The extent of algorithm aversion in decision-making situations with varying gravity

Area of Science:

• Decision Science
• Human-Computer Interaction
• Behavioral Economics

Background:

• Algorithms often outperform human experts in reliability.
• A notable segment of the population exhibits aversion towards algorithmic decision-making.
• The impact of decision consequences on algorithm aversion is not fully understood.

Purpose of the Study:

• To investigate the relationship between the consequences of decision-making situations and the prevalence of algorithm aversion.
• To quantify how decision severity influences the tendency to avoid algorithmic tools.

Main Methods:

• A framing experiment was conducted to simulate decision-making scenarios.
• Participants' aversion towards algorithms was measured across situations with varying consequence levels.

Main Results:

• Algorithm aversion frequency significantly increases as the potential consequences of a decision become more severe.
• This effect is particularly pronounced in high-stakes decision-making contexts.

Conclusions:

• The tragedy of algorithm aversion is evident: increased aversion in critical situations leads to a reduced probability of success.
• Understanding and mitigating algorithm aversion is crucial for optimizing outcomes in important decision-making processes.