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

Bias in time series forecasting is a systemic problem, not just a technical one: The paper reframes algorithmic bias as rooted in "normative choices and institutional constraints" rather than purely technical issues, meaning that biases stem from how problems are defined and designed by humans, not just from flawed data alone.

Historical data perpetuates and amplifies existing inequalities: Time series prediction models "inherit and amplify biases embedded in historical data," which is particularly concerning in high-stakes domains like healthcare and economic planning where biased predictions can reinforce structural inequalities over time.

Feedback loops create "emergent discrimination": The paper identifies how proxy variables and feedback loops in predictive systems can generate new forms of discrimination that weren't explicitly present in the original data, meaning bias can actually worsen as the system operates.

Fairness requires participatory design and stakeholder involvement: Rather than treating fairness as a purely technical fix, the paper advocates for "participatory governance, stakeholder engagement, and legally enforceable safeguards," emphasizing that affected communities should have a voice in how these systems are built.

Fairness should be evaluated continuously and contextually: The paper proposes "multi-metric, temporally-aware, and context-sensitive evaluation methods" for fairness validation, recognizing that bias in forecasting systems changes over time and varies by context, requiring ongoing monitoring rather than one-time audits.