ENVENV-003 — Resource Allocation Imbalances
ML Systems in Transport Driving Net Environmental Harm Through Prediction Error and Rebound Effects
4/5Sector: TransportGeography: GlobalStage: OperateIngested: —
Executive Summary
Machine learning systems in transport can increase emissions via prediction errors, such as unnecessary resource spin-up, and through rebound effects where automation raises overall vehicle usage. Boards must account for these environmental liabilities when approving ML deployments and reporting on climate commitments.
Domain
Environmental Factors
Blindspots in organisational culture, stakeholder expectations, resource allocation, market pressure, regulatory environment, and external partnerships.
Source
MIT AI Risk Repository — The Risks of Machine Learning Systems (Tan2022) ↗https://airisk.mit.edu/
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