‘AI for Social Good’: Whose Good and Who’s Good? Introduction to the Special Issue on Artificial Intelligence for Social Good

Cowls, Josh

doi:10.1007/s13347-021-00466-3

Cited by 18 publications

(9 citation statements)

References 5 publications

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“…ITU work on AI for good is therefore a direct extension of earlier UN work on ICT4D. The move to position SDGs as the thematic foundation of AI for Good has also been adopted by academic promoters (Cowls et al, 2021; Tomasev et al, 2020) and private corporate actors (Chui et al 2018).…”

Section: Transitions From Ict4d To Ai For Goodmentioning

confidence: 99%

“…The arguments in this approach originate from the wider discussion on information ethics and ethical data science, making Data and AI initiatives just a small area of the overall discussion. For example, the special issue on AI for Social Good in Philosophy and Technology edited by Cowls (2021) treats the subject matter only as a loose umbrella for philosophical debates. In more direct contributions to the pursuit of social good, researchers have proposed pre-defined ethical principles (Floridi et al, 2020), and practical questions to guide ethical reflection (Berendt, 2019(Berendt, , 2020.…”

Section: Promotional Literaturementioning

confidence: 99%

“…Both the promotional literature and the prescriptive analyses of normativity and ethics are trying to create new formations at the intersection of technology and society. Scholars like Green (2020) and Cowls (2021) are keenly aware of political dilemmas and try to find ways forward from the current challenges, and their work is paralleled by the wider literature on AI ethics. We argue that it would be a mistake to dismiss the ability of such works to create new practices and dynamics.…”

Section: Ways Forward For Researchmentioning

confidence: 99%

See 2 more Smart Citations

Stepping back from Data and AI for Good – current trends and ways forward

Aula

Bowles

2023

Big Data & Society

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Various ‘Data for Good’ and ‘AI for Good’ initiatives have emerged in recent years to promote and organise efforts to use new computational techniques to solve societal problems. The initiatives exercise ongoing influence on how the capabilities of computational techniques are understood as vehicles of social and political change. This paper analyses the development of the initiatives from a rhetorical slogan into a research program that understands itself as a ‘field’ of applications. It discusses recent academic literature on the topic to show a problematic entanglement between the promotion of initiatives and prescriptions of what ‘good’ ought to be. In contrast, we call researchers to take a practical and analytical step back. The paper provides a framework for future research by calling for descriptive research on the composition of the initiatives and critical research that draws from broader social science debates on computational techniques. The empirical part of the paper provides first steps towards this direction by positioning Data and AI for Good initiatives as part of a single continuum and situating it within a historical trajectory that has its immediate precursor in ICT for Development initiatives.

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Section: Transitions From Ict4d To Ai For Goodmentioning

confidence: 99%

Section: Promotional Literaturementioning

confidence: 99%

Section: Ways Forward For Researchmentioning

confidence: 99%

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Stepping back from Data and AI for Good – current trends and ways forward

Aula

Bowles

2023

Big Data & Society

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“…Furthermore, creating larger and larger datasets is relatively cheap, but the process of filtering those datasets or 'detoxifying' the models trained on them is expensive (Birhane et al, 2021b;Welbl et al, 2021;Xu et al, 2021). In addition, even when these changes in the direction of 'more ethical' or for a 'common good' are well-intentioned, the lack of conceptual clarity surrounding the targets of such change-i.e., considering what it means to 'be ethical' in the first place-will only compound the issue (Taylor, 2016;Green, 2019;Moore, 2019;Cowls, 2021).…”

Section: )mentioning

confidence: 99%

Metaethical Perspectives on 'Benchmarking' AI Ethics

LaCroix¹,

Luccioni²

2022

Preprint

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Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research and have been developed for a variety of tasks ranging from question answering to facial recognition. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. In this paper, drawing upon research in moral philosophy and metaethics, we argue that it is impossible to develop such a benchmark. As such, alternative mechanisms are necessary for evaluating whether an AI system is 'ethical'. This is especially pressing in light of the prevalence of applied, industrial AI research. We argue that it makes more sense to talk about 'values' (and 'value alignment') rather than 'ethics' when considering the possible actions of present and future AI systems. We further highlight that, because values are unambiguously relative, focusing on values forces us to consider explicitly what the values are and whose values they are. Shifting the emphasis from ethics to values therefore gives rise to several new ways of understanding how researchers might advance research programmes for robustly safe or beneficial AI. We conclude by highlighting a number of possible ways forward for the field as a whole, and we advocate for different approaches towards more value-aligned AI research.

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“…2019; Zuboff, 2019;Danaher, 2019;Couldry & Mejias, 2019;Coeckelbergh, 2020;Véliz, 2020;Berberich et al, 2020;Mohamed et al, 2020;Bartoletti, 2020;Crawford, 2021;Santoni de Sio & Mecacci, 2021;Cowls, 2021). However, less attention has been paid to the philosophical nature of AI.…”

Section: Introductionmentioning

confidence: 99%

Time Machines: Artificial Intelligence, Process, and Narrative

Coeckelbergh

2021

Philos. Technol.

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While today there is much discussion about the ethics of artificial intelligence (AI), less work has been done on the philosophical nature of AI. Drawing on Bergson and Ricoeur, this paper proposes to use the concepts of time, process, and narrative to conceptualize AI and its normatively relevant impact on human lives and society. Distinguishing between a number of different ways in which AI and time are related, the paper explores what it means to understand AI as narrative, as process, or as the emergent outcome of processes and narratives. It pays particular attention to what it calls the “narrator” and “time machine” roles of AI and the normative implications of these roles. It argues that AI processes and narratives shape our time and link past, present, and future in particular ways that are ethically and politically significant.

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‘AI for Social Good’: Whose Good and Who’s Good? Introduction to the Special Issue on Artificial Intelligence for Social Good

Abstract: This introduction sets out the aims and scope of the Special Issue and provides an overview of each of the research articles and commentaries that follow.

Cited by 18 publications

References 5 publications

Stepping back from Data and AI for Good – current trends and ways forward

Stepping back from Data and AI for Good – current trends and ways forward

Metaethical Perspectives on 'Benchmarking' AI Ethics

Time Machines: Artificial Intelligence, Process, and Narrative

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