Portland Water Bureau SERVUS Algorithm Reportedly Allocates Utility Bill Discount to High-Wealth Consumer

October 14, 2024

The Portland Water Bureau's AI-driven initiative, designed to provide discounts to financially struggling customers, has reportedly allocated a 40% discount to Tim Boyle, a high-wealth consumer with a high water usage. The program, developed by SERVUS, was intended to identify underserved individuals using machine learning.

This incident highlights the need for trustworthy AI practices and governance, particularly when addressing socioeconomic disparities. For those interested in shaping safe and secure AI decision-making, consider joining the HISPI Project Cerebellum TAIM initiative (Govern function) to map, measure, and manage such incidents to prevent harm and promote equitable outcomes.

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Suggested mapping from embedding similarity (not a formal assessment). Browse all TAIM controls

MAP 3.2 — similarity 0.680, rank 1. TAIM detail and related incidents →
MAP 1.6 — similarity 0.668, rank 2. TAIM detail and related incidents →
MEASURE 2.6 — similarity 0.658, rank 3. TAIM detail and related incidents →

Alleged deployer: portland-water-bureau
Alleged developer: portland-water-bureau
Alleged harmed parties: tim-boyle, portland-water-bureau, low-income-portland-residents, city-of-portland

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/868

Data source

Incident data is from the AI Incident Database (AIID).

When citing the database as a whole, please use:

McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.

Pre-print on arXiv · Database snapshots & citation guide

We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.