Incidente 868: translated-es-Portland Water Bureau SERVUS Algorithm Reportedly Allocates Utility Bill Discount to High-Wealth Consumer
Descripción: translated-es-The Portland Water Bureau's AI-driven pilot program for water bill discounts is reported to have randomly selected Tim Boyle, a wealthy high-water consumer, for a 40% discount intended for financially struggling customers. The program, developed by SERVUS, is meant to identify underserved individuals by using machine learning.
Entidades
Ver todas las entidadesPresunto: un sistema de IA desarrollado e implementado por Portland Water Bureau y SERVUS, perjudicó a Portland Water Bureau , Tim Boyle , Low-income Portland residents y City of Portland.
Sistema de IA presuntamente implicado: SERVUS
Estadísticas de incidentes
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
7.3. Lack of capability or robustness
Risk Domain
The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
- AI system safety, failures, and limitations
Entity
Which, if any, entity is presented as the main cause of the risk
AI
Timing
The stage in the AI lifecycle at which the risk is presented as occurring
Post-deployment
Intent
Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
Unintentional
Informes del Incidente
Cronología de Informes
Tim Boyle no podía creer lo que veía. El director ejecutivo de Columbia Sportswear paga muchas facturas, tanto a nivel personal como de su empresa. Es inusual que un proveedor le ofrezca un gran descuento por algo por lo que paga el precio …
Variantes
Una "Variante" es un incidente de IA similar a un caso conocido—tiene los mismos causantes, daños y sistema de IA. En lugar de enumerarlo por separado, lo agrupamos bajo el primer incidente informado. A diferencia de otros incidentes, las variantes no necesitan haber sido informadas fuera de la AIID. Obtenga más información del trabajo de investigación.
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