Classification algorithms and social outcomes.
Classification algorithms are increasingly important in areas such as obtaining credit, employment, health care, housing, law enforcement, and national security. These classification decisions affect people's lives and, accordingly, can shape their behaviors. We present a formal model of optimal cla...
| Publicado en: | American Journal of Political Science (John Wiley & Sons, Inc.) Vol. 70; no. 3; pp. 1229 - 1247 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
John Wiley & Sons, Inc.
Jul2026
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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=hlh&AN=195362941&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 195362941 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00925853 LVDV jtl: American Journal of Political Science (John Wiley & Sons, Inc.) issn: 00925853 maglogo: N pubinfo: dt: Jul2026 vid: 70 iid: 3 pid: 52269 pub: John Wiley & Sons, Inc. artinfo: ui: 195362941 10.1111/ajps.70005 ppf: 1229 ppct: 18 formats: tig: atl: Classification algorithms and social outcomes. aug: au: Penn, Elizabeth Maggie Patty, John W. affil: Departments of Political Science and Data & Decision Sciences, Emory University, Atlanta Georgia, , USA su: Classification algorithms Social impact Social dynamics Social influence Algorithmic bias sug: subj: Classification algorithms Social impact Social dynamics Social influence Algorithmic bias ab: Classification algorithms are increasingly important in areas such as obtaining credit, employment, health care, housing, law enforcement, and national security. These classification decisions affect people's lives and, accordingly, can shape their behaviors. We present a formal model of optimal classification by an algorithm designer who may want to affect the distribution of behavior in a population. Our model allows the designer to have a wide array of objectives (such as maximizing compliance or maximizing accuracy, among many others), and these objectives shape equilibrium behavioral outcomes in the population, sometimes in surprising ways. Our results also speak to questions of algorithmic fairness in settings where behavior and algorithms are interdependent, and where measures of fairness focusing on statistical parity across groups may not be appropriate. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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