Task-conditioned language and vision models omit co-present safety-critical signals they report when unconstrained, decoupling benchmark safety from deployment safety.
Mixed citations
Title resolution pending
Mixed citation behavior. Most common role is background (67%).
11
Pith papers citing it
1,371
external citations · OpenAlex
Background
67% of classified citations
citation-role summary
background 6
citation-polarity summary
years
2026 11roles
background 6representative citing papers
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
AI-assisted data literacy benefits from a cognitive alignment framework that maps AI modes (transmissive or deliberative) to user demands (receptive or deliberative) to reduce passivity and friction.
A queueing model of AI task processing identifies a 'variance wedge' where mean task speed falls but system delay rises due to rework and reduced oversight under congestion.
A new model derives a convex systemic risk coupling r(φ) that grows superlinearly with AI adoption share, producing a saddle-node bifurcation to algorithmic monoculture and 18-54% tail-loss amplification, validated on SEC 13F holdings data.
Pista decomposes AI agent actions in spreadsheets into auditable steps, enabling real-time user intervention that improves task outcomes, user comprehension, agent perception, and sense of co-ownership over baseline agents.
Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
Imbalanced user-AI relationships form a distinct front-end ethical failure in healthcare AI that design choices such as restricted inputs and suppressed uncertainty can undermine agency and that reciprocity offers a path to more balanced interactions.
A literature review shows that constructs for appropriate reliance on AI are fragmented, presents three views on the topic, and calls for consensus on objective metrics to enable better comparisons across studies.
AI integration in newsrooms drives internal deferral of judgment to LLMs and external shifts of power to platforms, making fairness, accountability, and transparency harder to sustain unless participatory mechanisms redistribute authority.
A position paper argues that post-hoc XAI explanations are unfaithful and paradoxical, proposing a shift to expert-based verification and certification of AI systems.
citing papers explorer
-
The inattentional gap in task conditioned AI models that omit otherwise reportable safety critical signals
Task-conditioned language and vision models omit co-present safety-critical signals they report when unconstrained, decoupling benchmark safety from deployment safety.
-
What Should Explanations Contain? A Human-Centered Explanation Content Model for Local, Post-Hoc Explanations
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
-
Disrupting Cognitive Passivity: Rethinking AI-Assisted Data Literacy through Cognitive Alignment
AI-assisted data literacy benefits from a cognitive alignment framework that maps AI modes (transmissive or deliberative) to user demands (receptive or deliberative) to reduce passivity and friction.
-
Queue & AI: When Faster Tasks Slow Down the Workflow
A queueing model of AI task processing identifies a 'variance wedge' where mean task speed falls but system delay rises due to rework and reduced oversight under congestion.
-
Artificial Intelligence and Systemic Risk: A Unified Model of Performative Prediction, Algorithmic Herding, and Cognitive Dependency in Financial Markets
A new model derives a convex systemic risk coupling r(φ) that grows superlinearly with AI adoption share, producing a saddle-node bifurcation to algorithmic monoculture and 18-54% tail-loss amplification, validated on SEC 13F holdings data.
-
Auditing and Controlling AI Agent Actions in Spreadsheets
Pista decomposes AI agent actions in spreadsheets into auditable steps, enabling real-time user intervention that improves task outcomes, user comprehension, agent perception, and sense of co-ownership over baseline agents.
-
Relationships Between Trust, Compliance, and Performance for Novice Programmers Using AI Code Generation
Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
-
The Imbalanced User-AI Relationships as an Ethical Failure of Front-End Design in Healthcare AI
Imbalanced user-AI relationships form a distinct front-end ethical failure in healthcare AI that design choices such as restricted inputs and suppressed uncertainty can undermine agency and that reciprocity offers a path to more balanced interactions.
-
From Trust to Appropriate Reliance: Measurement Constructs in Human-AI Decision-Making
A literature review shows that constructs for appropriate reliance on AI are fragmented, presents three views on the topic, and calls for consensus on objective metrics to enable better comparisons across studies.
-
FAccT-Checked: A Narrative Review of Authority Reconfigurations and Retention in AI-Mediated Journalism
AI integration in newsrooms drives internal deferral of judgment to LLMs and external shifts of power to platforms, making fairness, accountability, and transparency harder to sustain unless participatory mechanisms redistribute authority.
-
Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
A position paper argues that post-hoc XAI explanations are unfaithful and paradoxical, proposing a shift to expert-based verification and certification of AI systems.