Co-design and ethical artificial intelligence for health: An agenda for critical research and practice

Donia, Joseph; Shaw, James

doi:10.1177/20539517211065248

Cited by 37 publications

(22 citation statements)

References 111 publications

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“…While they have taken shape in very different ways, participatory practices are sustained by the understanding that people directly or indirectly affected by emerging technologies (as consumers, patients, or citizens), should have a say on the development, use, or oversight of said technologies ( Hagendijk and Irwin, 2006 ; Felt and Fochler, 2008 ; Gottweis et al, 2008 ; Doubleday and Wynne, 2011 ; Braun and Könninger, 2018 ). In the case of AI, public participation is also referred to as “co-creation” or “co-design ( Donia and Shaw, 2021 ). Involving the public in the development and oversight of AI-based systems is expected to render the latter socially robust and acceptable.…”

Section: Resultsmentioning

confidence: 99%

“Democratizing” artificial intelligence in medicine and healthcare: Mapping the uses of an elusive term

2022

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Introduction: “Democratizing” artificial intelligence (AI) in medicine and healthcare is a vague term that encompasses various meanings, issues, and visions. This article maps the ways this term is used in discourses on AI in medicine and healthcare and uses this map for a normative reflection on how to direct AI in medicine and healthcare towards desirable futures.Methods: We searched peer-reviewed articles from Scopus, Google Scholar, and PubMed along with grey literature using search terms “democrat*”, “artificial intelligence” and “machine learning”. We approached both as documents and analyzed them qualitatively, asking: What is the object of democratization? What should be democratized, and why? Who is the demos who is said to benefit from democratization? And what kind of theories of democracy are (tacitly) tied to specific uses of the term?Results: We identified four clusters of visions of democratizing AI in healthcare and medicine: 1) democratizing medicine and healthcare through AI, 2) multiplying the producers and users of AI, 3) enabling access to and oversight of data, and 4) making AI an object of democratic governance.Discussion: The envisioned democratization in most visions mainly focuses on patients as consumers and relies on or limits itself to free market-solutions. Democratization in this context requires defining and envisioning a set of social goods, and deliberative processes and modes of participation to ensure that those affected by AI in healthcare have a say on its development and use.

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Section: Resultsmentioning

confidence: 99%

“Democratizing” artificial intelligence in medicine and healthcare: Mapping the uses of an elusive term

2022

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“…Although several articles have highlighted how AI might help address health care shortages in LMICs, a recurrent problem noted in the literature is the lack of consideration for the infrastructure and human resources necessary to implement these AI technologies. To support the use of digital technologies, and specifically AI, LMICs require both health care providers trained to use specific technologies and sufficient technological infrastructure, including buildings where the hardware can be housed and cables to carry digital signals leading to widespread and stable internet access; in other words, the performance of AI algorithms is intertwined with sociotechnical factors [ 156 , 157 ]. Although HICs may have existing technological infrastructure to implement AI technologies more readily, LMICs often lack such infrastructure [ 158 ].…”

Section: Resultsmentioning

confidence: 99%

The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review

Istasy¹,

Lee²,

Iansavichene³

et al. 2022

J Med Internet Res

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Background The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. Objective We aimed to conduct a scoping review of the literature to address the question, “What are the current and potential impacts of AI technologies on health equity in oncology?” Methods Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. Results Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. Conclusions Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI’s ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.

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“…A central tension of this this recent wave of "participatory" is whether these mechanisms should merely serve to aid in the refinement of relevant machine learning sytem or rather emphasize lived experience as a critical form of knowledge and employ experiential learning as a force for community empowerment and advance algorithmic equity [33,49] or ensure wider humanitarian or societal benefits [11,14]. The heavy influence of industry stakeholders calling for greater participation without resolving these tensions has led to concerns of 'participationwashing' and calls for a greater need to focus on broader social structures and uneven power asymmetries [17,80], as well as the limits of participation in specific applications, such as healthcare [27].…”

Section: The Emergence Of Participatory Aimentioning

confidence: 99%

“…A growing body of work has shown the different roles and formats that participation can take in the development of AI, including: new approaches to technical development in NLP in healthcare [27,68], in the development of alternative design toolkits and processes [49,61], and methods that range from structured interviews to citizens juries [6]. In these cases, participation is meant to move beyond individual opinion to center the values of inclusion, plurality, collective safety, and ownership, subsequently shifting the relationship from one of designer-and-user to one of co-designers and co-creators.…”

Section: Introductionmentioning

confidence: 99%

Power to the People? Opportunities and Challenges for Participatory AI

Birhane,

Isaac,

Prabhakaran

et al. 2022

Preprint

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Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining momentum: the increased attention comes partly with the view that participation opens the gateway to an inclusive, equitable, robust, responsible and trustworthy AI.Among other benefits, participatory approaches are essential to understanding and adequately representing the needs, desires and perspectives of historically marginalized communities. However, there currently exists lack of clarity on what meaningful participation entails and what it is expected to do. In this paper we first review participatory approaches as situated in historical contexts as well as participatory methods and practices within the AI and ML pipeline. We then introduce three case studies in participatory AI. Participation holds the potential for beneficial, emancipatory and empowering technology design, development and deployment while also being at risk for concerns such as cooptation and conflation with other activities. We lay out these limitations and concerns and argue that as participatory AI/ML becomes in vogue, a contextual and nuanced understanding of the term as well as consideration of who the primary beneficiaries of participatory activities ought to be constitute crucial factors to realizing the benefits and opportunities that participation brings. CCS Concepts: • Human-centered computing → Interaction design theory, concepts and paradigms.

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Co-design and ethical artificial intelligence for health: An agenda for critical research and practice

Cited by 37 publications

References 111 publications

“Democratizing” artificial intelligence in medicine and healthcare: Mapping the uses of an elusive term

“Democratizing” artificial intelligence in medicine and healthcare: Mapping the uses of an elusive term

The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review

Power to the People? Opportunities and Challenges for Participatory AI

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