How Democracy Can Inform Consent: Cases of the Internet and Bioethics

Gould, Carol C.

doi:10.1111/japp.12360

Cited by 8 publications

(13 citation statements)

References 9 publications

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“…These epistemic issues are particularly pressing, if informed consent’s normative core (ie, protecting a patient’s autonomy and dignity) is to be kept intact. Instead, without providing adequate information, it runs at risk of eroding into a merely formalistic and legal form of protection 26 27…”

Section: Pitfalls Of Algorithmic Decision-making At the Individual Lementioning

confidence: 99%

On the ethics of algorithmic decision-making in healthcare

2019

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In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare.

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Section: Pitfalls Of Algorithmic Decision-making At the Individual Lementioning

confidence: 99%

On the ethics of algorithmic decision-making in healthcare

2019

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“…They are depicted as wanting ‘their’ data to be used while restrictions on data reuse, such as re-consenting in narrow informed consent models, are framed as a burden for them rather than as a protective or empowering element. This line of argument stands in tension with the simultaneously made arguments that build on the reciprocity between research and those who provide data (Kaye et al., 2015; Titmuss, 1971) or that underline the importance to supplement informed consent models with attempts for deliberation, participation and representation (Gould, 2019). These arguments are in line with the currently widespread claim for more inclusive forms of research, manifested in frames such as Responsible Research and Innovation (Braun and Griessler, 2018; Felt, 2018).…”

Section: Discussionmentioning

confidence: 99%

Negotiating the reuse of health-data: Research, Big Data, and the European General Data Protection Regulation

Starkbaum

Felt

2019

Big Data & Society

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Before the EU General Data Protection Regulation entered into force in May 2018, we witnessed an intense struggle of actors associated with data-dependent fields of science, in particular health-related academia and biobanks striving for legal derogations for data reuse in research. These actors engaged in a similar line of argument and formed issue alliances to pool their collective power. Using descriptive coding followed by an interpretive analysis, this article investigates the argumentative repertoire of these actors and embeds the analysis in ethical debates on data sharing and biobank-related data governance. We observe efforts to perform a paradigmatic shift of the discourse around the General Data Protection Regulation-implementation away from ‘protecting data’ as key concern to ‘protecting health’ of individuals and societies at large. Instead of data protection, the key risks stressed by health researchers became potential obstacles to research. In line, exchange of information with data subjects is not a key concern in the arguments of biobank-related actors and it is assumed that patients want ‘their’ data to be used. We interpret these narratives as a ‘reaction’ to potential restrictions for data reuse and in line with a broader trend towards Big Data science, as the very idea of biobanking is conceptualized around long-term use of readily prepared data. We conclude that a sustainable implementation of biobanks needs not only to comply with the General Data Protection Regulation, but must proactively re-imagine its relation to citizens and data subjects in order to account for the various ways that science gets entangled with society.

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“…The inequalities in power and the injustices in decision making and in distributions follow from the structures themselves. However, as I have argued elsewhere (Gould , 13–14), I think we need to discriminate two meanings of structural injustice that are not usually distinguished. The term “structural injustice” (along with the related terms “systemic injustice” and “systemic violence”) emerged from the Marxist tradition.…”

Section: Part I: Democracy In Crisis: Diagnosing the Problemmentioning

confidence: 98%