Human preferences toward algorithmic advice in a word association task

Abstract

AbstractAlgorithms provide recommendations to human decision makers across a variety of task domains. For many problems, humans will rely on algorithmic advice to make their choices and at times will even show complacency. In other cases, humans are mistrustful of algorithmic advice, or will hold algorithms to higher standards of performance. Given the increasing use of algorithms to support creative work such as text generation and brainstorming, it is important to understand how humans will respond to algorithms in those scenarios—will they show appreciation or aversion? This study tests the effects of algorithmic advice for a word association task, the remote associates test (RAT). The RAT task is an established instrument for testing critical and creative thinking with respect to multiple word association. We conducted a preregistered online experiment (154 participants, 2772 observations) to investigate whether humans had stronger reactions to algorithmic or crowd advice when completing multiple instances of the RAT. We used an experimental format in which subjects see a question, answer the question, then receive advice and answer the question a second time. Advice was provided in multiple formats, with advice varying in quality and questions varying in difficulty. We found that individuals receiving algorithmic advice changed their responses 13$$\%$$ % more frequently ($$\chi ^{2} = 59.06$$ χ 2 = 59.06 , $$p < 0.001$$ p < 0.001 ) and reported greater confidence in their final solutions. However, individuals receiving algorithmic advice also were 13$$\%$$ % less likely to identify the correct solution ($$\chi ^{2} = 58.79$$ χ 2 = 58.79 , $$p < 0.001$$ p < 0.001 ). This study highlights both the promises and pitfalls of leveraging algorithms to support creative work.