existing employment law principles apply to algorithmic decision-making and learn practical steps to protect your rights or ensure compliance. Whether you're implementing AI systems or working under them, understanding these legal requirements is essential for navigating the changing workplace.

The stakes are high. Employment tribunal cases involving AI-driven decisions are beginning to emerge, and the outcomes will shape how these technologies can be used legally. As Litigated's analysis of recent employment law cases shows, tribunals are increasingly scrutinising automated decision-making processes and demanding clear evidence of fairness and transparency.

The Foundation: Key Principles of UK Employment Law Relevant to AI

UK employment law serves as the bedrock for protecting workers' rights while allowing businesses to operate effectively. This legal framework governs every aspect of the employment relationship, from recruitment and contracts to performance management and dismissal. Understanding these principles becomes crucial when AI enters the equation, as the law doesn't exempt algorithmic decision-making from its requirements.

The Employment Rights Act 1996 establishes fundamental rights for employees, including: • Protection against unfair dismissal after two years of continuous service • Requirements for fair reasons and fair procedures for dismissal • Protections that apply regardless of whether AI informs the decision

The Equality Act 2010 prohibits discrimination based on protected characteristics such as age, race, gender, disability, and religion. These protections remain in force even when algorithms make or influence employment decisions.

Data protection represents another cornerstone of employment law, particularly relevant to AI systems. The Data Protection Act 2018, incorporating UK GDPR requirements, gives employees rights over their personal data and protection against solely automated decision-making. When AI systems process employee information for performance management or dismissal decisions, employers must comply with strict data protection obligations, including transparency about how data is used and the logic behind automated decisions.

Employment status also matters significantly when AI is involved in HR decisions. Employees enjoy the strongest protections under employment law, including unfair dismissal rights and full equality protections. Workers receive some protections but fewer than employees, while self-employed individuals have minimal legal protections. The classification affects what rights you can enforce if an AI system makes an adverse decision about your work.

Continuous service remains a key concept that AI cannot override. The longer you work for an employer, the greater your protection against unfair treatment. This principle ensures that even if an AI system flags you for dismissal, your accumulated employment rights still apply. An employee with five years' service has stronger legal protection than someone who started last month, regardless of what an algorithm might recommend.

These foundational principles create a framework that any AI system must respect. The law doesn't care whether a human manager or sophisticated algorithm makes the decision; the same standards of fairness, transparency, and non-discrimination apply. This means that implementing AI in HR doesn't provide a shortcut around employment law obligations but rather requires additional care to ensure compliance.

When Algorithms Judge Performance: Navigating the Legal Minefield

AI systems are increasingly taking on roles that were traditionally the domain of human managers. These technologies can monitor keystrokes, track productivity metrics, analyse communication patterns, and even assess emotional states through voice analysis. While these capabilities offer new insights into employee performance, they also create complex legal challenges that employment law must address.

The legal implications of AI-driven performance management extend far beyond simple data collection. When algorithms assess your work performance, multiple areas of employment law come into play simultaneously. The intersection of data protection, equality, and employment rights creates a complex legal landscape that both employers and employees must navigate carefully.

Data Protection and Privacy

UK GDPR requirements become particularly stringent when AI systems process employee data for performance management. You have the right to know what personal data is being collected, how it's being processed, and the logic behind any automated decision-making. This transparency obligation means employers cannot simply deploy AI systems and hope employees won't notice or ask questions.

The principle of data minimisation requires that only necessary data is collected for specific, legitimate purposes. AI systems that hoover up vast amounts of employee data without clear justification may violate this principle. Employers must demonstrate that every piece of data collected serves a legitimate business purpose and contributes to fair performance assessment.

Automated decision-making provisions under UK GDPR provide additional protection. You have the right not to be subject to decisions based solely on automated processing that significantly affect you. This right becomes particularly important when AI systems recommend performance ratings, training requirements, or disciplinary actions. Employers must provide meaningful human oversight and the opportunity to challenge automated decisions.

The legal requirement for explicit consent or legitimate interest as a lawful basis for processing adds another layer of complexity. Simply stating in an employment contract that AI will be used for performance monitoring may not satisfy legal requirements. Employers must ensure they have proper lawful bases for all data processing activities and maintain detailed records of their compliance efforts.

