2023
DOI: 10.1177/20539517231173901
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Stepping back from Data and AI for Good – current trends and ways forward

Abstract: 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 acad… Show more

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Cited by 5 publications
(1 citation statement)
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References 39 publications
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“…Digital humanities researchers are increasingly aware of the environmental footprint of their activities (Digital Humanities Climate Coalition, Information, Measurement and Practice IMP Action Group, 2022), while historians recognise the need to debate and research climate change (McNeill, 2016). This follows climate-conscious initiatives prioritising efficient hardware and algorithms, such as SustainNLP (Bender et al ., 2021, p. 612), and projects associated with “AI for Good”, using computation to solve societal problems (Aula and Bowles, 2023, p. 5). However, methodologies are needed to calculate the carbon footprint of ML research activities (Lacoste et al ., 2019), beyond carbon offsetting (Passalacqua, 2021), speaking to existing debates around climate and personal-corporate responsibility (Cuomo, 2011).…”
Section: Resultsmentioning
confidence: 99%
“…Digital humanities researchers are increasingly aware of the environmental footprint of their activities (Digital Humanities Climate Coalition, Information, Measurement and Practice IMP Action Group, 2022), while historians recognise the need to debate and research climate change (McNeill, 2016). This follows climate-conscious initiatives prioritising efficient hardware and algorithms, such as SustainNLP (Bender et al ., 2021, p. 612), and projects associated with “AI for Good”, using computation to solve societal problems (Aula and Bowles, 2023, p. 5). However, methodologies are needed to calculate the carbon footprint of ML research activities (Lacoste et al ., 2019), beyond carbon offsetting (Passalacqua, 2021), speaking to existing debates around climate and personal-corporate responsibility (Cuomo, 2011).…”
Section: Resultsmentioning
confidence: 99%