Towards socially responsible forecasting: Identifying and typifying forecasting harms

 

Data-driven organizations around the world routinely use forecasting methods to improve their planning and decision-making capabilities. Although much research exists on the harms resulting from traditional AI and machine learning applications, little has specifically focused on the ethical impact of time series forecasting. Yet forecasting raises unique ethical issues due to the way it is used in different organizational contexts, supports different goals, and involves different data processing, model development, and evaluation pipelines. These differences make it difficult to apply existing AI and machine learning harm taxonomies to real-world forecasting contexts. We leverage 20 interviews with expert industry practitioners and academic researchers to remedy this knowledge gap by cataloguing and analysing under-explored domains, applications, and scenarios where forecasting may cause harm, with the goal of developing a novel taxonomy of forecasting-specific harms. Inspired by the Microsoft Azure taxonomy for responsible innovation, we combined a human-led inductive coding scheme with an AI-driven analysis centered on the extraction of an initial taxonomy harm in forecasting. We then augment the taxonomy with relevant theoretical considerations from ethics and law, such as the distinction between a harm and wrong. The resulting taxonomy is designed to guide researchers and practitioners and encourage ethical reflection on the impact of their decisions during the forecasting process. A secondary objective is to create a research agenda for socially responsible forecasting that focuses on possible forecasting-related measures to mitigate harm. Our work extends the growing literature on AI and machine learning harms by empirically identifying unique forms of harm that may occur in forecasting and linking them with traditional concepts of responsibility from ethics and law.

Cite

@unpublished{harms,
  author = {Bahman Rostami-Tabar and Travis Greene and Galit Shmueli and Rob J Hyndman},
  title  = {Towards socially responsible forecasting: Identifying and typifying forecasting harms},
  url    = {https://robjhyndman.com/publications/harms.html},
  year   = {2025}
}