SEED is a structural encoding framework using typed actor-flow graphs to describe, evaluate novelty of, and generate experimental designs for AI-enabled science under feasibility and governance constraints.
hub
and Sharma, Amit and Tan, Chenhao , title =
15 Pith papers cite this work. Polarity classification is still indexing.
15
Pith papers citing it
hub tools
citation-role summary
background 1
method 1
citation-polarity summary
representative citing papers
CFQ trains quantizer parameters and mixed-precision allocation to preserve counterfactual recourse validity, cost, and direction on Adult, German Credit, and COMPAS while matching accuracy of standard quantizers.
Collective recourse formalizes community reports to fix group harms in diffusion models for urban visualizations via a report-triage-fix-verify pipeline, four primitives, a mandate score, and synthetic evaluation of 240 reports.
Formalizing personalization as individual actionability in causal recourse shows hard constraints degrade validity and plausibility while revealing socio-demographic disparities in costs.
Adversarial explanation attacks preserve nearly all human trust in wrong AI outputs by using persuasive framing, shown in a study varying reasoning, evidence, style, and format with over 200 participants.
Cognitive forcing interventions reduce overreliance on AI recommendations more than simple explanations, with effects moderated by individual need for cognition.
EvalAI providing pro/con arguments improves provision-level accuracy and reduces misclassification distance in DSA illegal content reporting under AI error conditions versus conventional XAI.
A method that translates causal relationships into a Bipolar Argumentation Framework and applies semi-stable semantics to generate explanatory feature sets for machine learning predictions.
The paper proposes six interconnected elements of a design space to close the synergy gap in human-AI decision-making.
Mixed-methods study finds AI assistance linked to higher textual overlap with suggestions in writing tasks, and a reflective interface prototype increases user awareness of AI incorporation.
LLM reasoning traces and post-hoc explanations increase false trust in incorrect predictions, whereas contrastive dual explanations enhance users' ability to distinguish correct from incorrect AI outputs.
RUBEN discovers minimal rule sets explaining RAG LLM outputs via novel pruning and applies them to evaluate LLM safety against adversarial injections.
Interviews with 16 qualitative researchers identify efficiency, ownership, and trust as key factors shaping preferences for AI as a supportive assistant rather than a full collaborator or supervisor in qualitative data analysis.
An online experiment finds that showing users an overview of an AI's values reduces reliance on AI suggestions during writing tasks.
A qualitative-to-quantitative scoring framework is proposed to evaluate how well model-agnostic XAI methods support EU AI Act explainability requirements.
citing papers explorer
-
Agents for Experiments, Experiments for Agents: A Design Grammar for AI-Enabled Experimental Science
SEED is a structural encoding framework using typed actor-flow graphs to describe, evaluate novelty of, and generate experimental designs for AI-enabled science under feasibility and governance constraints.
-
When Bits Break Recourse: Counterfactual-Faithful Quantization
CFQ trains quantizer parameters and mixed-precision allocation to preserve counterfactual recourse validity, cost, and direction on Adult, German Credit, and COMPAS while matching accuracy of standard quantizers.
-
Collective Recourse for Generative Urban Visualizations
Collective recourse formalizes community reports to fix group harms in diffusion models for urban visualizations via a report-triage-fix-verify pipeline, four primitives, a mandate score, and synthetic evaluation of 240 reports.
-
From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recourse
Formalizing personalization as individual actionability in causal recourse shows hard constraints degrade validity and plausibility while revealing socio-demographic disparities in costs.
-
When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Making
Adversarial explanation attacks preserve nearly all human trust in wrong AI outputs by using persuasive framing, shown in a study varying reasoning, evidence, style, and format with over 200 participants.
-
To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making
Cognitive forcing interventions reduce overreliance on AI recommendations more than simple explanations, with effects moderated by individual need for cognition.
-
AI at the Front Lines of Platform Governance: Using LLMs to Support Illegal Content Reporting under the Digital Services Act
EvalAI providing pro/con arguments improves provision-level accuracy and reduces misclassification distance in DSA illegal content reporting under AI error conditions versus conventional XAI.
-
A Causal Argumentation Method for Explainability of Machine Learning Models
A method that translates causal relationships into a Bipolar Argumentation Framework and applies semi-stable semantics to generate explanatory feature sets for machine learning predictions.
-
Addressing the Synergy Gap: The Six Elements of the Design Space
The paper proposes six interconnected elements of a design space to close the synergy gap in human-AI decision-making.
-
Overreliance in Writing Tasks: Exploring Similarity-Based Measures of AI Influence on Writing and Proposing a Reflective Writing Interface Intervention
Mixed-methods study finds AI assistance linked to higher textual overlap with suggestions in writing tasks, and a reflective interface prototype increases user awareness of AI incorporation.
-
Evaluating the False Trust Engendered by LLM Explanations
LLM reasoning traces and post-hoc explanations increase false trust in incorrect predictions, whereas contrastive dual explanations enhance users' ability to distinguish correct from incorrect AI outputs.
-
RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems
RUBEN discovers minimal rule sets explaining RAG LLM outputs via novel pruning and applies them to evaluate LLM safety against adversarial injections.
-
Not a Collaborator or a Supervisor, but an Assistant: Striking the Balance Between Efficiency and Ownership in AI-incorporated Qualitative Data Analysis
Interviews with 16 qualitative researchers identify efficiency, ownership, and trust as key factors shaping preferences for AI as a supportive assistant rather than a full collaborator or supervisor in qualitative data analysis.
-
Framing an AI with Values Reduces AI Reliance in AI-supported Writing Tasks
An online experiment finds that showing users an overview of an AI's values reduces reliance on AI suggestions during writing tasks.
-
Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements
A qualitative-to-quantitative scoring framework is proposed to evaluate how well model-agnostic XAI methods support EU AI Act explainability requirements.