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Algorithms are less trusted for tasks involving moral or emotional judgment, such as ethical dilemmas or empathetic decision-making. For example, individuals may reject algorithmic decisions in scenarios where they perceive moral stakes to be high, such as autonomous vehicle decisions or medical life-or-death situations.<ref>{{Cite journal |last1=Castelo |first1=Noah |last2=Ward |first2=Adrian F. |date=2021-12-20 |title=Conservatism predicts aversion to consequential Artificial Intelligence |journal=PLOS ONE |language=en |volume=16 |issue=12 |pages=e0261467 |doi=10.1371/journal.pone.0261467 |doi-access=free |issn=1932-6203 |pmc=8687590 |pmid=34928989|bibcode=2021PLoSO..1661467C }}</ref>
== Mechanisms
Algorithm aversion arises from a combination of psychological, task-related, cultural, and design-related factors. These mechanisms interact to shape individuals' negative perceptions and behaviors toward algorithms, even in cases where algorithmic performance is objectively superior to human decision-making.
=== Psychological
==== Perceived
Individuals often feel a heightened sense of accountability when using algorithmic advice compared to human advice. This stems from the belief that, if a decision goes wrong, they will be solely responsible because an algorithm lacks the capacity to share blame. By contrast, decisions made with human input are perceived as more collaborative, allowing for shared accountability. For example, users are less likely to rely on algorithmic recommendations in high-stakes domains like healthcare or financial advising, where the repercussions of errors are significant.<ref name=":7"/>
==== Locus of
People with an internal locus of control, who believe they have direct influence over outcomes, are more reluctant to trust algorithms. They may perceive algorithmic decision-making as undermining their autonomy, preferring human input that feels more modifiable or personal. Conversely, individuals with an external locus of control, who attribute outcomes to external forces, may accept algorithmic decisions more readily, viewing algorithms as neutral and effective tools. This tendency is particularly evident in decision-making contexts where users seek to maintain agency.<ref name=":5">{{Cite journal |last1=Mahmud |first1=Hasan |last2=Islam |first2=A. K. M. Najmul |last3=Ahmed |first3=Syed Ishtiaque |last4=Smolander |first4=Kari |date=2022-02-01 |title=What influences algorithmic decision-making? A systematic literature review on algorithm aversion |url=https://linkinghub.elsevier.com/retrieve/pii/S0040162521008210 |journal=Technological Forecasting and Social Change |volume=175 |pages=121390 |doi=10.1016/j.techfore.2021.121390 |issn=0040-1625|doi-access=free }}</ref>
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Neurotic individuals are more prone to anxiety and fear of uncertainty, making them less likely to trust algorithms. This aversion may be fueled by concerns about the perceived "coldness" of algorithms or their inability to account for nuanced emotional factors. For example, in emotionally sensitive tasks like healthcare or recruitment, neurotic individuals may reject algorithmic inputs in favor of human recommendations, even when the algorithm performs equally well or better.<ref>{{Cite journal |last1=Jussupow |first1=Ekaterina |last2=Benbasat |first2=Izak |last3=Heinzl |first3=Armin |date=2020-06-15 |title=WHY ARE WE AVERSE TOWARDS ALGORITHMS? A COMPREHENSIVE LITERATURE REVIEW ON ALGORITHM AVERSION |url=https://aisel.aisnet.org/ecis2020_rp/168/?utm_source=aisel.aisnet.org/ecis2020_rp/168&utm_medium=PDF&utm_campaign=PDFCoverPages |journal=ECIS 2020 Research Papers}}</ref>
=== Task-
==== Task
The nature of the task significantly influences algorithm aversion. For routine and low-risk tasks, such as recommending movies or predicting product preferences, users are generally comfortable relying on algorithms. However, for high-stakes or subjective tasks, such as making medical diagnoses, financial decisions, or moral judgments, algorithm aversion increases. Users perceive these tasks as requiring empathy, ethical reasoning, or nuanced understanding—qualities that they believe algorithms lack. This disparity highlights why algorithms are better received in technical fields (e.g., logistics) but face resistance in human-centric domains.<ref name=":6"/>
==== Outcome
People's reactions to algorithmic decisions are influenced by the nature of the decision outcome. When algorithms deliver positive results, users are more likely to trust and accept them. However, when outcomes are negative, users are more inclined to reject algorithms and attribute blame to their use. This phenomenon is linked to the perception that algorithms lack accountability, unlike human decision-makers, who can offer justifications or accept responsibility for failures.<ref name=":6" />
=== Cultural
==== Individualism vs.
Cultural norms significantly shape attitudes toward algorithmic decision-making. In individualistic cultures, such as the United States, people value autonomy and personalization, making them more skeptical of algorithmic systems that they perceive as impersonal or rigid. Conversely, in collectivist cultures like India, individuals are more likely to accept algorithmic recommendations, particularly when these systems align with group norms or social expectations. Familiarity with algorithms in collectivist societies also reduces aversion, as users view algorithms as tools to reinforce societal goals rather than threats to individual autonomy.<ref name=":7"/>
==== Cultural
Cultural norms and values significantly impact algorithm acceptance. Individualistic cultures, such as those in the United States, tend to display higher algorithm aversion due to an emphasis on autonomy, personal agency, and distrust of generalized systems. On the other hand, collectivist cultures, such as in India, exhibit greater acceptance of algorithms, particularly when familiarity is high and the decision aligns with societal norms. These differences highlight the importance of tailoring algorithmic systems to align with cultural expectations.<ref name=":7" />
==== Organizational
The role of organizations in supporting and explaining the use of algorithms can greatly influence aversion levels. When organizations actively promote algorithmic tools and provide training on their usage, employees are less likely to resist them. Transparency about how algorithms support decision-making processes fosters trust and reduces anxiety, particularly in high-stakes or workplace settings.<ref name=":0" />
=== Agency and
==== Advisory vs.
