Non-Determinism and the Lawlessness of Machine Learning Code

• URL: http://arxiv.org/abs/2206.11834v4
• Date: Wed, 14 Aug 2024 00:11:30 GMT
• Title: Non-Determinism and the Lawlessness of Machine Learning Code
• Authors: A. Feder Cooper, Jonathan Frankle, Christopher De Sa,
• Abstract summary: We show that the effects of non-determinism, and consequently its implications for the law, become clearer from the perspective of reasoning about ML outputs as distributions over possible outcomes. We conclude with a brief discussion of what work ML can do to constrain the potentially harm-inducing effects of non-determinism.
• Score: 43.662736664344095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Legal literature on machine learning (ML) tends to focus on harms, and thus tends to reason about individual model outcomes and summary error rates. This focus has masked important aspects of ML that are rooted in its reliance on randomness -- namely, stochasticity and non-determinism. While some recent work has begun to reason about the relationship between stochasticity and arbitrariness in legal contexts, the role of non-determinism more broadly remains unexamined. In this paper, we clarify the overlap and differences between these two concepts, and show that the effects of non-determinism, and consequently its implications for the law, become clearer from the perspective of reasoning about ML outputs as distributions over possible outcomes. This distributional viewpoint accounts for randomness by emphasizing the possible outcomes of ML. Importantly, this type of reasoning is not exclusive with current legal reasoning; it complements (and in fact can strengthen) analyses concerning individual, concrete outcomes for specific automated decisions. By illuminating the important role of non-determinism, we demonstrate that ML code falls outside of the cyberlaw frame of treating ``code as law,'' as this frame assumes that code is deterministic. We conclude with a brief discussion of what work ML can do to constrain the potentially harm-inducing effects of non-determinism, and we indicate where the law must do work to bridge the gap between its current individual-outcome focus and the distributional approach that we recommend.

Related papers

• Fairness-Accuracy Trade-Offs: A Causal Perspective [58.06306331390586]
  We analyze the tension between fairness and accuracy from a causal lens for the first time. We show that enforcing a causal constraint often reduces the disparity between demographic groups. We introduce a new neural approach for causally-constrained fair learning.
  arXiv  Detail & Related papers  (2024-05-24T11:19:52Z)
• On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model [1.3996171129586732]
  An implicit ambiguity in the field of prediction-based decision-making regards the relation between the concepts of prediction and decision. We show the different ways in which these two elements influence the final fairness properties of a prediction-based decision system. We propose a framework that enables a better understanding and reasoning of the conceptual logic of creating fairness in prediction-based decision-making.
  arXiv  Detail & Related papers  (2023-10-09T10:34:42Z)
• A Semantic Approach to Decidability in Epistemic Planning (Extended Version) [72.77805489645604]
  We use a novel semantic approach to achieve decidability. Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity. We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
  arXiv  Detail & Related papers  (2023-07-28T11:26:26Z)
• Causal Fair Machine Learning via Rank-Preserving Interventional Distributions [0.5062312533373299]
  We define individuals as being normatively equal if they are equal in a fictitious, normatively desired (FiND) world. We propose rank-preserving interventional distributions to define a specific FiND world in which this holds. We show that our warping approach effectively identifies the most discriminated individuals and mitigates unfairness.
  arXiv  Detail & Related papers  (2023-07-24T13:46:50Z)
• Machine Learning with Probabilistic Law Discovery: A Concise Introduction [77.34726150561087]
  Probabilistic Law Discovery (PLD) is a logic based Machine Learning method, which implements a variant of probabilistic rule learning. PLD is close to Decision Tree/Random Forest methods, but it differs significantly in how relevant rules are defined. This paper outlines the main principles of PLD, highlight its benefits and limitations and provide some application guidelines.
  arXiv  Detail & Related papers  (2022-12-22T17:40:13Z)
• Legal perspective on possible fairness measures - A legal discussion using the example of hiring decisions (preprint) [0.0]
  We explain the different kinds of fairness concepts that might be applicable for the specific application of hiring decisions. We analyze their pros and cons with regard to the respective fairness interpretation and evaluate them from a legal perspective.
  arXiv  Detail & Related papers  (2021-08-16T06:41:39Z)
• The zoo of Fairness metrics in Machine Learning [62.997667081978825]
  In recent years, the problem of addressing fairness in Machine Learning (ML) and automatic decision-making has attracted a lot of attention. A plethora of different definitions of fairness in ML have been proposed, that consider different notions of what is a "fair decision" in situations impacting individuals in the population. In this work, we try to make some order out of this zoo of definitions.
  arXiv  Detail & Related papers  (2021-06-01T13:19:30Z)
• Fairness Under Feature Exemptions: Counterfactual and Observational Measures [34.5472206536785]
  We propose an information-theoretic decomposition of the total disparity into two components. A non-exempt component quantifies the part that cannot be accounted for by the critical features, and an exempt component quantifies the remaining disparity. We perform case studies to show how one can audit/train models while reducing non-exempt disparity.
  arXiv  Detail & Related papers  (2020-06-14T19:14:00Z)
• On Consequentialism and Fairness [64.35872952140677]
  We provide a consequentialist critique of common definitions of fairness within machine learning. We conclude with a broader discussion of the issues of learning and randomization.
  arXiv  Detail & Related papers  (2020-01-02T05:39:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.