As artificial intelligence (AI) and automated systems are increasingly used to make decisions that affect people’s lives, concerns about algorithmic bias have become more prominent. From predicting criminal behaviour to assessing mortgage applications, algorithms are playing an influential role in shaping legal and economic outcomes. However, when these systems replicate or even amplify social biases, the consequences can be profoundly unjust.

This raises an important legal question: how should the law respond to bias in algorithms?

What Is Algorithmic Bias?

Algorithmic bias arises when an AI system produces outcomes that systematically disadvantage particular groups, often based on race, gender, socioeconomic background, or other protected characteristics. This bias can emerge for several reasons:

  • Biased training data, which reflects historic inequalities
  • Incomplete data sets, which fail to represent certain populations
  • Flawed model design, which incorporates irrelevant but correlated variables
  • Lack of transparency, which makes bias difficult to detect or challenge

Even when the bias is unintentional, these outcomes can reinforce existing discrimination. For example, predictive policing algorithms may lead to increased surveillance of already over-policed communities, while biased credit-scoring systems may exclude marginalised individuals from financial services.

Algorithmic bias is already influencing key decisions in areas such as law enforcement, employment, and finance:

  • Risk assessment tools used in criminal justice systems have shown racial disparities, sometimes overestimating the risk posed by Black defendants.
  • Recruitment algorithms have been found to favour male applicants, particularly in technology roles, when trained on biased hiring data.
  • Housing and lending technologies have led to allegations of digital redlining, where certain demographics or postcodes are unfairly excluded.

When these biased outputs are accepted as objective, they risk embedding inequality more deeply into systems that are supposed to deliver fair outcomes.

Responding to algorithmic bias within existing legal frameworks is far from straightforward. Many of the laws currently in place were drafted before the advent of AI-driven decision-making. Nonetheless, there are a number of avenues through which legal remedies may be pursued.

1. Equality and Anti-Discrimination Law

In the UK, the Equality Act 2010 prohibits discrimination in key areas such as employment, education, housing, and access to services. It covers both direct and indirect discrimination, which could be relevant when algorithms disproportionately disadvantage certain groups.

However, enforcement presents several challenges:

  • Individuals must often demonstrate that a discriminatory impact has occurred, which can be difficult when algorithmic processes are opaque.
  • Companies may claim that decisions were neutral and data-driven, even when the outcomes are biased.

There is growing recognition that the law may need reform to handle complex, data-driven systems more effectively.

2. Data Protection Law

The UK General Data Protection Regulation (UK GDPR) provides important protections for individuals subject to automated decision-making, including:

  • The right to be informed about automated decisions that significantly affect them
  • The right to contest those decisions
  • Requirements for data to be accurate, relevant, and fairly processed

These provisions offer a potential route for individuals to challenge biased or unjust outcomes, particularly when decisions are made without meaningful human oversight.

3. Role of Regulators

Specialist regulators are well placed to enforce fairness and transparency in algorithmic systems. For instance:

  • The Financial Conduct Authority (FCA) could mandate bias audits for credit-scoring and lending tools.
  • The Equality and Human Rights Commission (EHRC) might develop guidance on AI use in recruitment and public services.
  • The proposed Digital Markets, Competition and Consumers Bill may also play a role in setting clearer expectations around fairness and accountability in tech platforms.

By establishing sector-specific rules, regulators can provide clarity and prevent harm before it occurs.

Promoting Fairness in Practice

Legal reform alone is not enough. Addressing algorithmic bias requires collaboration between legal, technical, and ethical domains. Some practical steps include:

  • Conducting bias audits: Organisations should regularly test their models for discriminatory outcomes.
  • Inclusive data practices: Data used to train algorithms should reflect the diversity of the populations affected.
  • Transparent decision-making: AI systems should be explainable and open to scrutiny, allowing individuals to understand and challenge their outputs.
  • Ethical oversight: Internal ethics boards or independent review panels can help guide the responsible development and use of AI technologies.

Conclusion: Holding Algorithms to Account

Algorithmic bias presents a serious threat to fairness and equality. As society becomes increasingly reliant on automated systems, the law must evolve to ensure that these technologies support justice rather than undermine it.

Through stronger regulation, improved transparency, and a commitment to inclusive design, it is possible to create AI systems that serve all communities fairly. In doing so, the legal profession has a vital role to play in shaping a more equitable digital future.

Author's Note:
This article is part of a series exploring how law interacts with emerging technologies. If you are a student interested in contributing, consider joining SCL Student Bytes to take part in the discussion.