Who Gets Heeded? An Obligation-Level Audit of Responsiveness in EPA Rulemaking
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Computer Science > Computers and Society
arXiv:2608.10329 (cs)
[Submitted on 11 Aug 2026]
Title:Who Gets Heeded? An Obligation-Level Audit of Responsiveness in EPA Rulemaking
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Abstract:Notice-and-comment rulemaking gives any affected party the same formal right to influence federal regulation, but formal access is not substantive capacity to shape rule text. Existing strategies operate at the rule or aggregate-corpus level, too coarse to capture the discrete regulatory obligations where commenters seek change. We introduce obligation-level responsiveness auditing, an auditable, AI-assisted framework for measuring whether public-comment engagement co-occurs with changes to specific regulatory duties. The framework extracts proposed and final-rule obligations, matches comments to the obligations they address, and classifies proposed-final outcomes; each load-bearing component is evaluated against blind human judgment. We apply the framework to 70,075 comments across 36 EPA anchor rulemakings, drawn from a corpus of 786,197 comments across 6,145 dockets from 2010-2022. Three descriptive findings emerge. First, engagement is associated with revision at a modest within-docket magnitude. Second, support-versus-opposition direction does not clearly differentiate outcomes, an informative null inconsistent with simple preference-aggregation. Third, under a permissive reconstruction of commenter type, organizational-majority engagement concentrates in editorial-refinement rather than substantive-modification outcomes at the cross-docket level. A blind human audit of the load-bearing outcome contrast preserves this third finding under corrected labels and reveals that text-similarity methods are insufficient for distinguishing editorial from substantive regulatory change, a measurement-validity lesson we treat as a supporting methodological contribution. Together, these findings locate the equity asymmetry upstream of agency response: in differential capacity across commenter populations to identify, interpret, and contest specific legal obligations.
| Comments: | 14 pages, 6 figures. Accepted as a full paper at the 6th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO '26), Munich, Germany. Selected for oral presentation |
| Subjects: | Computers and Society (cs.CY); Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.10329 [cs.CY] |
| (or arXiv:2608.10329v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2608.10329
arXiv-issued DOI via DataCite (pending registration)
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