Collective Reflective Equilibrium and algorithmic bioethics: a new approach to complex ethical dilemmas

The intersection of ethics, resource allocation, and artificial intelligence is becoming increasingly crucial in modern bioethics. In a recent paper, Julian Savulescu explores how we can navigate ethical complexities through a methodology called Collective Reflective Equilibrium (CRE). This approach, combined with algorithmic bioethics, offers a way to balance competing moral theories while ensuring practical and justifiable policy decisions.

The Challenge of Ethical Decision-Making in Medicine

Ethical dilemmas in medicine often arise when there are limited resources. Who should receive a ventilator during a pandemic? Should age or disability influence organ transplant eligibility? Traditional bioethics provides conflicting answers: egalitarianism prioritises equal treatment, utilitarianism seeks to maximise benefits, and contractualism emphasises fairness based on rational agreement. The challenge is integrating these diverse perspectives into a coherent decision-making framework.

What is Collective Reflective Equilibrium?

Inspired by John Rawls’ Reflective Equilibrium, CRE seeks to resolve ethical conflicts by balancing moral intuitions with theoretical principles. Instead of relying on a single ethical theory, it considers public values and empirical data to find a broadly acceptable resolution. For example, in allocating scarce medical resources, public preferences, ethical theories, and practical constraints are all weighed together to refine policies that are both morally justified and socially implementable.

Algorithmic Bioethics: Structuring Ethical Decisions

Algorithmic bioethics formalises ethical reasoning into structured decision processes. By creating decision trees that incorporate key ethical considerations—such as maximising survival rates while respecting individual rights—AI and policymakers can apply these frameworks in real-world scenarios. This was evident in ventilator allocation during COVID-19, where decisions had to balance survival probability, duration of treatment, and fairness.

Applying CRE in Practice

One key example explored in Savulescu’s work is the debate over saving the greatest number of lives. Philosophers like John Taurek argue that fairness requires giving each person an equal chance, such as through a lottery. However, public surveys and ethical theories like utilitarianism and contractualism often converge on prioritising saving the most people. CRE helps navigate these disagreements by integrating ethical principles with real-world attitudes. For example, Savulescu shows how both utilitarianism and contractualism converge and are supported by public attitudes to save the greatest number, which implies that those who have the greatest chance of surviving and those who would use a scarce lifesaving resource for the shortest time (such as a ventilator) should have priority.

Additionally, the paper discusses the fair innings argument, which suggests that younger individuals should be prioritised in healthcare to ensure they reach a certain threshold of life years. This principle is contrasted with public preferences, which often favour saving younger individuals even beyond the fair innings threshold. CRE enables a balanced approach by weighing ethical theories against empirical data. Both utilitarianism and contractualism again converge with public attitudes to give greater priority to the young.

The Future of Ethical AI in Medicine

As AI becomes more involved in medical decision-making, algorithmic bioethics ensures these systems make ethically sound choices. By embedding pluralistic ethical reasoning into AI, we can create decision-making tools that reflect both moral theories and societal values. This could revolutionise how we approach dilemmas in healthcare, from organ allocation to pandemic response strategies.

Furthermore, the paper highlights how AI can be utilized to analyse large-scale moral preferences, such as those identified in the Moral Machine experiment, which collected global responses on ethical dilemmas in autonomous vehicle programming. By incorporating public moral reasoning into ethical algorithms, policymakers can craft regulations that are both principled and publicly acceptable.

Conclusion

Collective Reflective Equilibrium and Algorithmic Bioethics offer a promising way to navigate the complexities of modern medical ethics. By embracing plural values, integrating empirical research, and structuring decision-making through ethical algorithms, we can develop policies that are both just and practical. As medicine and AI evolve, these frameworks will be essential in guiding ethical decision-making in increasingly complex scenarios.

This approach ensures that ethical deliberation remains transparent, inclusive, and adaptable to future challenges in bioethics.

This blog is based on the paper: Savulescu J. Collective Reflective Equilibrium, Algorithmic Bioethics and Complex Ethics. Cambridge Quarterly of Healthcare Ethics. Published online 2025:1-16. doi:10.1017/S0963180124000719. We were assisted by AI in the writing of this blog.