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== Proposed methods to overcome algorithm aversion ==
Algorithms are often capable of outperforming humans or performing tasks much more cost-effectively.<ref>{{Cite journal |last1=Dietvorst |first1=Berkeley J. |last2=Simmons |first2=Joseph P. |last3=Massey |first3=Cade |date=2015 |title=Algorithm aversion: People erroneously avoid algorithms after seeing them err. |url=https://doi.apa.org/doi/10.1037/xge0000033 |journal=Journal of Experimental Psychology: General |language=en |volume=144 |issue=1 |pages=114–126 |doi=10.1037/xge0000033 |pmid=25401381 |issn=1939-2222|url-access=subscription }}</ref><ref>{{Cite journal |last1=Yeomans |first1=Michael |last2=Shah |first2=Anuj |last3=Mullainathan |first3=Sendhil |last4=Kleinberg |first4=Jon |date=October 2019 |title=Making sense of recommendations |url=https://onlinelibrary.wiley.com/doi/10.1002/bdm.2118 |journal=Journal of Behavioral Decision Making |language=en |volume=32 |issue=4 |pages=403–414 |doi=10.1002/bdm.2118 |issn=0894-3257|url-access=subscription }}</ref> Despite this, algorithm aversion persists due to a range of psychological, cultural, and design-related factors. To mitigate resistance and build trust, researchers and practitioners have proposed several strategies.
=== Human-in-the-loop ===
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