Skip to content
#generative ai Open access

Legislative Integrity and Accountability Act: Open-Source Model Legislation for Preventing Congressional Financial Conflicts and Protecting Public Trust v2.9

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

The Legislative Integrity and Accountability Act (LIAA) is open-source model federal legislation for prospectively preventing congressional financial conflicts. Rather than requiring proof of insider trading, corrupt motive, use of material nonpublic information, market effect, or a causal connection between a financial interest and a legislative act, LIAA separates covered federal legislative power and privileged governmental information from privately attributable, materially particularized financial exposure. The proposal covers Members of Congress, senior congressional staff, spouses, and dependent children through person-specific attribution while preserving ordinary diversified saving, retirement, broad market participation, and United States Treasury investment. LIAA regulates effective economic exposure rather than relying solely on product labels, account wrappers, or nominal ownership. Its architecture addresses direct, indirect, beneficial, synthetic, concentrated, derivative, digital-asset, municipal, and government-event exposure; structured transition and successor states; compliance divestiture; trust administration; public disclosure; institutional allocation of administrative functions; external civil enforcement; judicial review; and statutory maintenance. The proposal establishes a machine-readable Public Compliance Ledger while preserving a separate protected administrative record. It distributes functions among existing institutions according to role, including House and Senate ethics offices, the Office of Government Ethics, the Clerk of the House, the Secretary of the Senate, the Government Accountability Office, the Department of Justice, and Article III courts. Version 2.9 is a substantial successor revision to the previously published LIAA v2.5. It preserves the proposal's prospective conflict-prevention objective while replacing a primarily issuer-specific trading-ban architecture with a broader effective-exposure framework, person-specific household attribution, structured successor-state accounting, function-specific institutional carriers, a public compliance-lineage data model, fitted civil remedies, and standing-law periodic review. The revision also adds a conforming Certificate of Divestiture amendment to 26 U.S.C. § 1043 and replaces the prior automatic-sunset architecture with annual metrics, an implementation review, and recurring GAO review. LIAA v2.9 is model legislation. It is not enacted law, is not an introduced congressional bill unless and until introduced by a Member of Congress, is not an official product of Congress or any Federal agency, and is not legal advice. It is intended as a technically reviewable legislative proposal and may receive ordinary technical conforming edits from congressional legislative counsel without altering its substantive design. The v2.9 release architecture was developed after comparison with current 119th-Congress congressional-trading proposals and review of relevant federal ethics, disclosure, tax, enforcement, accessibility, and public-record law. The formal release audit of current-law and current-Congress anchors was completed September 9, 2026. During the preparation and revision of this model legislation, the author used generative AI tools to assist with drafting support, language refinement, structural editing, title development, technical organization, legal-research organization, and clarity. All substantive legal architecture, policy choices, constitutional framing, scope decisions, statutory design, and final editorial judgments are the author's own. The author reviewed and approved the final manuscript and takes full responsibility for its content.

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations

Related blog posts

GPT-Lab Sep 17, 2026

Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering

AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.