mlsanityData to 7 Oct 2026

Fairness in Forecasting of Observations of Linear Dynamical Systems

arXiv 2209.05274 ↗First posted 12 Sep 2022cs.LG cs.SY eess.SYcited by 4

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Abstract (arXiv)

In machine learning, training data often capture the behaviour of multiple subgroups of some underlying human population. This behaviour can often be modelled as observations of an unknown dynamical system with an unobserved state. When the training data for the subgroups are not controlled carefully, however, under-representation bias arises. To counter under-representation bias, we introduce two natural notions of fairness in time-series forecasting problems: subgroup fairness and instantaneous fairness. These notions extend predictive parity to the learning of dynamical systems. We also show globally convergent methods for the fairness-constrained learning problems using hierarchies of convexifications of non-commutative polynomial optimisation problems. We also show that by exploiting sparsity in the convexifications, we can reduce the run time of our methods considerably. Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate the efficacy of our methods.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 54 of its 119 references that are in the map. First posted on arXiv 12 Sep 2022.

Builds on

54 of 119 references.

References · 54

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 1,965
  3. 2017Counterfactual Fairnesscited by 802
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38 more
  1. 2018An Intersectional Definition of Fairnesscited by 126
  2. 2018Near optimal finite time identification of arbitrary linear dynamical systemscited by 89
  3. 2020On the Privacy Risks of Algorithmic Fairnesscited by 82
  4. 2020Robust Optimization for Fairness with Noisy Protected Groupscited by 78
  5. 2019Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Makingcited by 75
  6. 2019Finite Sample Analysis of Stochastic System Identificationcited by 67
  7. 2017Finite Time Identification in Unstable Linear Systemscited by 66
  8. 2017On Formalizing Fairness in Prediction with Machine Learningcited by 65
  9. 2021Post-processing for Individual Fairnesscited by 61
  10. 2019Learning Linear Dynamical Systems with Semi-Parametric Least Squarescited by 58
  11. 2019Algorithms for Fairness in Sequential Decision Makingcited by 53
  12. 2019Discrimination in the Age of Algorithmscited by 51
  13. 2019Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?cited by 50
  14. 2015On the relation between accuracy and fairness in binary classificationcited by 47
  15. 2017Learning Linear Dynamical Systems via Spectral Filteringcited by 46
  16. 2019Average Individual Fairness: Algorithms, Generalization and Experimentscited by 46
  17. 2020Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policycited by 44
  18. 2022Causal Conceptions of Fairness and their Consequencescited by 38
  19. 2018Spectral Filtering for General Linear Dynamical Systemscited by 37
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  21. 2013Online Learning for Time Series Predictioncited by 33
  22. 2021Representation Matters: Assessing the Importance of Subgroup Allocations in Training Datacited by 31
  23. 2020Does enforcing fairness mitigate biases caused by subpopulation shift?cited by 25
  24. 2020Bias and Discrimination in AI: a cross-disciplinary perspectivecited by 24
  25. 2021Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Valuescited by 24
  26. 2021Assessing Fairness in the Presence of Missing Datacited by 24
  27. 2020Online Learning of the Kalman Filter with Logarithmic Regretcited by 21
  28. 2021Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputscited by 18
  29. 2018On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filterscited by 16
  30. 2019TSSOS: A Moment-SOS hierarchy that exploits term sparsitycited by 14
  31. 2020Beyond Individual and Group Fairnesscited by 13
  32. 2019Sample Complexity of Kalman Filtering for Unknown Systemscited by 11
  33. 2020Chordal-TSSOS: a moment-SOS hierarchy that exploits term sparsity with chordal extensioncited by 11
  34. 2020Fair Prediction with Endogenous Behaviorcited by 10
  35. 2019Dynamic fairness - Breaking vicious cycles in automatic decision makingcited by 4
  36. 2019Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testingcited by 4
  37. 2021Weighted Fairness Notions for Indivisible Items Revisitedcited by 4
  38. 2020Fairness in Forecasting and Learning Linear Dynamical Systemscited by 3

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Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2024Fairness in Ranking: Robustness through Randomization without the Protected Attributecited by 1
  2. 2023Improving the Validity of Decision Trees as Explanationscited by 0
  3. 2025Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networkscited by 0
  4. 2025humancompatible.detect: a Python Toolkit for Detecting Bias in AI Modelscited by 0