Event Title
Algorithmic Decision-Making Systems: A Conceptualization and Agenda for Green IS Research
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Paper Number
2123
Paper Type
Complete
Description
Algorithmic decision-making systems (ADMSs), consisting of the two distinct but related concepts of artificial intelligence (AI) and big data analytics (BDA), represent the most current computing advances for decision-making. ADMSs are associated with significant opportunities and challenges in a wide range of high-impact application areas. However, the conceptual confusion around ADMSs limits information systems (IS) research in comprehensively studying them and their impacts within a clearly defined cumulative tradition. This literature review develops an inclusive conceptualization of ADMS through the ideas of AI and BDA to mitigate such shortcomings. The conceptualization of ADMS is inductively generated following a grounded theory approach used to analyze the content of 54 IS articles. The resulting conceptualization includes eleven key aspects representing the intricate socio-technical nature of current computing processes for decision-making. Lastly, a green IS research agenda is proposed to illustrate the applicability of the ADMS conceptualization to IS scholarship.
Recommended Citation
Grenier Ouimet, Antoine and Addas, Shamel, "Algorithmic Decision-Making Systems: A Conceptualization and Agenda for Green IS Research" (2022). ICIS 2022 Proceedings. 14.
https://aisel.aisnet.org/icis2022/ai_business/ai_business/14
Algorithmic Decision-Making Systems: A Conceptualization and Agenda for Green IS Research
Algorithmic decision-making systems (ADMSs), consisting of the two distinct but related concepts of artificial intelligence (AI) and big data analytics (BDA), represent the most current computing advances for decision-making. ADMSs are associated with significant opportunities and challenges in a wide range of high-impact application areas. However, the conceptual confusion around ADMSs limits information systems (IS) research in comprehensively studying them and their impacts within a clearly defined cumulative tradition. This literature review develops an inclusive conceptualization of ADMS through the ideas of AI and BDA to mitigate such shortcomings. The conceptualization of ADMS is inductively generated following a grounded theory approach used to analyze the content of 54 IS articles. The resulting conceptualization includes eleven key aspects representing the intricate socio-technical nature of current computing processes for decision-making. Lastly, a green IS research agenda is proposed to illustrate the applicability of the ADMS conceptualization to IS scholarship.
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