Fairness and Bias

Algorithmic bias represents one of the most significant legal risks in AI-driven performance management. These systems can perpetuate and amplify existing discrimination, creating unfair outcomes that violate equality law. The Equality Act 2010 prohibits both direct and indirect discrimination, and these protections extend to algorithmic decision-making.

Direct discrimination occurs when an AI system explicitly uses protected characteristics to make decisions. More commonly, indirect discrimination happens when apparently neutral AI systems disproportionately impact certain groups. For example, an AI system that penalises employees for working non-standard hours might indirectly discriminate against women with caring responsibilities.

The legal concept of reasonable adjustments also applies to AI systems. Employers must consider whether their AI-driven performance management systems create barriers for disabled employees and make appropriate adjustments. This might involve modifying algorithms, providing alternative assessment methods, or ensuring human oversight for employees with disabilities.

Regular bias audits become legally prudent, if not mandatory, when using AI for performance management. Employers who can demonstrate proactive efforts to identify and eliminate bias are better positioned to defend against discrimination claims. These audits should examine both the training data used to develop AI systems and the outcomes they produce in practice.

Transparency and Explainability

The legal requirement for transparency in performance management becomes more challenging with AI systems. You have the right to understand how your performance is being assessed, what criteria are being used, and how decisions affecting you are reached. Opaque algorithms that cannot explain their reasoning create significant legal risks for employers.

Employment law has always required that performance management decisions be explicable and justified. This principle becomes more complex when AI systems use machine learning techniques that even their creators cannot fully explain. The legal requirement for transparency may push employers toward more interpretable AI systems or require them to provide additional human oversight and explanation.

The right to explanation, while not absolute under UK law, becomes practically important in employment contexts. If you face adverse consequences based on AI-driven performance assessment, you can reasonably expect your employer to explain how that decision was reached. Employers who cannot provide clear explanations may find their decisions challenged successfully at employment tribunals.

Documentation requirements also become more complex with AI systems. Employers must maintain records not only of decisions made but also of the algorithmic processes used to reach those decisions. This includes version control of AI systems, training data used, and any modifications made to algorithms over time. Such documentation becomes crucial evidence if decisions are later challenged.

The Robot as Decision-Maker: Applying Legal Tests for Fair Dismissal

When AI systems move beyond performance monitoring to actually influencing dismissal decisions, the legal stakes increase dramatically. The fundamental tests for fair dismissal under UK employment law remain unchanged, but their application to AI-driven decisions creates new complexities and challenges.

The legal framework for unfair dismissal requires employers to demonstrate both substantive and procedural fairness. This means having a fair reason for dismissal and following a fair process to reach that conclusion. AI systems can support these requirements but cannot replace the human judgement and oversight that employment law demands.

Substantive Fairness

A fair reason for dismissal must be established regardless of whether AI informs the decision. The Employment Rights Act 1996 recognises five potentially fair reasons for dismissal: 1. Capability 2. Conduct 3. Redundancy 4. Statutory requirements 5. Some other substantial reason

AI-generated performance data can provide evidence to support these reasons, but the underlying facts must still justify the dismissal.

Capability dismissals based on AI assessments face particular scrutiny. The system must accurately measure relevant performance indicators and account for factors beyond the employee's control. An AI system that flags an employee for poor performance without considering factors like inadequate training, faulty equipment, or unrealistic targets may not provide a fair basis for dismissal.

The legal requirement for consistency in dismissal decisions becomes more complex with AI systems. While algorithms can ensure consistent application of criteria, they may also perpetuate unfair standards or fail to account for individual circumstances. Employers must demonstrate that their AI systems apply fair and reasonable standards consistently across all employees.

Bias in AI systems can fundamentally undermine substantive fairness. If an algorithm is trained on historical data that reflects past discrimination, it may perpetuate those biases in dismissal recommendations. Employers must actively monitor for such bias and take corrective action to ensure fair outcomes.

Procedural Fairness

Following a fair procedure remains essential even when AI systems identify employees for potential dismissal. The ACAS Code of Practice on Disciplinary and Grievance Procedures still applies, requiring investigation, warnings, and opportunities for the employee to respond before dismissal.