Algorithm aversion is higher for autonomous systems that make decisions independently (performative algorithms) compared to advisory systems that provide recommendations but allow humans to retain final decision-making power. Users tend to view advisory algorithms as supportive tools that enhance their control, whereas autonomous algorithms may be perceived as threatening to their authority or ability to intervene.<ref name=":0" />
==== Perceived
Algorithms are often perceived as lacking human-specific skills, such as empathy or moral reasoning. This perception leads to greater aversion in tasks involving subjective judgment, ethical dilemmas, or emotional interactions. Users are generally more accepting of algorithms in objective, technical tasks where human qualities are less critical.<ref name=":0" />
=== Social and
==== Expertise ====
In high-stakes or expertise-intensive tasks, users tend to favor human experts over algorithms. This preference stems from the belief that human experts can account for context, nuance, and situational complexity in ways that algorithms cannot. Algorithm aversion is particularly pronounced when humans with expertise are available as an alternative to the algorithm.<ref name=":0" />
==== Social
Users are more likely to reject algorithms when the alternative is their own input or the input of someone they know and relate to personally. In contrast, when the alternative is an anonymous or distant human agent, algorithms may be viewed more favorably. This preference for closer, more relatable human agents highlights the importance of perceived social connection in algorithmic decision acceptance.<ref name=":0" />
=== Design-
==== Transparency ====
A lack of transparency in algorithmic systems, often referred to as the "black box" problem, creates distrust among users. Without clear explanations of how decisions are made, users may feel uneasy relying on algorithmic outputs, particularly in high-stakes scenarios. For instance, transparency in medical AI systems—such as providing explanations for diagnostic recommendations—can significantly improve trust and reduce aversion. Transparent algorithms empower users by demystifying decision-making processes, making them feel more in control.<ref name=":5" />
==== Error
Users are generally less forgiving of algorithmic errors than human errors, even when the frequency of errors is lower for algorithms. This heightened scrutiny stems from the belief that algorithms should be "perfect" or error-free, unlike humans, who are expected to make mistakes. However, algorithms that demonstrate the ability to learn from their mistakes and adapt over time can foster greater trust. For example, users are more likely to accept algorithms in financial forecasting if they observe improvements based on feedback.<ref name=":5" />
==== Anthropomorphic
Designing algorithms with human-like traits, such as avatars, conversational interfaces, or relatable language, can reduce aversion by making interactions feel more natural and personal. For instance, AI-powered chatbots with empathetic communication styles are better received in customer service than purely mechanical interfaces. This design strategy helps mitigate the perception that algorithms are "cold" or impersonal, encouraging users to engage with them more comfortably.<ref name=":3" />
=== Delivery
==== Mode of
The format in which algorithms present their recommendations significantly affects user trust. Systems that use conversational or audio interfaces are generally more trusted than those relying solely on textual outputs, as they create a sense of human-like interaction.<ref>{{Citation |last1=Wischnewski |first1=Magdalena |title=Can AI Reduce Motivated Reasoning in News Consumption? Investigating the Role of Attitudes Towards AI and Prior-Opinion in Shaping Trust Perceptions of News |date=2022 |work=HHAI2022: Augmenting Human Intellect |pages=184–198 |url=https://ebooks.iospress.nl/doi/10.3233/FAIA220198 |access-date=2024-11-18 |publisher=IOS Press |doi=10.3233/faia220198 |last2=Krä |last3=Mer |first3=Nicole|series=Frontiers in Artificial Intelligence and Applications |isbn=978-1-64368-308-9 |doi-access=free }}</ref>
==== Presentation
Algorithms that provide clear, concise, and well-organized explanations of their recommendations are more likely to gain user acceptance. Systems that offer detailed yet accessible insights into their decision-making process are perceived as more reliable and trustworthy.<ref name=":5" />
=== General
==== Default
Many individuals harbor an ingrained skepticism toward algorithms, particularly when they lack familiarity with the system or its capabilities. Early negative experiences with algorithms can entrench this distrust, making it difficult to rebuild confidence. Even when algorithms perform better, this bias often persists, leading to outright rejection.<ref name=":7"/>
==== Favoritism
People often display a preference for human decisions over algorithmic ones, particularly for positive outcomes. Yalsin et al. highlighted that individuals are more likely to internalize favorable decisions made by humans, attributing success to human expertise or effort. In contrast, decisions made by algorithms are viewed as impersonal, reducing the sense of achievement or satisfaction. This favoritism contributes to a persistent bias against algorithmic systems, even when their performance matches or exceeds that of humans.<ref name=":6"/>
=== Reputational
People may also be averse to using algorithms if doing so conveys negative information about the human's ability.
== Proposed Methods to Overcome Algorithm Aversion ==
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