Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time

Date
2023-01-03
Authors
Leffrang, Dirk
Bösch, Kevin
Müller, Oliver
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4016
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Abstract
Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners.
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Human-centricity in a Sustainable Digital Economy, advice taking, algorithm aversion, decision making, forecasting, time series
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10
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Related To
Proceedings of the 56th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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