AI systems can support procedural fairness by providing consistent documentation and tracking of performance issues over time. However, they cannot replace the human elements of investigation, consultation, and decision-making that procedural fairness requires. Employers must ensure that AI-generated evidence is properly investigated and verified before taking action.

The right to be accompanied during disciplinary proceedings becomes more important when AI systems are involved. Employees may need assistance understanding complex algorithmic evidence and challenging the validity of AI-generated assessments. Employers should be prepared to explain their AI systems clearly and provide opportunities for meaningful challenge.

Appeals processes must also account for AI involvement in dismissal decisions. Employees should have the right to challenge not only the decision itself but also the AI system's role in reaching that decision. This might include questioning the algorithm's design, the data used, and the human oversight provided.

The Role of Human Oversight

Meaningful human involvement remains legally essential in dismissal decisions, even when AI systems provide recommendations. The concept of "human in the loop" becomes particularly important, requiring that humans have the authority, competence, and information necessary to make genuinely independent decisions.

Human oversight must be more than rubber-stamping AI recommendations. Decision-makers need to understand the AI system's reasoning, consider individual circumstances, and have the authority to override algorithmic recommendations when appropriate. This requires training for HR professionals and managers on both the capabilities and limitations of AI systems.

The legal requirement for individualised assessment cannot be delegated entirely to AI systems. Each dismissal decision must consider the specific employee's circumstances, history, and potential for improvement. While AI can provide relevant data and insights, human judgement remains necessary to evaluate these factors fairly.

Quality assurance processes become legally important when AI influences dismissal decisions. Employers should implement regular reviews of AI-driven recommendations, tracking their accuracy and fairness over time. This ongoing monitoring helps ensure that human oversight remains effective and that AI systems continue to support rather than replace fair decision-making.

Addressing Algorithmic Discrimination in Dismissals

The risk of discrimination in AI-driven dismissal decisions represents one of the most serious legal challenges facing employers who use these technologies. Algorithmic discrimination can be subtle, systematic, and difficult to detect, making it particularly dangerous from a legal perspective. The Equality Act 2010 provides strong protections, but proving and preventing algorithmic discrimination requires new approaches and understanding.

Traditional discrimination often involves obvious differential treatment based on protected characteristics. Algorithmic discrimination typically operates more subtly, through biased training data, flawed assumptions built into algorithms, or seemingly neutral criteria that disproportionately impact protected groups. This subtlety makes it harder to detect but no less illegal under UK employment law.

Proving Discrimination

Establishing that an AI system has discriminated requires sophisticated analysis that goes beyond traditional discrimination cases. Statistical evidence becomes crucial, as patterns of algorithmic bias often only emerge when examining large datasets. You may need expert testimony to demonstrate how seemingly neutral algorithms produce discriminatory outcomes.

The legal burden of proof in discrimination cases typically shifts once a prima facie case is established. With algorithmic discrimination, this might involve showing statistical disparities in how AI systems treat different groups. Once such disparities are demonstrated, employers must provide non-discriminatory explanations for the outcomes.

Accessing evidence to prove algorithmic discrimination can be challenging. Unlike human decision-makers who can be questioned about their reasoning, AI systems require technical expertise to audit and understand. Employees may need to request detailed information about AI systems under data protection rights or seek expert assistance to analyse algorithmic decision-making.

The complexity of AI systems can make it difficult to establish causation between algorithmic processes and discriminatory outcomes. However, employment law doesn't require proof of intentional discrimination; disparate impact can be sufficient to establish liability. This means employers cannot hide behind algorithmic complexity to avoid responsibility for discriminatory outcomes.

Mitigating Discrimination Risks

"The most dangerous form of AI bias is the kind that appears neutral on the surface but systematically disadvantages protected groups. Employers have a positive duty to seek out and eliminate these biases."

Preventing algorithmic discrimination requires proactive measures throughout the AI system lifecycle. From initial design through ongoing operation, employers must actively work to identify and eliminate sources of bias. This process involves technical, legal, and organisational changes that go beyond traditional anti-discrimination measures.

