Empirical study of 522 registered data brokers finds 9% fully compliant with Delete Act transparency requirements, 43% make exercising all privacy rights impossible, and 64% add substantial friction to request processes.
Is stack overflow obsolete? an empirical study of the characteristics of chatgpt answers to stack overflow questions
9 Pith papers cite this work. Polarity classification is still indexing.
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PrivacyAkinator uses LLM-generated questions grounded in data-flow representations and a news-mined design space to help developers surface privacy decisions, yielding 47% more decisions identified in 73% less time than PRAM in a 24-person study.
Experiments on 250 participants show LLM-assisted survey responses range from under 10% on Prolific to over 80% on Mechanical Turk, with identifiable characteristics and partial mitigation effects.
A Privacy Guardian Agent automates routine consent choices with user profiles and contextual awareness, escalating unclear or high-risk cases to users while keeping autonomous decisions reviewable for transparency.
Exploratory lab study finds shared LLM use builds shared understanding in design teams while parallel use risks context drift, with professionals reflecting on outputs for insights but sometimes anchoring early.
Agentic entropy names the systemic drift in AI coding agents away from architectural intent; a new framework using conformity seeding, reasoning monitoring, and causal graph interfaces supplies process-level oversight to complement existing review methods.
User study reveals nine LLM failure categories in SE tasks and quantifies abandonment factors from 26 participants.
Aleena is an open-source AI agent that ingests multi-modal research software collaboration artifacts and transforms them into structured GitHub records to maintain continuous stakeholder alignment across the project lifecycle.
Develops a conceptual distinction between human-cognitive and artificial-stochastic error architectures in code generation, drawing on Dennett, Rescher, and Floridi to explore implications for AI-human collaboration.
citing papers explorer
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Privacy Without Remedy: An Assessment of Data Broker Compliance with California Privacy Law
Empirical study of 522 registered data brokers finds 9% fully compliant with Delete Act transparency requirements, 43% make exercising all privacy rights impossible, and 64% add substantial friction to request processes.
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PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions
PrivacyAkinator uses LLM-generated questions grounded in data-flow representations and a news-mined design space to help developers surface privacy decisions, yielding 47% more decisions identified in 73% less time than PRAM in a 24-person study.
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A Penny for Your Prompts: Experiments Detecting and Mitigating LLM Usage by Survey Respondents
Experiments on 250 participants show LLM-assisted survey responses range from under 10% on Prolific to over 80% on Mechanical Turk, with identifiable characteristics and partial mitigation effects.
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The Privacy Guardian Agent: Towards Trustworthy AI Privacy Agents
A Privacy Guardian Agent automates routine consent choices with user profiles and contextual awareness, escalating unclear or high-risk cases to users while keeping autonomous decisions reviewable for transparency.
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The Role of LLMs in Collaborative Software Design
Exploratory lab study finds shared LLM use builds shared understanding in design teams while parallel use risks context drift, with professionals reflecting on outputs for insights but sometimes anchoring early.
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Beyond the 'Diff': Addressing Agentic Entropy in Agentic Software Development
Agentic entropy names the systemic drift in AI coding agents away from architectural intent; a new framework using conformity seeding, reasoning monitoring, and causal graph interfaces supplies process-level oversight to complement existing review methods.
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"Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineering
User study reveals nine LLM failure categories in SE tasks and quantifies abandonment factors from 26 participants.
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Aleena: Alignment Agent for Research Software Engineering Collaborations
Aleena is an open-source AI agent that ingests multi-modal research software collaboration artifacts and transforms them into structured GitHub records to maintain continuous stakeholder alignment across the project lifecycle.
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Architectures of Error: A Philosophical Inquiry into AI and Human Code Generation
Develops a conceptual distinction between human-cognitive and artificial-stochastic error architectures in code generation, drawing on Dennett, Rescher, and Floridi to explore implications for AI-human collaboration.