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      Computer-based personality judgments are more accurate than those made by humans.

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          Abstract

          Judging others' personalities is an essential skill in successful social living, as personality is a key driver behind people's interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants' Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.

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          Most cited references17

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          On the accuracy of personality judgment: a realistic approach.

          The "accuracy paradigm" for the study of personality judgment provides an important, new complement to the "error paradigm" that dominated this area of research for almost 2 decades. The present article introduces a specific approach within the accuracy paradigm called the Realistic Accuracy Model (RAM). RAM begins with the assumption that personality traits are real attributes of individuals. This assumption entails the use of a broad array of criteria for the evaluation of personality judgment and leads to a model that describes accuracy as a function of the availability, detection, and utilization of relevant behavioral cues. RAM provides a common explanation for basic moderators of accuracy, sheds light on how these moderators interact, and outlines a research agenda that includes the reintegration of the study of error with the study of accuracy.
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            Who knows what about a person? The self-other knowledge asymmetry (SOKA) model.

            This article tests a new model for predicting which aspects of personality are best judged by the self and which are best judged by others. Previous research suggests an asymmetry in the accuracy of personality judgments: Some aspects of personality are known better to the self than others and vice versa. According to the self-other knowledge asymmetry (SOKA) model presented here, the self should be more accurate than others for traits low in observability (e.g., neuroticism), whereas others should be more accurate than the self for traits high in evaluativeness (e.g., intellect). In the present study, 165 participants provided self-ratings and were rated by 4 friends and up to 4 strangers in a round-robin design. Participants then completed a battery of behavioral tests from which criterion measures were derived. Consistent with SOKA model predictions, the self was the best judge of neuroticism-related traits, friends were the best judges of intellect-related traits, and people of all perspectives were equally good at judging extraversion-related traits. The theoretical and practical value of articulating this asymmetry is discussed. Copyright 2009 APA, all rights reserved
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              An other perspective on personality: meta-analytic integration of observers' accuracy and predictive validity.

              The bulk of personality research has been built from self-report measures of personality. However, collecting personality ratings from other-raters, such as family, friends, and even strangers, is a dramatically underutilized method that allows better explanation and prediction of personality's role in many domains of psychology. Drawing hypotheses from D. C. Funder's (1995) realistic accuracy model about trait and information moderators of accuracy, we offer 3 meta-analyses to help researchers and applied psychologists understand and interpret both consistencies and unique insights afforded by other-ratings of personality. These meta-analyses integrate findings based on 44,178 target individuals rated across 263 independent samples. Each meta-analysis assessed the accuracy of observer ratings, as indexed by interrater consensus/reliability (Study 1), self-other correlations (Study 2), and predictions of behavior (Study 3). The results show that although increased frequency of interacting with targets does improve accuracy in rating personality, informants' interpersonal intimacy with the target is necessary for substantial increases in other-rating accuracy. Interpersonal intimacy improved accuracy especially for traits low in visibility (e.g., Emotional Stability) but only minimally for traits high in evaluativeness (e.g., Agreeableness). In addition, observer ratings were strong predictors of behaviors. When the criterion was academic achievement or job performance, other-ratings yielded predictive validities substantially greater than and incremental to self-ratings. These findings indicate that extraordinary value can gained by using other-reports to measure personality, and these findings provide guidelines toward enriching personality theory. Various subfields of psychology in which personality variables are systematically assessed and utilized in research and practice can benefit tremendously from use of others' ratings to measure personality variables.
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                Author and article information

                Journal
                Proc. Natl. Acad. Sci. U.S.A.
                Proceedings of the National Academy of Sciences of the United States of America
                1091-6490
                0027-8424
                Jan 27 2015
                : 112
                : 4
                Affiliations
                [1 ] Department of Psychology, University of Cambridge, Cambridge CB2 3EB, United Kingdom; and yw341@cam.ac.uk.
                [2 ] Department of Computer Science, Stanford University, Stanford, CA 94305.
                [3 ] Department of Psychology, University of Cambridge, Cambridge CB2 3EB, United Kingdom; and.
                Article
                1418680112
                10.1073/pnas.1418680112
                25583507
                891c8ea9-8b75-45a3-9bbc-ba58c58a59ce
                History

                artificial intelligence,big data,computational social science,personality judgment,social media

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