Training data represents a critical vulnerability in AI systems. Historical employment data often reflects past discrimination, and AI systems trained on such data may perpetuate those biases. Employers must carefully curate training data, removing or correcting biased examples while ensuring diverse representation across all protected groups.

Diverse development teams can help identify potential sources of bias before AI systems are deployed. Teams that include members from different backgrounds and perspectives are more likely to spot problems that homogeneous teams might miss. This diversity should extend to technical developers, HR professionals, and legal advisors involved in AI system development.

Regular algorithmic audits represent a legal and practical necessity for employers using AI in dismissal decisions. These audits should examine both the technical functioning of AI systems and their real-world outcomes. Independent audits by external experts can provide additional credibility and objectivity to the process.

Legal Challenges and Case Studies

While specific case law on algorithmic discrimination in UK employment remains limited, existing discrimination precedents provide guidance on how courts might approach these issues. The principles established in traditional discrimination cases will likely extend to algorithmic decision-making, with additional complexity around technical evidence and causation.

International cases involving algorithmic discrimination offer insights into potential legal developments. Courts in other jurisdictions have found liability for discriminatory AI systems, establishing precedents that UK courts may follow. These cases emphasise the importance of transparency, accountability, and ongoing monitoring of AI systems.

The emerging regulatory landscape suggests increased scrutiny of AI systems in employment. The Information Commissioner's Office and Equality and Human Rights Commission have both indicated interest in algorithmic discrimination, potentially leading to enforcement actions or guidance that clarifies legal obligations.

Future cases will likely focus on the adequacy of employers' efforts to prevent algorithmic discrimination rather than simply whether discrimination occurred. Employers who can demonstrate comprehensive bias prevention measures will be better positioned to defend against discrimination claims, even if their AI systems produce some disparate outcomes.

Staying Ahead of the Curve: Anticipating Legal Developments

The legal landscape surrounding AI in employment continues to evolve rapidly, with new legislation, regulatory guidance, and court decisions shaping how these technologies can be used legally. Understanding these developments is crucial for both employers implementing AI systems and employees working under them.

The pace of technological change often outstrips legal development, creating uncertainty about what is permitted and what may be prohibited in the future. However, several trends in legal development are becoming clear, offering guidance on likely future requirements and restrictions.

The Impact of the Employment Rights Bill

The Employment Rights Bill represents the most significant potential change to UK employment law in decades. While the full Bill may not be implemented until 2026, its provisions will likely influence tribunal decisions and employer practices well before formal implementation. The Bill's approach to AI and automated decision-making could set new standards for fairness and transparency.

Day-one unfair dismissal rights proposed in the Bill would significantly strengthen employee protections against AI-driven dismissals. Currently, employees need two years of continuous service to claim unfair dismissal, but the Bill would eliminate this requirement. This change would mean that even new employees could challenge dismissals based on AI recommendations, increasing the legal risks for employers.

The Bill's provisions on fire and rehire practices could also affect AI-driven dismissals. If AI systems identify employees for dismissal as part of restructuring efforts, the enhanced protections in the Bill would require employers to follow more rigorous consultation processes. This could make it harder to justify dismissals based solely on algorithmic recommendations.

Collective consultation requirements may also be strengthened under the Bill, particularly for AI-driven redundancies affecting multiple employees. If AI systems identify groups of employees for dismissal, employers may need to engage in more extensive consultation with trade unions or employee representatives before proceeding.

Regulatory Guidance and Case Law

ACAS has begun developing guidance on AI use in employment, recognising the need for clear standards in this emerging area. Their guidance emphasises the importance of human oversight, transparency, and fairness in AI-driven employment decisions. As this guidance develops, it will likely become a key reference point for employment tribunals assessing AI-related cases.

The Information Commissioner's Office continues to develop its approach to AI and data protection in employment contexts. Their guidance on automated decision-making and employment data processing provides important insights into legal requirements. Future ICO enforcement actions will likely clarify what constitutes adequate protection for employees subject to AI-driven decisions.

Employment tribunal cases involving AI are beginning to emerge, though published decisions remain limited. Early cases suggest that tribunals will scrutinise AI systems carefully, requiring employers to demonstrate that their use of AI is fair, transparent, and non-discriminatory. These cases will establish important precedents for future AI-related employment disputes.

