Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping Software
- URL: http://arxiv.org/abs/2604.10875v1
- Date: Mon, 13 Apr 2026 00:49:58 GMT
- Title: Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping Software
- Abstract summary: This study explores how an audit standard's design influences its effectiveness.<n>We identify numerous gaps between the standard's desired outcomes and the auditing practices it enables.
- Score: 44.528140097188604
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: AI governance efforts increasingly rely on audit standards: agreed-upon practices for conducting audits. However, poorly designed standards can hide and lend credibility to inadequate systems. We explore how an audit standard's design influences its effectiveness through a case study of ASB 018, a standard for auditing probabilistic genotyping software -- software that the U.S. criminal legal system increasingly uses to analyze DNA samples. Through qualitative analysis of ASB 018 and five audit reports, we identify numerous gaps between the standard's desired outcomes and the auditing practices it enables. For instance, ASB 018 envisions that compliant audits establish restrictions on software use based on observed failures. However, audits can comply without establishing such boundaries. We connect these gaps to the design of the standard's requirements such as vague language and undefined terms. We conclude with recommendations for designing audit standards and evaluating their effectiveness.
Related papers
- AspisAI: A Canonical, Machine-Interpretable Governance Framework for Automated Multi-Standard Compliance Monitoring [0.0]
This paper presents emphAspisAI, a bounded, standard-agnostic governance framework that translates selected requirements into a canonical, machine-interpretable control model.<n>Within a bounded scope of 26 representative requirements, the framework is evaluated in a controlled simulation against five governance-oriented criteria.<n>Results include full requirement encoding, 88.5,% mapping coverage, complete traceability, and correct detection of all introduced gaps.
arXiv Detail & Related papers (2026-09-09T22:37:12Z) - VERA-8B: Evidence-Grounded Audit Risk Reasoning from SEC Filings [0.6445605125467574]
We develop VERA-8B, a new end-to-end audit reasoning system.<n> VERA-8B identifies audit risks before enforcement actions occur.<n>We are the first to unify SFT and GRPO for evidence-grounded audit reasoning.
arXiv Detail & Related papers (2026-08-28T14:55:49Z) - Bayesian control for coding agents [63.64172141184361]
We propose a cost-sensitive sequential hypothesis testing framework for coding agents.<n>A Bayesian controller maintains a belief over correctness and decides whether to gather more evidence, refine the candidate, verify it, or stop.<n>We show that the belief state yields an interpretable correctness score that outperforms token-probability and raw tool-success baselines for uncertainty quantification.
arXiv Detail & Related papers (2026-06-23T11:41:32Z) - IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation [49.796717294455796]
We present IMMACULATE, a practical auditing framework that detects economically motivated deviations.<n>IMMACULATE selectively audits a small fraction of requests using verifiable computation, achieving strong detection guarantees while amortizing cryptographic overhead.
arXiv Detail & Related papers (2026-02-26T07:21:02Z) - Judging by the Rules: Compliance-Aligned Framework for Modern Slavery Statement Monitoring [24.13989765643719]
Modern slavery affects millions of people worldwide, and regulatory frameworks such as Modern Slavery Acts now require companies to publish detailed disclosures.<n>These statements are often vague and inconsistent, making manual review time-consuming and difficult to scale.<n>We propose a novel framework that harnesses AI for rule-level compliance verification while preserving expert oversight.
arXiv Detail & Related papers (2025-11-11T03:41:44Z) - SConU: Selective Conformal Uncertainty in Large Language Models [59.25881667640868]
We propose a novel approach termed Selective Conformal Uncertainty (SConU)<n>We develop two conformal p-values that are instrumental in determining whether a given sample deviates from the uncertainty distribution of the calibration set at a specific manageable risk level.<n>Our approach not only facilitates rigorous management of miscoverage rates across both single-domain and interdisciplinary contexts, but also enhances the efficiency of predictions.
arXiv Detail & Related papers (2025-04-19T03:01:45Z) - NLP-based Regulatory Compliance -- Using GPT 4.0 to Decode Regulatory Documents [0.0]
This study evaluates GPT-4.0's ability to identify conflicts within regulatory requirements.<n>Using metrics such as precision, recall, and F1 score, the experiment demonstrates GPT-4.0's effectiveness in detecting inconsistencies.
arXiv Detail & Related papers (2024-12-29T22:14:59Z) - From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditing [1.196505602609637]
Audits can take many forms, including pre-deployment risk assessments, ongoing monitoring, and compliance testing.
There are many operational challenges to AI auditing that complicate its implementation.
We argue that auditing can be cast as a natural hypothesis test, draw parallels hypothesis testing and legal procedure, and argue that this framing provides clear and interpretable guidance on audit implementation.
arXiv Detail & Related papers (2024-10-07T06:15:46Z) - Automatically Adaptive Conformal Risk Control [49.95190019041905]
We propose a methodology for achieving approximate conditional control of statistical risks by adapting to the difficulty of test samples.<n>Our framework goes beyond traditional conditional risk control based on user-provided conditioning events to the algorithmic, data-driven determination of appropriate function classes for conditioning.
arXiv Detail & Related papers (2024-06-25T08:29:32Z) - A Framework for Assurance Audits of Algorithmic Systems [2.2342503377379725]
We propose the criterion audit as an operationalizable compliance and assurance external audit framework.
We argue that AI audits should similarly provide assurance to their stakeholders about AI organizations' ability to govern their algorithms in ways that harms and uphold human values.
We conclude by offering a critical discussion on the benefits, inherent limitations, and implementation challenges of applying practices of the more mature financial auditing industry to AI auditing.
arXiv Detail & Related papers (2024-01-26T14:38:54Z) - Tight Auditing of Differentially Private Machine Learning [77.38590306275877]
For private machine learning, existing auditing mechanisms are tight.
They only give tight estimates under implausible worst-case assumptions.
We design an improved auditing scheme that yields tight privacy estimates for natural (not adversarially crafted) datasets.
arXiv Detail & Related papers (2023-02-15T21:40:33Z)
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.