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Incremental XAI: Memorable Understanding of AI with Incremental Explanations
Focusing on linear factor explanations (factors $\times$ values = outcome), we introduce Incremental XAI to automatically partition explanations for general and atypical instances by providing Base + Incremental factors to help users read and remember more faithful explanations.
IRIS: Interpretable Rubric-Informed Segmentation for Action Quality Assessment
Indeed, to account for their decisions, instead of scoring subjectively, sports judges use a consistent set of criteria - rubric - on multiple actions in each performance sequence.
RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions
With model explanations of the proxy model, we curated a rubric to adjust text prompts to optimize image generation for precise emotion expression.
Diagrammatization: Rationalizing with diagrammatic AI explanations for abductive-deductive reasoning on hypotheses
Many visualizations have been developed for explainable AI (XAI), but they often require further reasoning by users to interpret.
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning
We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for explanation and bias level predictions.
Towards Relatable Explainable AI with the Perceptual Process
Inspired by the perceptual process from cognitive psychology, we propose the XAI Perceptual Processing Framework and RexNet model for relatable explainable AI with Contrastive Saliency, Counterfactual Synthetic, and Contrastive Cues explanations.
Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation
We propose Interpretable Directed Diversity to automatically predict ideation quality and diversity scores, and provide AI explanations - Attribution, Contrastive Attribution, and Counterfactual Suggestions - to feedback on why ideations were scored (low), and how to get higher scores.
SalienTrack: providing salient information for semi-automated self-tracking feedback with model explanations
Finally, we created interfaces to present salient information and conducted a formative user study to gain insights about how SalienTrack could be integrated into an interface for reflection.
Exploiting Explanations for Model Inversion Attacks
We study this risk for image-based model inversion attacks and identified several attack architectures with increasing performance to reconstruct private image data from model explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations
Feature attribution is widely used in interpretable machine learning to explain how influential each measured input feature value is for an output inference.
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning
We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for explanation and bias level predictions.
NaMemo: Enhancing Lecturers' Interpersonal Competence of Remembering Students' Names
Addressing students by their names helps a teacher to start building rapport with students and thus facilitates their classroom participation.