The role of expert evidence in AI-related employment cases is becoming increasingly important. Technical experts who can explain how AI systems work and whether they operate fairly will be crucial in both defending and challenging AI-driven employment decisions. This trend suggests that employment law practice will need to incorporate more technical expertise.

International Comparisons

The European Union's AI Act provides a comprehensive framework for AI regulation that may influence UK developments. The Act's requirements for high-risk AI systems, including those used in employment, establish standards for risk assessment, documentation, and human oversight that could inform UK policy.

Different approaches to AI regulation across jurisdictions create challenges for multinational employers. While the UK currently takes a more flexible approach than the EU, pressure for convergence may lead to similar requirements over time. Employers operating across multiple jurisdictions must navigate varying legal requirements while maintaining consistent practices.

The United States has seen significant litigation around algorithmic discrimination in employment, with courts establishing precedents that may influence UK legal development. These cases demonstrate the types of evidence and arguments that may be relevant in UK employment tribunal cases involving AI systems.

Learning from international experience can help UK employers prepare for likely future requirements. Countries that have implemented specific AI regulations in employment contexts provide examples of both successful and problematic approaches, offering lessons for UK policy development and employer practices.

Navigating AI in HR: A Practical Guide

Successfully implementing AI in HR requires careful attention to legal requirements, ethical considerations, and practical challenges. Both employers and employees need clear guidance on how to navigate this complex landscape while protecting their rights and interests.

The key to successful AI implementation in HR lies in understanding that technology should support, not replace, good employment practices. AI systems can enhance decision-making, but they cannot substitute for the human judgement, empathy, and flexibility that effective HR requires.

Guidance for Employers

For Employers: • Develop comprehensive AI policies explaining system use and employee rights • Conduct regular risk assessments for legal compliance • Provide ongoing training for HR professionals and managers • Build meaningful human oversight into all AI systems • Implement regular audit and monitoring procedures • Maintain detailed documentation and records

Developing comprehensive AI policies represents the foundation of legally compliant AI use in HR. These policies should clearly explain how AI systems are used, what data they process, and what safeguards are in place to ensure fairness. Employees should understand their rights regarding AI-driven decisions and how they can challenge outcomes they believe are unfair.

Risk assessment becomes crucial when implementing AI systems in HR. Employers should identify potential legal risks, including discrimination, data protection violations, and procedural fairness issues. Regular review and updating of risk assessments ensures that new developments in technology and law are properly addressed.

Training for HR professionals and managers using AI systems must go beyond basic system operation. Users need to understand the legal implications of AI-driven decisions, the limitations of AI systems, and their responsibilities for ensuring fair outcomes. This training should be ongoing, updating users on legal developments and system changes.

Human oversight mechanisms must be built into AI systems from the start, not added as an afterthought. Decision-makers need the authority, information, and training necessary to provide meaningful oversight of AI recommendations. This includes the ability to override AI decisions when individual circumstances warrant different treatment.

Audit and monitoring systems help ensure ongoing compliance with legal requirements. Regular audits should examine both the technical performance of AI systems and their real-world outcomes. These audits should look for bias, errors, and unintended consequences that could create legal liability.

Documentation and record-keeping become more complex but more important with AI systems. Employers must maintain records of AI system decisions, the data used to make those decisions, and any human oversight provided. This documentation becomes crucial evidence if decisions are later challenged.

Guidance for Employees

For Employees: • Understand your data protection rights regarding AI systems • Request transparency about AI use in your workplace • Challenge unfair AI-driven decisions through proper channels • Seek support from unions or legal advisors when needed • Stay informed about legal developments affecting AI in employment

Understanding your rights regarding AI-driven employment decisions empowers you to protect yourself and challenge unfair treatment. Data protection rights give you access to information about how AI systems use your personal data and the logic behind automated decisions. Exercise these rights to understand how AI affects your employment.

Requesting transparency about AI systems used in your workplace helps you understand how your performance is assessed and decisions about you are made. Employers should be able to explain their AI systems in understandable terms and describe the safeguards in place to ensure fairness.

