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Conceptualizing Automated Decision-Making in Organizational Contexts.

Anna Katharina Boos¹

¹Department of Philosophy, University of Zurich, Zurich, Switzerland.

Philosophy & Technology

|July 19, 2024

Summary

Automated decision-making (ADM) involves using algorithms for practical, action-triggering choices in organizations, not just cognitive ones. This clarifies ADM types like suggesting, offloading, or superseding human decisions.

Keywords:

Algorithmic Decision Making (ADM)Artificial Intelligence (AI)Conceptual Analysis Definition of ADM Human-Computer Interaction (HCI)Socio-Technical Systems (STS)

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Area of Science:

Organizational Studies
Information Systems
Decision Science

Background:

Growing interest in automated decision-making (ADM) necessitates conceptual clarity.
Existing research lacks a clear definition and demarcation of ADM within organizational contexts.

Purpose of the Study:

To develop a conceptualization of ADM tailored for organizational settings.
To distinguish ADM from similar algorithm-supported practices.
To categorize different types of ADM.

Main Methods:

Conceptual analysis of decision-making processes.
Distinguishing between cognitive decisions (p) and practical decisions (φ).
Examining the algorithm's role in translating cognitive outputs into actionable decisions.

Main Results:

ADM is defined as using algorithms to solve decision problems, focusing on practical, action-triggering choices (φ).
Algorithmic tools primarily address cognitive problems (p), but become ADM when their output triggers action.
Three ADM types are identified: suggesting, offloading, and superseding human decisions.

Conclusions:

A clear conceptualization of ADM is established for organizational contexts.
The p-φ relationship is key to understanding and classifying ADM.
This framework differentiates ADM from other algorithm-supported practices.