Artificial intelligence systems are making consequential decisions affecting millions of people every day. They determine whether a loan application is approved, whether a CV reaches a human recruiter, whether a medical image is flagged as requiring urgent attention, and — in some contexts — whether a defendant is assessed as high-risk for re-offending and denied bail. These are not merely technical outputs. They are decisions with profound implications for people’s lives, and they raise moral questions that technical solutions cannot resolve alone.
The field of AI ethics — examining what it means for AI systems to behave fairly, transparently, accountably and in ways that respect human dignity — has moved from academic philosophy to regulatory and corporate priority with remarkable speed. The EU AI Act, which came into full force in 2026, represents the most comprehensive attempt yet to translate ethical principles into regulatory requirements. Understanding what the key ethical challenges are, how they arise and what is being done about them is increasingly important for anyone affected by AI-driven decisions — which, in 2026, means almost everyone.
Algorithmic Bias: How It Arises and Why It Matters
AI systems learn from historical data, and historical data reflects historical inequalities. A hiring algorithm trained on historical hiring decisions at a company that historically hired more men than women may learn to replicate that pattern — not because anyone programmed it to discriminate, but because discrimination is embedded in the training data. Similarly, a criminal risk assessment algorithm trained on arrest records may produce racially biased risk scores, not because race is included as an input variable, but because it is correlated with other variables (neighbourhood, prior contact with the criminal justice system) that are themselves products of racial bias in policing.
Buolamwini and Gebru (2018), in their landmark audit of commercial facial recognition systems, found that error rates differed dramatically by demographic group: the best systems had error rates below 1% for light-skinned men, but error rates of over 34% for dark-skinned women. This finding — that AI systems trained primarily on non-representative data perform worse for under-represented groups — has been replicated in medical imaging, speech recognition, natural language processing and many other domains. The practical consequence is that AI systems can systematically disadvantage already disadvantaged groups while appearing, on aggregate performance metrics, to be highly accurate.
The EU AI Act: Regulation Arrives
The European Union’s Artificial Intelligence Act, which entered full application in stages from 2024 to 2026, establishes a risk-based regulatory framework for AI in Europe. It prohibits certain AI applications outright — mass biometric surveillance in public spaces, social scoring by governments, and AI systems that manipulate people through subliminal techniques. It places the most demanding requirements on high-risk AI applications: those used in healthcare, education, employment, critical infrastructure and law enforcement must meet requirements for transparency, human oversight, accuracy and robustness.
For lower-risk applications, including most generative AI tools, the requirements focus primarily on transparency — users must be told when they are interacting with an AI system, and content generated by AI must be labelled. The UK, post-Brexit, has taken a different approach: rather than sector-spanning AI legislation, the UK government has assigned responsibility for AI regulation to existing sector regulators (the FCA for finance, the CQC for healthcare, the ICO for data protection), guided by a cross-sector framework of principles. The regulatory divergence between the UK and EU creates compliance challenges for organisations operating across both jurisdictions.
Transparency and Explainability: The Black Box Problem
Many of the most accurate AI systems — particularly deep neural networks — are also the least interpretable. They can tell you what their output is, but not straightforwardly why. This ‘black box’ problem has significant implications for accountability and trust. A person denied a loan by an AI system has, under GDPR and its UK equivalent, the right to a meaningful explanation of the decision — a right that is difficult to fulfil when the system’s decision process involves millions of parameters interacting in ways that are not reducible to a simple explanation.
The field of explainable AI (XAI) is developing techniques — such as LIME and SHAP — that can provide post-hoc approximations of how specific inputs influenced a specific output. These techniques are useful but imperfect: they provide explanations of the explanation, not of the underlying model. The deeper problem is that very high accuracy and high interpretability are often in tension — simpler, more interpretable models are frequently less accurate. Deciding which to prioritise in a given context is itself an ethical choice, not a technical one.
Islamic Ethics and Artificial Intelligence
The questions that AI ethics raises — about justice, fairness, accountability, and the proper treatment of human dignity — are not new questions. They are ancient questions being asked in a new context. The Islamic tradition has a rich framework for thinking about justice (adl), accountability (amanah, the concept of trust and responsibility), and the obligations of those with power toward those who are affected by their decisions. These frameworks are directly applicable to the design and deployment of AI systems.
Muslim scholars and thinkers are increasingly engaging with AI ethics through an Islamic lens — examining questions such as: what obligations do AI developers have toward the people affected by their systems? How should we think about accountability when harm results from an AI decision? What forms of surveillance and behavioural manipulation are incompatible with human dignity and autonomy as Islamic ethics understands them? These are not peripheral questions. As AI systems become more powerful and more pervasive, the ethical frameworks through which we evaluate them will shape the kind of society we live in. The regulatory dimension is explored in more depth in our article on the AI arms race, and the technical foundations are covered in our guide to what is artificial intelligence.
References
- Buolamwini, J. and Gebru, T. (2018) ‘Gender shades: intersectional accuracy disparities in commercial gender classification’, Proceedings of Machine Learning Research, 81, pp. 1–15.
- European Parliament (2024) Regulation (EU) 2024/1689 of the European Parliament and of the Council: the Artificial Intelligence Act. Brussels: Official Journal of the European Union.
- Floridi, L. et al. (2018) ‘AI4People — an ethical framework for a good AI society’, Minds and Machines, 28(4), pp. 689–707.
- O’Neil, C. (2016) Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. London: Allen Lane.
