Good intentions, unintended consequences: exploring forecasting harms

TL;DR

This paper explores the unique ethical challenges and potential harms in time series forecasting, proposing a new taxonomy to guide responsible use and mitigate risks in data-driven decision-making.

Contribution

It develops a novel forecasting-specific harm taxonomy by combining expert interviews and AI analysis, addressing a gap in existing ML harm frameworks.

Findings

Identified underexplored domains and risks in forecasting

Developed a taxonomy of forecasting-specific harms

Provided a research agenda for harm mitigation

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 underexplored 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 taxonomy for responsible innovation, we integrate a human-led inductive coding approach with AI-driven analysis to extract key categories of harm in forecasting. This taxonomy aims to support researchers and practitioners by fostering ethical reflection on their decision-making throughout the forecasting process. Additionally, we seek to establish a research agenda focused on identifying measures to mitigate potential harms in forecasting. By highlighting unique risks within forecasting, our work contributes to the broader discourse on machine learning ethics.