Prejudiced Futures? Algorithmic Bias in Time Series Forecasting and Its Ethical Implications

TL;DR

This paper critically examines the ethical challenges of algorithmic bias in time series forecasting, emphasizing the importance of socio-technical considerations, diagnosis, and participatory governance for responsible and fair predictive systems.

Contribution

It introduces a socio-technical framework for understanding bias, diagnosis methods, and structural reforms to embed fairness in time series prediction models.

Findings

Bias in time series models can reinforce structural inequalities.

Causal modeling and interpretability are key to diagnosing bias.

Participatory governance enhances fairness and accountability.

Abstract

Time series prediction algorithms are increasingly central to decision-making in high-stakes domains such as healthcare, energy management, and economic planning. Yet, these systems often inherit and amplify biases embedded in historical data, flawed problem specifications, and socio-technical design decisions. This paper critically examines the ethical foundations and mitigation strategies for algorithmic bias in time series prediction. We outline how predictive models, particularly in temporally dynamic domains, can reproduce structural inequalities and emergent discrimination through proxy variables and feedback loops. The paper advances a threefold contribution: First, it reframes algorithmic bias as a socio-technical phenomenon rooted in normative choices and institutional constraints. Second, it offers a structured diagnosis of bias sources across the pipeline, emphasizing the need for causal modeling, interpretable systems, and inclusive design practices. Third, it advocates for structural reforms that embed fairness through participatory governance, stakeholder engagement, and legally enforceable safeguards. Special attention is given to fairness validation in dynamic environments, proposing multi-metric, temporally-aware, and context-sensitive evaluation methods. Ultimately, we call for an integrated ethics-by-design approach that positions fairness not as a trade-off against performance, but as a co-requisite of responsible innovation. This framework is essential to developing predictive systems that are not only effective and adaptive but also aligned with democratic values and social equity.