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2021
DOI: 10.1111/soc4.12851
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Sociological perspectives on artificial intelligence: A typological reading

Abstract: Interest in applying sociological tools to analysing the social nature, antecedents and consequences of artificial intelligence (AI) has been rekindled in recent years. However, for researchers new to this field of enquiry, navigating the expansive literature can be challenging. This paper presents a practical way to help these researchers to think about, search and read the literature more effectively. It divides the literature into three categories. Research in each category is informed by one analytic persp… Show more

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Cited by 35 publications
(31 citation statements)
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References 62 publications
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“…The need for a sociological conception of AI lies in the missing link to consistent social sciences empirical studies. Existing research is predominantly driven by the technical possibility (machine learning and neural network) applied to social and economic phenomena rather than being spurred by theoretically grounded research questions (Liu, 2021). To be more explicit, in the former case AI systems are more probably blind to social complexity (inequality, diversity, power structures) while in the latter sociological possibilities could inform and drive technological development.…”
Section: A Sociology For Aimentioning
confidence: 99%
“…The need for a sociological conception of AI lies in the missing link to consistent social sciences empirical studies. Existing research is predominantly driven by the technical possibility (machine learning and neural network) applied to social and economic phenomena rather than being spurred by theoretically grounded research questions (Liu, 2021). To be more explicit, in the former case AI systems are more probably blind to social complexity (inequality, diversity, power structures) while in the latter sociological possibilities could inform and drive technological development.…”
Section: A Sociology For Aimentioning
confidence: 99%
“…Social science research on inequality and ADM systems as well as interactions between algorithms and humans goes far beyond what we were able to cover here (e.g. Joyce et al, 2021;Liu, 2021). Other challenges range from, for example, accounting for the agency of algorithms (Lange et al, 2019), social and political challenges with respect to regulation (Mittelstadt, 2019), privacy (Anthony et al, 2017), and governance (Danaher et al, 2017), to artificial intelligence shifting power relationships (Kalluri, 2020), or other social impacts beyond inequality outcomes.…”
Section: Discussionmentioning
confidence: 92%
“…Scholars from various disciplines have called for examining algorithmic outcomes to avoid or mitigate undesired consequences of ADM (Kusner and Loftus, 2020; Zou and Schiebinger, 2018). Previous research from computer science (Mehrabi et al, 2019), legal studies (Wachter, 2020), and philosophical (Mittelstadt et al, 2016) perspectives discussed algorithmic, structural, and ethical problems with ADM. Joyce et al (2021) and Liu (2021) provide a general overview of sociological perspectives on related artificial intelligence.…”
Section: Introductionmentioning
confidence: 99%
“…Yapay zekâya ilişkin yakın dönemde sosyoloji içinde ilginin hızlandığı söylenebilir (Liu, 2021). Yapay zekâ üzerine sosyoloji içerisindeki tartışma ve çalışmaları iki genel paradigma içinde sınıflandırmak mümkündür: Birincisi, yapay zekâyı toplumsal ve kültürel bir inşa, toplumsal bir sorun alanı olarak ele alan hümanist (insan-merkezci) yaklaşım, ikincisi ise genelde teknolojiyi özelde ise yapay zekânın toplumsal bir fail olarak düşünülmesi gerektiğine vurgu yapan post-fenomonoloji ve Latouryen sosyolojinin etkisindeki post-humanist yaklaşım.…”
Section: Yapay Zekâ Ve Sosyolojiunclassified