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Algorithm aversion
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<p>Algorithm aversion is defined as a "biased assessment of an algorithm which manifests in negative behaviors and attitudes towards the algorithm compared to a human agent." This phenomenon describes the tendency of humans to reject advice or recommendations from an algorithm in situations where they would accept the same advice if it came from a human. </p><p><a href="/facts/Algorithm/fnl5NmRt">Algorithms</a>, particularly those utilizing <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a> methods or <a href="/facts/Artificial_intelligence/lJGauQwX">artificial intelligence</a> (AI), play a growing role in decision-making across various fields. Examples include recommender systems in <a href="/facts/E-commerce/7vxeea0Y">e-commerce</a> for identifying products a customer might like and AI systems in healthcare that assist in diagnoses and treatment decisions. Despite their proven ability to outperform humans in many contexts, algorithmic recommendations are often met with resistance or rejection, which can lead to inefficiencies and suboptimal outcomes. </p><p>The study of algorithm aversion is critical as algorithms become increasingly embedded in our daily lives. Factors such as perceived accountability, lack of transparency, and skepticism towards machine judgment contribute to this aversion. Conversely, there are scenarios where individuals are more likely to trust and follow algorithmic advice over human recommendations, a phenomenon referred to as algorithm appreciation. Understanding these dynamics is essential for improving human-algorithm interactions and fostering greater acceptance of AI-driven decision-making. </p>