Co-design and Ethical Artificial Intelligence for Health

Donia, Joseph; Shaw, Jay

doi:10.1145/3461702.3462537

Cited by 11 publications

(8 citation statements)

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“…Many PD projects conceptualized participation as the involvement of individuals-often within formal organizations-with varying forms of creative input and research involvement [106]. In fact, many PD projects still engage individual-level feedback to inform design decisions, including in design workshops, which are often removed from local community sites and rely largely on researcher identification of individual participants [33,105,112].…”

Section: Background 21 Hci and Action Researchmentioning

confidence: 99%

A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing Research

Cooper

Horne

Hayes

et al. 2022

CHI Conference on Human Factors in Computing Systems

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HCI researchers have been gradually shifting attention from individual users to communities when engaging in research, design, and system development. However, our field has yet to establish a cohesive, systematic understanding of the challenges, benefits, and commitments of community-collaborative approaches to research. We conducted a systematic review and thematic analysis of 47 computing research papers discussing participatory research with communities for the development of technological artifacts and systems, published over the last two decades. From this review, we identified seven themes associated with the evolution of a project: from establishing community partnerships to sustaining results. Our findings suggest that several tensions characterize these projects, many of which relate to the power and position of researchers, and the computing research environment, relative to community partners. We discuss the implications of our findings and offer methodological proposals to guide HCI, and computing research more broadly, towards practices that center communities.

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Section: Background 21 Hci and Action Researchmentioning

confidence: 99%

A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing Research

Cooper

Horne

Hayes

et al. 2022

CHI Conference on Human Factors in Computing Systems

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show abstract

“…Deploying information technology to address corruption is often not successful because, in these environments, “systems are inefficient, administrative rationalities different, and enforcement structures generally weak” (Addo, 2021: 109). But it is also because information technology can amplify or create alternative opportunities for corruption, potentially of a much larger scale than the petty corruption prevalent in many African states (Addo, 2021; Donovan, 2015; Mutungi et al ., 2019). In fact, the changes or disruption to roles associated with information technology implementation is itself argued to lead to corruption (Elgohary and Abdelazyz, 2020).…”

Section: Integrity Of Recommendationsmentioning

confidence: 99%

Digital discretion and public administration in Africa: Implications for the use of artificial intelligence

Plantinga

2022

Information Development

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African public administrations are in an especially difficult position with respect to the adoption of artificial intelligence (AI), in both how they harness its benefits but also in managing potential harms. Through a narrative review of existing research, this paper synthesises findings from previous digital government implementations on the African continent, and considers the implications for AI use. The analysis is centred on the way public officials are bypassed or empowered to work with emerging technologies, and the influence of public service values on their adoption. Four concerns emerge as relevant: Integrity of recommendations provided by decision-support systems, including how they are influenced by local organisational practices and the reliability of underlying infrastructures; Inclusive decision-making that balances the (assumed) objectivity of data-driven algorithms and the influence of different stakeholder groups; Exception and accountability in how digital and AI platforms are funded, developed, implemented and used; and an expectation of Complete understanding of people and events through the integration of traditionally dispersed data sources and systems, and how policy actors seek to mitigate the risks associated with this aspiration.

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“…Following prior work, I refer to AI as "the use of digital technology to create systems capable of performing tasks commonly thought to require intelligence" and will follow the common practice of using the terms 'machine learning' (ML) and 'artificial intelligence' (AI) interchangeably [10,22]. I refer to machine learning open source software as computer software released under an open source license that is designed specifically with machine learning use cases in mind.…”

Section: Defining Machine Learning Open Source Software (Mloss)mentioning

confidence: 99%

How Open Source Machine Learning Software Shapes AI

Langenkamp

Yue

2022

Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

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If we want a future where AI serves a plurality of interests, then we should pay attention to the factors that drive its success. While others have studied the importance of data, hardware, and models in directing the trajectory of AI, I argue that open source software is a neglected factor shaping AI as a discipline. I start with the observation that almost all AI research and applications are built on machine learning open source software (MLOSS). This thesis presents four contributions. First, it quantifies the outsized impact of MLOSS by using Github contributions data. By contrasting the costs of MLOSS and its economic benefits, I find that the average dollar of MLOSS investment corresponds to at least $100 of global economic value created, corresponding to $30B of economic value created this year. Second, I leverage interviews with AI researchers and developers to develop a causal model of the effect of open sourcing on economic value. I argue that open sourcing creates value through three primary mechanisms: standardization of MLOSS tools, increased experimentation in AI research, and creation of commuities. Third, I analyze the various incentives behind MLOSS by examining three key factors: business strategy, sociotechnical factors, and ideological motivations. In the last section, I explore how MLOSS may help us understand the future of AI and make a number of probabilistic predictions. I intend this thesis to be useful for technologists and academics who want to analyze and critique AI, and policymakers who want to better understand and regulate AI systems.

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Co-design and Ethical Artificial Intelligence for Health

Cited by 11 publications

References 0 publications

A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing Research

A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing Research

Digital discretion and public administration in Africa: Implications for the use of artificial intelligence

How Open Source Machine Learning Software Shapes AI

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