2695. Good intentions, unintended consequences: Exploring forecasting harms
Invited abstract in session TC-38: Forecasting, prediction and optimization 3, stream Data Science meets Optimization.
Tuesday, 12:30-14:00Room: Michael Sadler LG19
Authors (first author is the speaker)
| 1. | Bahman Rostami-Tabar
|
| Data lab for Social Good Research Group, Cardiff Business School, Cardiff University |
Abstract
Organizations worldwide that rely on data-driven approaches regularly employ forecasting methods to enhance their planning and decision-making processes. While extensive research has examined the harms associated with traditional machine learning applications, relatively little attention has been given to the ethical implications of time series forecasting. However, forecasting presents distinct ethical challenges due to its diverse organizational applications, varied objectives, and unique data processing, model development, and evaluation workflows. These distinctions complicate the direct application of existing machine learning harm taxonomies to common forecasting scenarios. To address this gap, we conduct multiple interviews with industry experts and academic researchers, systematically identifying and analyzing under-explored domains, use cases, and potential risks associated with forecasting. Our objective is to develop a novel taxonomy of forecasting-specific harms. Drawing inspiration from Microsoft Azure’s taxonomy for responsible innovation, we integrate a human-led inductive coding approach with AI-driven analysis to extract key categories of harm in forecasting.
Keywords
- Forecasting
- Ethics
Status: accepted
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