Governing AI‐Driven Health Research: Are IRBs Up to the Task?

Friesen, Phoebe; Douglas‐Jones, Rachel; Marks, Mason; Pierce, Robin; Fletcher, Katherine; Mishra, Abhishek; Lorimer, Jessica; Véliz, Carissa; Hallowell, Nina; Graham, Mackenzie; Chan, Mei Sum; Davies, Huw C.; Sallamuddin, Taj

doi:10.1002/eahr.500085

Cited by 21 publications

(25 citation statements)

References 16 publications

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“…First, they challenge traditional research principles such as data privacy, informed consent, scientific validity of research, risk assessment, and distribution of benefits ( Price & Cohen, 2019 ; Rivas Velarde et al, 2020 ). Second, they introduce new epistemic challenges related to the assessment of scientific validity, technological reliability, accountability, fairness, and transparency ( Friesen et al, 2021 ). Finally, they challenge the very notion of human participants in research, as they enable retrospective data processing without physical interaction with research participants ( Metcalf & Crawford, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study

Ferretti

Ienca

Velarde

et al. 2021

Journal of Empirical Research on Human Research Ethics

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Big data trends in health research challenge the oversight mechanism of the Research Ethics Committees (RECs). The traditional standards of research quality and the mandate of RECs illuminate deficits in facing the computational complexity, methodological novelty, and limited auditability of these approaches. To better understand the challenges facing RECs, we explored the perspectives and attitudes of the members of the seven Swiss Cantonal RECs via semi-structured qualitative interviews. Our interviews reveal limited experience among REC members with the review of big data research, insufficient expertise in data science, and uncertainty about how to mitigate big data research risks. Nonetheless, RECs could strengthen their oversight by training in data science and big data ethics, complementing their role with external experts and ad hoc boards, and introducing precise shared practices.

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Section: Introductionmentioning

confidence: 99%

The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study

Ferretti

Ienca

Velarde

et al. 2021

Journal of Empirical Research on Human Research Ethics

View full text Add to dashboard Cite

show abstract

“…Ethics review boards must demand this type of transparency and fairness in the creation of models so that systems that harness machine learning can be implemented in real clinical practice with low risk. Some discussion of this path forward has been brought to light by Friesen et al (2021) who reported on IRBs as a means of ethics oversight in health research that harnesses AI.…”

Section: Discussionmentioning

confidence: 99%

Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

2021

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Recently, research on mental health conditions using public online data, including Reddit, has surged in NLP and health research but has not reported user characteristics, which are important to judge generalisability of findings. This paper shows how existing NLP methods can yield information on clinical, demographic, and identity characteristics of almost 20K Reddit users who self-report a bipolar disorder diagnosis. This population consists of slightly more feminine-than masculinegendered mainly young or middle-aged USbased adults who often report additional mental health diagnoses, which is compared with general Reddit statistics and epidemiological studies. Additionally, this paper carefully evaluates all methods and discusses ethical issues. ReferencesWasim Ahmed, Peter A. Bath, and Gianluca Demartini.2017. Using Twitter as a data source: an overview of ethical, legal and methodological challenges. In Kandy Woodfield, editor, The Ethics of Online Research, pages 79-107. Emerald Books.

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“…Facebook, for example, is carrying out a suicide prediction and prevention project, which relies exclusively on the information that users post on the social network [18]. Because this type of research is now possible, and the available ethics review model exempts many big data projects from ERC appraisal, gaps in oversight are growing [17,73]. Just as corporations can re-use publicly available datasets (such as social media data) to determine life insurance premiums [74], citizen science projects can be conducted without seeking research oversight [75].…”

Section: Novel Weaknesses: Purview Weaknessesmentioning

confidence: 99%

Ethics review of big data research: What should stay and what should be reformed?

et al. 2021

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Background Ethics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts. Main text In this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC’s scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC’s way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science. Conclusions We believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large.

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Governing AI‐Driven Health Research: Are IRBs Up to the Task?

Cited by 21 publications

References 16 publications

The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study

The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study

Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

Ethics review of big data research: What should stay and what should be reformed?

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