Clinical utility is an increasingly used concept in health care, but one that lacks an agreed formal definition or conceptualization. In this article, I show that the term is commonly used as a synonym for studies of clinical effectiveness and/or economic evaluations and argue that further factors relating to everyday working practice should be included under its auspices. I go on to develop a multi-dimensional model that outlines four factors in practitioners' judgements about clinical utility: appropriateness, accessibility, practicability, and acceptability.

We examine the way race and racial categories are adopted in algorithmic fairness frameworks. Current methodologies fail to adequately account for the socially constructed nature of race, instead adopting a conceptualization of race as a fixed attribute. Treating race as an attribute, rather than a structural, institutional, and relational phenomenon, can serve to minimize the structural aspects of algorithmic unfairness. In this work, we focus on the history of racial categories and turn to critical race theory and sociological work on race and ethnicity to ground conceptualizations of race for fairness research, drawing on lessons from public health, biomedical research, and social survey research. We argue that algorithmic fairness researchers need to take into account the multidimensionality of race, take seriously the processes of conceptualizing and operationalizing race, focus on social processes which produce racial inequality, and consider perspectives of those most affected by sociotechnical systems.

Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, emergent issues can become difficult or impossible to trace back to their source. In this paper, we introduce a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle. Each stage of the audit yields a set of documents that together form an overall audit report, drawing on an organization's values or principles to assess the fit of decisions made throughout the process. The proposed auditing framework is intended to contribute to closing the accountability gap in the development and deployment of large-scale artificial intelligence systems by embedding a robust process to ensure audit integrity. CCS CONCEPTS • Social and professional topics → System management; Technology audits; • Software and its engineering → Software development process management.

Background/AimsThis paper reports data from a qualitative study of patient experiences of DNA testing and cascade screening for hypertrophic cardiomyopathy and long QT syndrome, cardiac conditions that place sufferers at risk of sudden death. The paper particularly focuses on potential impediments to testing and screening. Methods Semi-structured interviews were undertaken with a purposive sample of 27 people in the UK who had undergone testing. ResultsIn the context of the uncertainties that can characterize experiences of these disorders, the majority of participants in this sample embraced testing and screening as a way of providing health information for themselves or their relatives (particularly children). There was nevertheless evidence of ambivalence about the value and impact of the DNA test information which could influence participants' dispositions toward testing, and play into dilemmas about family communication. Other concerns arose in relation to communicating about these disorders, decisions to involve elderly relatives and pressures relating to family responsibility. ConclusionThe evidence of ambivalence provides insight into why some people may be resistant to testing, screening and sharing information. The findings about communication processes indicate potential areas of concern for the cascading process.

This is the accepted version of the paper.This version of the publication may differ from the final published version. Design. The WTM is nationally representative of the UK population and has collected data over two waves, 2009 and 2012. Data pooled from both waves (n = 2575) were used to examine associations between ethnic group and participation in medical research, and willingness to participate in medical research. Logistic regression analysis used models that controlled for socio-economic and demographic factors, and relevant outlooks and experiences that are, or could reasonably be assumed to be, associated with engagement with medical research. Permanent repository link: Results.Respondents from the BAME group were less likely to have participated in medical research compared to those from the White British group, but there was only patchy evidence of small ethnic group differences in willingness to participate. Influences on engagement in medical research varied somewhat between the White British and BAME groups, in particular in relation to occupation, education, health, attitudes to medical science and belief.Conclusions. These findings consolidate previously context-specific evidence of BAME group under-representation in the UK, and highlight the heterogeneity that exists within the broad BAME group. Efforts to address the under-representation of those from BAME groups might benefit from targeted strategies for recruitment and advocacy, although improved datasets are required to fully understand ethnic differences in engagement with medical research.