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Automation bias is the propensity for humans to favor suggestions from automated decision-making systems and to ignore contradictory information made without automation, even if it is correct. [1] Automation bias stems from the social psychology literature that found a bias in human-human interaction that showed that people assign more positive ...
Automated decision-making involves using data as input to be analyzed within a process, model, or algorithm or for learning and generating new models. [7] ADM systems may use and connect a wide range of data types and sources depending on the goals and contexts of the system, for example, sensor data for self-driving cars and robotics, identity data for security systems, demographic and ...
Quality Control: Investigate if a manufactured product adheres to a defined set of quality criteria. Reliability : Quantify the reliability of test scores. Robust T-Tests : Robustly evaluate the difference between two means.
Automation bias, the tendency to depend excessively on automated systems which can lead to erroneous automated information overriding correct decisions. [54] Gender bias, a widespread [55] set of implicit biases that discriminate against a gender. For example, the assumption that women are less suited to jobs requiring high intellectual ability.
Cognitive bias mitigation is the prevention and reduction of the negative effects of cognitive biases – unconscious, automatic influences on human judgment and decision making that reliably produce reasoning errors. Coherent, comprehensive theories of cognitive bias mitigation are lacking.
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Bias can enter into algorithmic systems as a result of pre-existing cultural, social, or institutional expectations; by how features and labels are chosen; because of technical limitations of their design; or by being used in unanticipated contexts or by audiences who are not considered in the software's initial design. [4]
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