Computability, Complexity, Consistency and Controllability: A Four C's Framework for cross-disciplinary Ethical Algorithm Research
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Abstract (arXiv)
The ethical consequences of, constraints upon and regulation of algorithms arguably represent the defining challenges of our age, asking us to reckon with the rise of computational technologies whose potential to radically transforming social and individual orders and identity in unforeseen ways is already being realised. Yet despite the multidisciplinary impact of this algorithmic turn, there remains some way to go in motivating the crossdisciplinary collaboration that is crucial to advancing feasible proposals for the ethical design, implementation and regulation of algorithmic and automated systems. In this work, we provide a framework to assist cross-disciplinary collaboration by presenting a Four C's Framework covering key computational considerations researchers across such diverse fields should consider when approaching these questions: (i) computability, (ii) complexity, (iii) consistency and (iv) controllability. In addition, we provide examples of how insights from ethics, philosophy and population ethics are relevant to and translatable within sciences concerned with the study and design of algorithms. Our aim is to set out a framework which we believe is useful for fostering cross-disciplinary understanding of pertinent issues in ethical algorithmic literature which is relevant considering the feasibility of ethical algorithmic governance, especially the impact of computational constraints upon algorithmic governance.
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References · 13
- 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
- 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
- 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
- 2020Fairness in Machine Learning: A Surveycited by 369
- 2016Cooperative Inverse Reinforcement Learningcited by 298
- 2018Delayed Impact of Fair Machine Learningcited by 183
- 2018An Empirical Study of Rich Subgroup Fairness for Machine Learningcited by 81
- 2018Probably Approximately Metric-Fair Learningcited by 29
- 2017Provably Fair Representationscited by 22
- 2020Review of Mathematical frameworks for Fairness in Machine Learningcited by 21
- 2020Multi-Principal Assistance Gamescited by 10
- 2019Connections Between Adaptive Control and Optimization in Machine Learningcited by 6
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