Challenging AI-driven decisions requires understanding both the technical and legal aspects of how these systems work. If you believe an AI system has made an unfair decision about you, start by raising concerns through internal grievance procedures. Document your concerns carefully and request specific information about how the decision was made.

Seeking support from trade unions, employee representatives, or legal advisors can help you navigate complex AI-related employment issues. These representatives can help you understand your rights, challenge unfair decisions, and ensure that your voice is heard in discussions about AI implementation.

Staying informed about legal developments in AI and employment law helps you understand how changing regulations might affect your rights. Resources like Litigated provide expert analysis of employment law developments that can help you stay ahead of changes that might affect your workplace.

The Human Element Remains Crucial in the Age of AI

The integration of AI into employment decision-making represents both an opportunity and a challenge for UK workplaces. While these technologies offer benefits in terms of efficiency and consistency, they cannot replace the human judgement, empathy, and contextual understanding that fair employment decisions require.

Employment law's fundamental principles of fairness, transparency, and non-discrimination remain unchanged regardless of the technology used to support decision-making. AI systems must be designed, implemented, and monitored to ensure they support rather than undermine these principles. The legal tests for fair dismissal continue to apply, requiring both substantive and procedural fairness whether decisions are made by humans or algorithms.

The evolving legal landscape suggests that regulation of AI in employment will continue to develop, with stronger protections for employees and clearer obligations for employers. Staying informed about these developments and adapting practices accordingly will be essential for both employers and employees navigating this changing environment.

Human oversight and accountability remain at the heart of fair employment practices. While AI can provide valuable insights and support better decision-making, the responsibility for ensuring fair treatment ultimately rests with human decision-makers. This responsibility cannot be delegated to algorithms, no matter how sophisticated they become.

Frequently Asked Questions About AI, Performance, and Dismissals in UK Employment Law

Can an Employer Use AI to Monitor My Performance?

Employers can use AI systems to monitor employee performance, but they must comply with strict legal requirements. Under UK GDPR, you have the right to know what personal data is being collected about you and how it's being used. Your employer must provide clear information about their AI monitoring systems, including what data is collected, how it's analysed, and what decisions might be based on this information. The monitoring must be proportionate to legitimate business needs and cannot be used for purposes that would be considered excessive or intrusive. You also have rights to access your personal data and understand the logic behind any automated decisions that affect you.

Is a Dismissal Based Solely on an AI Decision Legal in the UK?

A dismissal based entirely on an automated AI decision without meaningful human involvement would likely be found unfair by an employment tribunal. UK employment law requires both substantive fairness (a fair reason for dismissal) and procedural fairness (following a fair process). While AI can provide evidence to support dismissal decisions, there must be genuine human oversight and decision-making involved. The human decision-maker must have the authority and information necessary to make an independent assessment, including the ability to override AI recommendations when appropriate. Simply rubber-stamping an AI recommendation would not satisfy the requirements for fair dismissal.

How Does the Equality Act 2010 Apply to AI in HR?

The Equality Act 2010 fully applies to AI-driven employment decisions, prohibiting discrimination based on protected characteristics such as age, race, gender, disability, and religion. AI systems can discriminate either directly (by explicitly using protected characteristics) or indirectly (by using criteria that disproportionately affect certain groups). Employers have a duty to ensure their AI systems do not produce discriminatory outcomes and must take proactive steps to identify and eliminate bias. This includes auditing AI systems for discriminatory patterns, using diverse and representative training data, and implementing safeguards to prevent unfair treatment of protected groups.

What Should I Do if I Think an AI-Influenced Decision About Me Was Unfair?

Start by raising your concerns through your employer's internal grievance procedure, clearly explaining why you believe the AI-influenced decision was unfair. Request detailed information about how the AI system reached its decision, including what data was used and what criteria were applied. Use your data protection rights to access information about automated decision-making processes. If internal procedures don't resolve your concerns, seek advice from ACAS, a trade union representative, or an employment law solicitor. You may have grounds for an employment tribunal claim if you can demonstrate that the decision was unfair, discriminatory, or violated your employment rights. Keep detailed records of all communications and evidence related to your case.

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Nick

With a background in international business and a passion for technology, Nick aims to blend his diverse expertise to advocate for justice in employment and technology law.