A new auditing framework measures how much of the public's submitted viewpoints is lost in AI-generated consultation summaries, finding that official summaries represent the population worse than a random set of participants and that critical voices are most likely excluded.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
Increasing LLM coding agents' reasoning effort raises cost and process complexity but does not reliably improve model quality across 140 controlled runs on networked anagram game data.
A pipeline using SBERT/UMAP/HDBSCAN clustering on 339 repositories identifies 692k recurring Gherkin slices, labels 200 of them, and trains an XGBoost model that achieves F1 0.891 for extraction-worthiness, outperforming rule and LLM baselines, with prevalence statistics released.
citing papers explorer
-
Participatory provenance as representational auditing for AI-mediated public consultation
A new auditing framework measures how much of the public's submitted viewpoints is lost in AI-generated consultation summaries, finding that official summaries represent the population worse than a random set of participants and that critical voices are most likely excluded.
-
An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery
Increasing LLM coding agents' reasoning effort raises cost and process complexity but does not reliably improve model quality across 140 controlled runs on networked anagram game data.
-
Given, When, Then, Again: Mining Subscenario Refactoring Candidates in Behaviour-Driven Test Suites with ML Classifiers and LLM-Judge Baselines
A pipeline using SBERT/UMAP/HDBSCAN clustering on 339 repositories identifies 692k recurring Gherkin slices, labels 200 of them, and trains an XGBoost model that achieves F1 0.891 for extraction-worthiness, outperforming rule and LLM baselines, with prevalence statistics released.