Healthcare priority-setting institutions have good reason to want to demonstrate that their decisions are morally justified—and those who contribute to and use the health service have good reason to hope for the same. However, finding a moral basis on which to evaluate healthcare priority-setting is difficult. Substantive approaches are vulnerable to reasonable disagreement about the appropriate grounds for allocating resources, while procedural approaches may be indeterminate and insufficient to ensure a just distribution. In this paper, we set out a complementary, (...) coherence-based approach to the evaluation of healthcare priority-setting. Drawing on Rawls, we argue that an institutional priority-setter’s claim to moral justification can be assessed, in part, based on the extent to which its many normative commitments are mutually supportive and free from dissonance; that is, on the ability to establish narrow reflective equilibrium across the normative content of a priority-setter’s policy and practice. While we do not suggest that the establishment of such equilibrium is sufficient for moral justification, we argue that failure to do so might—like the proverbial canary in the coalmine—act as a generalised warning that something is awry. We offer a theoretical argument in support of this view and briefly outline a practical method for systematically examining coherence across priority-setting policy and practice. (shrink)

Background As the UK’s main healthcare priority-setter, the National Institute for Health and Care Excellence (NICE) has good reason to want to demonstrate that its decisions are morally justified. In doing so, it has tended to rely on the moral plausibility of its principle of cost-effectiveness and the assertion that it has adopted a fair procedure. But neither approach provides wholly satisfactory grounds for morally defending NICE’s decisions. In this study we adopt a complementary approach, based on the proposition that (...) a priority-setter's claim to moral justification can be assessed, in part, based on the coherence of its approach and that the reliability of any such claim is undermined by the presence of dissonance within its moral system. This study is the first to empirically assess the coherence of NICE’s formal approach and in doing so to generate evidence-based conclusions about the extent to which this approach is morally justified. Methods The study is grounded in the theory, methods and standards of empirical bioethics. Twenty NICE policy documents were coded to identify and classify the normative commitments contained within NICE technology appraisal policy as of 31 December 2021. Coherence was systematically assessed by attempting to bring these commitments into narrow reflective equilibrium (NRE) and by identifying sources of dissonance. Findings Much of NICE policy rests on coherent values that provide a strong foundation for morally justified decision-making. However, NICE’s formal approach also contains several instances of dissonance which undermine coherence and prevent NRE from being fully established. Dissonance arises primarily from four sources: i) NICE’s specification of the principle of cost-effectiveness; ii) its approach to prioritising the needs of particular groups; iii) its conception of reasonableness in the context of uncertainty, and iv) its concern for innovation as an independent value. Conclusion At the time of analysis, the level of coherence across NICE policy provides reason to question the extent to which its formal approach to technology appraisal is morally justified. Some thoughts are offered on why, given these findings, NICE has been able to maintain its legitimacy as a healthcare priority-setter and on what could be done to enhance coherence. (shrink)

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers have (...) taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

Various U.S. laws, such as the Clean Air Act and the Food Quality Protection Act, require additional protections for susceptible subpopulations who face greater environmental health risks. The main ethical rationale for providing these protections is to ensure that environmental health risks are distributed fairly. In this article, we (1) consider how several influential theories of justice deal with issues related to the distribution of environmental health risks; (2) show that these theories often fail to provide specific guidance concerning policy (...) choices; and (3) argue that an approach to public decision making known as accountability for reasonableness can complement theories of justice in establishing acceptable environmental health risks for the general population and susceptible subpopulations. Since accountability for reasonableness focuses on the fairness of the decision-making process, not the outcome, it does not guarantee that susceptible subpopulations will receive a maximum level of protection, regardless of costs or other morally relevant considerations. (shrink)

Artificial intelligence (AI) is a powerful tool for several healthcare tasks. AI tools are suited to optimize predictive models in medicine. Ethical debates about AI’s extension of the predictive power of medical models suggest a need to adapt core principles of medical ethics. This article demonstrates that a popular interpretation of the principle of justice in healthcare needs amendment given the effect of AI on decision-making. The procedural approach to justice, exemplified with Norman Daniels and James Sabin’saccountability for reasonablenessconception, needs (...) amendment because, as research into algorithmic fairness shows, it is insufficiently sensitive to differential effects of seemingly just principles on different groups of people. The same line of research generates methods to quantify differential effects and make them amenable for correction. Thus, what is needed to improve the principle of justice is a combination of procedures for selecting just criteria and principles and the use of algorithmic tools to measure the real impact these criteria and principles have. In this article, the author shows that algorithmic tools do not merely raise issues of justice but can also be used in their mitigation by informing us about the real effects certain distributional principles and criteria would create. (shrink)