Paper Number
1762
Paper Type
Short Paper
Abstract
The increased adoption of algorithmic solutions into existing pathways within and around organizational settings can provide opportunities for improving outcomes by enabling experts to delegate tasks to such solutions. The extant literature on the topic of human-algorithm interactions, however, paradoxically suggests that experts tend to be reluctant when it comes to delegating low-complexity tasks to algorithmic solutions, while their intention to delegate increases for high-complexity ones. We refer to this phenomenon as the ‘delegation paradox’ and we attempt to unpack it by drawing upon the theories of algorithm aversion as well as algorithm appreciation. In doing so, we suggest bringing forward a mid-range theory of experts’ task delegation to algorithmic solutions, while we distill a set of propositions. We discuss the implications of our work for theory as well as practice, and we delineate an agenda for future research on the topic.
Recommended Citation
Gante, Stefanie and Angelopoulos, Spyros, "Delegation Paradox: How Complexity Impacts Task Delegation to Algorithmic Solutions" (2024). ECIS 2024 Proceedings. 8.
https://aisel.aisnet.org/ecis2024/track19_hci/track19_hci/8
Delegation Paradox: How Complexity Impacts Task Delegation to Algorithmic Solutions
The increased adoption of algorithmic solutions into existing pathways within and around organizational settings can provide opportunities for improving outcomes by enabling experts to delegate tasks to such solutions. The extant literature on the topic of human-algorithm interactions, however, paradoxically suggests that experts tend to be reluctant when it comes to delegating low-complexity tasks to algorithmic solutions, while their intention to delegate increases for high-complexity ones. We refer to this phenomenon as the ‘delegation paradox’ and we attempt to unpack it by drawing upon the theories of algorithm aversion as well as algorithm appreciation. In doing so, we suggest bringing forward a mid-range theory of experts’ task delegation to algorithmic solutions, while we distill a set of propositions. We discuss the implications of our work for theory as well as practice, and we delineate an agenda for future research on the topic.
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