Abstract
AbstractDifferent people have different perceptions about artificial intelligence (AI). It is extremely important to bring together all the alternative frames of thinking—from the various communities of developers, researchers, business leaders, policymakers, and citizens—to properly start acknowledging AI. This article highlights the ‘fruitful collaboration’ that sociology and AI could develop in both social and technical terms. We discuss how biases and unfairness are among the major challenges to be addressed in such a sociotechnical perspective. First, as intelligent machines reveal their nature of ‘magnifying glasses’ in the automation of existing inequalities, we show how the AI technical community is calling for transparency and explainability, accountability and contestability. Not to be considered as panaceas, they all contribute to ensuring human control in novel practices that include requirement, design and development methodologies for a fairer AI. Second, we elaborate on the mounting attention for technological narratives as technology is recognized as a social practice within a specific institutional context. Not only do narratives reflect organizing visions for society, but they also are a tangible sign of the traditional lines of social, economic, and political inequalities. We conclude with a call for a diverse approach within the AI community and a richer knowledge about narratives as they help in better addressing future technical developments, public debate, and policy. AI practice is interdisciplinary by nature and it will benefit from a socio-technical perspective.
Funder
Knut och Alice Wallenbergs Stiftelse
Horizon 2020 Framework Programme
Horizon 2020
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Computer Science Applications
Reference121 articles.
1. Abebe, R., Barocas, S., Kleinberg, J., Levy, K., Raghavan, M., and Robinson, D.G. (2020). Roles for computing in social computing in social change. In: Conference on Fairness, Accountability, and Transparency (FAT* ‘20)
2. Adams, R. (2020). Helen A’Loy and other tales of female automata: A gendered reading of the narratives of hopes and fears of intelligent machines and artificial intelligence. AI & Society, 35, 569–579. https://doi.org/10.1007/s00146-019-00918-7
3. Aggarwal, N. (2020). The norms of algorithmic credit scoring. Cambridge Law Journal. https://doi.org/10.2139/ssrn.3569083
4. Albright, B. (2019). If you give a judge a risk score: Evidence from Kentucky bail decisions. Retrieved from https://thelittledataset.com/about_files/albright_judge_score.pdf
5. Aler Tubella, A., Theodorou, A., Dignum, F., and Dignum, V. (2019). Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI). DOI: https://doi.org/10.24963/ijcai.2019/802
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