Position: AI’s growing due process problem
Authors: Sunayana Rane
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Using two recent court decisions as a foundation, this paper takes the position that AI in its current form cannot guarantee due process, and therefore cannot and (should not) be used to make decisions that should be subject to due process. The supporting legal analysis investigates how the current lack of technical answers about the interpretability and causality of AI decisions, coupled with extreme trade secret protections severely limiting any exercise of the small amount of technical knowledge we do have, serve as a fatal anti-due-process combination. Throughout the analysis, this paper explains why technical researchers involvement is vital to informing the legal process and restoring due process protections. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Princeton University 2University of Chicago Law School. Correspondence to: Sunayana Rane <EMAIL>. |
| Pseudocode | No | The paper discusses legal analysis, case studies, and conceptual arguments regarding AI and due process. It does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a position paper presenting a legal analysis and theoretical arguments. It does not describe any methodology for which open-source code would be provided or relevant. |
| Open Datasets | No | The paper discusses legal cases and the data involved in those cases (e.g., '733 data points are used to create the model' for the IDHW tool, or the COMPAS algorithm). However, these are not datasets used by the authors for their own experiments, nor are they made publicly available by the authors. The paper itself does not conduct experiments using open datasets. |
| Dataset Splits | No | The paper does not describe any experiments conducted by the authors that would require dataset splits for reproducibility. It discusses legal cases and AI system usage in those contexts. |
| Hardware Specification | No | The paper is a legal and theoretical analysis. It does not describe any computational experiments conducted by the authors, and therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is a legal and theoretical analysis and does not involve experimental implementation that would require specific software dependencies for reproducibility. |
| Experiment Setup | No | The paper is a legal and theoretical analysis, focusing on conceptual arguments and case studies. It does not describe any experiments conducted by the authors, and therefore no experimental setup details like hyperparameters or training configurations are provided. |