The ethics of risk is a branch of ethical inquiry that examines how risks should be assessed, distributed, and managed, particularly in relation to technological, social, and environmental uncertainties (Hansson, 2013). It addresses the moral obligations of individuals, organisations, and governments when making decisions that involve potential risks to human well-being, justice, and societal values. This framework is particularly relevant in areas such as artificial intelligence (AI), defence, healthcare, and climate change, where uncertainty and high stakes necessitate ethical deliberation (Roeser, 2012).
One of the key principles in the ethics of risk is the precautionary principle, which states that measures should be taken to prevent harm even in the absence of conclusive scientific evidence (Sunstein, 2005). This principle is particularly significant in emerging technologies such as AI, where the long-term implications remain uncertain. Embedding responsible principles and processes that can respond to future, as yet undefined, situations in appropriate ways rather than simply following a script that is no longer relevant, is particularly important.
Another critical aspect of risk ethics is the distinction between acceptable and unacceptable risks. Ethical discussions often revolve around determining what levels of risk are morally justifiable, considering the potential benefits and harms (Beck, 1992). In AI-driven healthcare, for example, while machine learning can enhance efficiency in diagnostics, the risk of algorithmic biases leading to incorrect diagnoses raises concerns about patient safety and fairness (Mittelstadt et al., 2016). This is particularly pertinent in defence where the chaos and uncertainty of a contested military environment is inherently high risk for many actors in many ways. Appropriately balancing risks of harm to multiple different stakeholders (e.g. friendly forces, civilians) when choosing whether to advance a force through a minefield or a neighbouring village is something that military personal are trained to consider.
The ethics of risk also emphasises the fair distribution of risk, ensuring that certain groups do not disproportionately bear the burden while others reap the benefits (Shrader-Frechette, 2002). In AI surveillance systems, ethical concerns arise when facial recognition technologies are disproportionately used against marginalised communities, increasing the risk of discrimination and privacy violations (Benjamin, 2019). A just approach to risk distribution necessitates careful policy considerations to prevent the exacerbation of existing inequalities.
Transparency and informed consent are fundamental ethical requirements in risk management. Individuals and societies affected by AI risks should be aware of potential consequences and have the opportunity to make informed decisions (Floridi et al., 2018). This principle is particularly relevant in cases where AI-driven technologies, such as facial recognition or algorithmic hiring systems, operate without adequate public understanding or oversight, raising questions about accountability and consent (O’Neil, 2016).
Another essential principle in the ethics of risk is responsibility and accountability. When AI-driven systems make harmful decisions, determining liability becomes a complex issue (Moor, 1985). If an autonomous military system incorrectly identifies a target, responsibility may be unclear—should accountability lie with the developers, military operators, or the AI itself? Ethical AI frameworks, such as those set out here by the UK Ministry of Defence (MoD), stress the importance of ensuring that AI remains understandable, responsible, and reliable (UK MoD, 2022). See card:
What is meant by “an accountability gap”?, and see also the discussion on "Moral luck" Responsibility vs Accountability, what is the difference?.
Finally, risk-benefit analysis plays a central role in ethical risk assessment, but it must be balanced against fundamental ethical boundaries. While certain AI applications may offer significant societal benefits, some uses may remain ethically unacceptable regardless of their efficiency (Bostrom, 2014). For example, an AI-powered interrogation system that improves crime detection but violates human rights would be ethically unjustifiable, highlighting the need to prioritise ethical considerations over utilitarian efficiency.
As well as considering the risk of taking a particular path or action, it is important to consider the risk of not acting. What happens if you don’t do something and is the potential cost even higher? Inaction is rarely cost neutral.
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