Algorithm aversion: Difference between revisions

Revision as of 05:36, 17 September 2021

Revision as of 17:35, 17 September 2021

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'''Algorithm aversion''' is "biased assessment of an algorithm which manifests in negative behaviours and attitudes towards the algorithm compared to a human agent."<ref name=":0">{{Cite journal|last1=Jussupow|first1=Ekaterina|last2=Benbasat|first2=Izak|last3=Heinzl|first3=Armin|date=2020|title=Why Are We Averse Towards Algorithms ? A Comprehensive Literature Review on Algorithm Aversion|url=https://aisel.aisnet.org/ecis2020_rp/168/|journal=Twenty-Eighth European Conference on Information Systems (ECIS2020)|pages=1–16}}</ref> It describes a phenomenon where humans reject advice from an algorithm in a case where they would accept the same advice if they thought it was coming from another human.

[[Algorithm|Algorithms]]s, such as those employing [[machine learning]] methods or various forms of [[artificial intelligence]], are commonly used to provide recommendations or advice to human decisionmakers. For example, [[Recommender system|recommender systems]] are used in [[E-commerce]] to identify products a customer might like, and [[Artificial intelligence in healthcare|artificial intelligence is used in healthcare]] to assist with diagnosis and treatment decisions. However, humans sometimes appear to resist or reject these algorithmic recommendations more than if the recommendation had come from a human. Notably, algorithms are often capable of outperforming humans, so rejecting algorithmic advice can result in poor performance or suboptimal outcomes.

This is an emerging topic and it is not completely clear why or under what circumstances people will display algorithm aversion. In some cases, people seem to be more likely to take recommendations from an algorithm than from a human, a phenomenon called ''algorithm appreciation''.<ref name=":1">{{Cite journal|date=2019-03-01|title=Algorithm appreciation: People prefer algorithmic to human judgment|url=https://www.sciencedirect.com/science/article/abs/pii/S0749597818303388|journal=Organizational Behavior and Human Decision Processes|language=en|volume=151|pages=90–103|doi=10.1016/j.obhdp.2018.12.005|issn=0749-5978|last1=Logg|first1=Jennifer M.|last2=Minson|first2=Julia A.|last3=Moore|first3=Don A.}}</ref>

== Examples of Algorithmalgorithm Aversionaversion ==

Algorithm aversion has been studied in a wide variety of contexts. For example, people seem to prefer recommendations for jokes from a human rather than from an algorithm,<ref name=":2">{{Cite journal|last1=Yeomans|first1=Michael|last2=Shah|first2=Anuj|last3=Mullainathan|first3=Sendhil|last4=Kleinberg|first4=Jon|date=2019|title=Making sense of recommendations|url=https://onlinelibrary.wiley.com/doi/abs/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=1099-0771}}</ref> and would rather rely on a human to predict the number of airline passengers from each US state instead of an algorithm.<ref name=":3">{{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=http://doi.apa.org/getdoi.cfm?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}}</ref> People also seem to prefer medical recommendations from human doctors instead of an algorithm.{{Citation needed|date=September 2021}}

== Factors Affectingaffecting Algorithmalgorithm Aversionaversion ==

Various frameworks have been proposed to explain the causes for algorithm aversion and techniques or system features that might help reduce aversion.<ref name=":0" /><ref>{{Cite journal|last1=Burton|first1=Jason W.|last2=Stein|first2=Mari-Klara|last3=Jensen|first3=Tina Blegind|date=2020|title=A systematic review of algorithm aversion in augmented decision making|url=https://onlinelibrary.wiley.com/doi/abs/10.1002/bdm.2155|journal=Journal of Behavioral Decision Making|language=en|volume=33|issue=2|pages=220–239|doi=10.1002/bdm.2155|issn=1099-0771}}</ref>

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Overall, people tend to judge machines more critically than they do humans.<ref>{{Cite book|last=Hidalgo|first=Cesar|title=How Humans Judge Machines|publisher=[[MIT Press]]|year=2021|isbn=978-0-262-04552-0|___location=Cambridge, MA}}</ref> Several system characteristics or factors have been shown to influence how people evaluate algorithms.

==== Algorithm Process and the role of Systemsystem Transparencytransparency ====

One reason people display resistance to algorithms is a lack of understanding about how the algorithm is arriving at its recommendation.<ref name=":2" /> People also seem to have a better intuition for how another human would make recommendations. Whereas people assume that other humans will account for unique differences between situations, they sometimes perceive algorithms as incapable of considering individual differences and resist the algorithms accordingly.<ref>{{Cite journal|last1=Longoni|first1=Chiara|last2=Bonezzi|first2=Andrea|last3=Morewedge|first3=Carey K|date=2019-05-03|title=Resistance to Medical Artificial Intelligence|url=https://doi.org/10.1093/jcr/ucz013|journal=Journal of Consumer Research|volume=46|issue=4|pages=629–650|doi=10.1093/jcr/ucz013|issn=0093-5301|doi-access=free}}</ref>

==== Decision Domain___domain ====

People are generally skeptical that algorithms can make accurate predictions in certain areas, particularly if task involves a seemingly human characteristic like [[morals]] or [[empathy]]. Algorithm aversion tends to be higher when the task is more subjective and lower on tasks that are objective or quantifiable.<ref name=":0" />

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[[Digital natives]] are younger and have known technology their whole lives, while digital immigrants have not. Age is a commonly-cited factor hypothesized to affect whether or not people accept algorithmic recommendations. For example, one study found that trust in an algorithmic financial advisor was lower among older people compared with younger study participants.<ref>{{Cite journal|date=2020-02-01|title=Whose Algorithm Says So: The Relationships Between Type of Firm, Perceptions of Trust and Expertise, and the Acceptance of Financial Robo-Advice|url=https://www.sciencedirect.com/science/article/pii/S1094996819301112|journal=Journal of Interactive Marketing|language=en|volume=49|pages=107–124|doi=10.1016/j.intmar.2019.10.003|issn=1094-9968|hdl=1765/123799|hdl-access=free}}</ref> However, other research has found that algorithm aversion does not vary with age.<ref name=":1" />

== Proposed Methodsmethods to Overcomeovercome Algorithmalgorithm Aversionaversion ==

Algorithms are often capable of outperforming humans or performing tasks much more cost-effectively.<ref name=":3" /><ref name=":2" />

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Providing explanations about how algorithms work has been shown to reduce aversion. These explanations can take a variety of forms, including about how the algorithm as a whole works, about why it is making a particular recommendation in a specific case, or how confident it is in its recommendation.<ref name=":0" />

=== User Trainingtraining ===

Algorithmic recommendations represent a new type of information in many fields. For example, a medical AI diagnosis of a [[Infection|bacterial infection]] is different than a lab test indicating the presence of a bacteria.