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

2025-12-01

AI Summary

This paper identifies the deep ethical roots of algorithmic bias in time-series forecasting: systemic discrimination arises from historical data biases, inadequate problem formalization, and normative choices embedded in socio-technical design—exacerbating social inequities in high-stakes domains such as healthcare, energy, and economics. To address this, we propose a “socio-technical bias” analytical framework that conceptualizes bias as emerging from institutional constraints and value-laden design decisions. We develop a holistic diagnostic methodology spanning data curation, modeling, and evaluation, integrating causal reasoning, interpretable model design, multi-metric dynamic fairness validation, and context-sensitive assessment. Empirical results demonstrate that fairness and predictive accuracy can be jointly optimized. Our work establishes a fairness-by-design paradigm, advancing responsible innovation toward democratic values and institutional safeguards.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• Addresses algorithmic bias in time series forecasting across high-stakes domains.
• Examines how models reproduce inequalities via proxy variables and feedback loops.
• Proposes fairness integration through design, governance, and dynamic validation methods.

Innovation

• Reframes bias as socio-technical phenomenon with normative choices
• Proposes causal modeling and interpretable systems for bias diagnosis
• Advocates participatory governance and fairness-by-design as co-requisite