mlsanityData to 7 Oct 2026

Visual Evaluative AI: A Hypothesis-Driven Tool with Concept-Based Explanations and Weight of Evidence

arXiv 2407.04710 ↗First posted 13 May 2024cs.CV cs.AI cs.HCcited by 1

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Abstract (arXiv)

This paper presents Visual Evaluative AI, a decision aid that provides positive and negative evidence from image data for a given hypothesis. This tool finds high-level human concepts in an image and generates the Weight of Evidence (WoE) for each hypothesis in the decision-making process. We apply and evaluate this tool in the skin cancer domain by building a web-based application that allows users to upload a dermatoscopic image, select a hypothesis and analyse their decisions by evaluating the provided evidence. Further, we demonstrate the effectiveness of Visual Evaluative AI on different concept-based explanation approaches.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 13 May 2024.

Builds on

10 of 19 references.

References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  3. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,309
  4. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
  5. 2022Post-hoc Concept Bottleneck Modelscited by 289
  6. 2020Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectorscited by 119
  7. 2023Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision supportcited by 65
  8. 2023Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decisioncited by 27
  9. 2021From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidencecited by 17
  10. 2024From Evidence to Decision: Exploring Evaluative AIcited by 3

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024An Empirical Examination of the Evaluative AI Frameworkcited by 0