Psychological AI: Designing Algorithms Informed by Human Psychology.
Psychological artificial intelligence (AI) applies insights from psychology to design computer algorithms. Its core domain is decision-making under uncertainty, that is, ill-defined situations that can change in unexpected ways rather than well-defined, stable problems, such as chess and Go. Psychol...
| Publicado en: | Perspectives on Psychological Science Vol. 19; no. 5; pp. 839 - 849 |
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| Autor principal: | |
| Formato: | Journal Article |
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Sage Publications Inc.
Sep2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179766023&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179766023 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17456916 1WQQ jtl: Perspectives on Psychological Science issn: 17456916 maglogo: N pubinfo: dt: Sep2024 vid: 19 iid: 5 pid: 20732 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 179766023 169023924 10.1177/17456916231180597 179766023 ppf: 839 ppct: 10 formats: tig: atl: Psychological AI: Designing Algorithms Informed by Human Psychology. aug: au: Gigerenzer, Gerd affil: Max Planck Institute for Human Development, Berlin, Germany sug: subj: Psychological Theory Artificial Intelligence Algorithms Psychology Decision Trees Web Search Engines Data Analytics Computer Memory Recidivism Prediction Models Problem Solving ab: Psychological artificial intelligence (AI) applies insights from psychology to design computer algorithms. Its core domain is decision-making under uncertainty, that is, ill-defined situations that can change in unexpected ways rather than well-defined, stable problems, such as chess and Go. Psychological theories about heuristic processes under uncertainty can provide possible insights. I provide two illustrations. The first shows how recency—the human tendency to rely on the most recent information and ignore base rates—can be built into a simple algorithm that predicts the flu substantially better than did Google Flu Trends's big-data algorithms. The second uses a result from memory research—the paradoxical effect that making numbers less precise increases recall—in the design of algorithms that predict recidivism. These case studies provide an existence proof that psychological AI can help design efficient and transparent algorithms. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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