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Unpicking Epistemic Injustices in Digital Health: On the Implications of Designing Data-Driven Technologies for the Management of Long-Term Conditions

Lookup NU author(s): Dr Caroline ClaisseORCiD, Professor Abi DurrantORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Applications of Artificial Intelligence (AI) in the domain of Personal Health Informatics (PHI) offer potential avenues for personalised treatment and support for people living with long-term conditions, however, they also present a number of ethical challenges. Whilst participatory approaches can help mitigate concerns by actively involving healthcare professionals, patients, and other stakeholders in design and development, these are constrained by the limits of epistemic standpoints and the risks posed by extrapolation from individuals to groups. In this paper we draw upon interviews with stakeholders involved in Human Immunodeficiency Virus (HIV) care, including clinicians, insurance providers and pharmaceutical industry representatives, to map intentions and ethical considerations for developing PHI tools for people living with HIV. Whilst treatment efficacy for HIV has improved patient quality of life and life expectancy, management and care is complicated by knowledge gaps about what living and ageing with HIV entails. We investigate how the critical concept of epistemic injustice can inform the design of data-driven technologies intended to address these gaps, helping orient expert perspectives within the broader structures and socio-historical influences that shape them. This is of particular importance when designing for marginalized populations such as people with HIV (i.e. who may experience social stigma and be under-resourced, managing multiple conditions), helping to identify and better account for fundamental ethical considerations such as equity.


Publication metadata

Author(s): Bennett SJ, Claisse C, Luger E, Durrant AC

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES '23)

Year of Conference: 2023

Pages: 322-332

Online publication date: 29/08/2023

Acceptance date: 05/05/2023

Date deposited: 27/07/2023

Publisher: ACM

URL: https://doi.org/10.1145/3600211.3604684

DOI: 10.1145/3600211.3604684

ePrints DOI: 10.57711/ynee-wq93

Library holdings: Search Newcastle University Library for this item

ISBN: 979840070231


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