Insights into Data through Model Behaviour: An Explainability-driven Strategy for Data Auditing for Responsible Computer Vision Applications

• URL: http://arxiv.org/abs/2106.09177v1
• Date: Wed, 16 Jun 2021 23:46:39 GMT
• Title: Insights into Data through Model Behaviour: An Explainability-driven Strategy for Data Auditing for Responsible Computer Vision Applications
• Authors: Alexander Wong, Adam Dorfman, Paul McInnis, and Hayden Gunraj
• Abstract summary: This study explores an explainability-driven strategy to data auditing. We demonstrate this strategy by auditing two popular medical benchmark datasets. We discover hidden data quality issues that lead deep learning models to make predictions for the wrong reasons.
• Score: 70.92379567261304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we take a departure and explore an explainability-driven strategy to data auditing, where actionable insights into the data at hand are discovered through the eyes of quantitative explainability on the behaviour of a dummy model prototype when exposed to data. We demonstrate this strategy by auditing two popular medical benchmark datasets, and discover hidden data quality issues that lead deep learning models to make predictions for the wrong reasons. The actionable insights gained from this explainability driven data auditing strategy is then leveraged to address the discovered issues to enable the creation of high-performing deep learning models with appropriate prediction behaviour. The hope is that such an explainability-driven strategy can be complimentary to data-driven strategies to facilitate for more responsible development of machine learning algorithms for computer vision applications.

Related papers

• Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance [61.06245197347139]
  We propose a novel approach to explain the behavior of a black-box model under feature shifts. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation.
  arXiv  Detail & Related papers  (2024-08-24T18:28:19Z)
• Verification of Machine Unlearning is Fragile [48.71651033308842]
  We introduce two novel adversarial unlearning processes capable of circumventing both types of verification strategies. This study highlights the vulnerabilities and limitations in machine unlearning verification, paving the way for further research into the safety of machine unlearning.
  arXiv  Detail & Related papers  (2024-08-01T21:37:10Z)
• Defogger: A Visual Analysis Approach for Data Exploration of Sensitive Data Protected by Differential Privacy [5.117818675551463]
  We take the lead in describing corresponding exploration scenarios, including underlying requirements and available exploration strategies. Our approach applies a reinforcement learning model to provide diverse suggestions for exploration strategies according to the exploration intent of users. A novel visual design for representing uncertainty in correlation patterns is integrated into our prototype system to support the proposed approach.
  arXiv  Detail & Related papers  (2024-07-28T02:14:12Z)
• DISCOVER: A Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of Human Behaviour [6.716560115378451]
  We introduce a modular, flexible, yet user-friendly software framework specifically developed to streamline computational-driven data exploration for human behavior analysis. Our primary objective is to democratize access to advanced computational methodologies, thereby enabling researchers across disciplines to engage in detailed behavioral analysis without the need for extensive technical proficiency.
  arXiv  Detail & Related papers  (2024-07-18T11:28:52Z)
• Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation [3.2340528215722553]
  A systematic task formulation of continual neural information retrieval is presented. A comprehensive continual neural information retrieval framework is proposed. Empirical evaluations illustrate that the proposed framework can successfully prevent catastrophic forgetting in neural information retrieval.
  arXiv  Detail & Related papers  (2023-08-16T14:01:25Z)
• Predicting Seriousness of Injury in a Traffic Accident: A New Imbalanced Dataset and Benchmark [62.997667081978825]
  The paper introduces a new dataset to assess the performance of machine learning algorithms in the prediction of the seriousness of injury in a traffic accident. The dataset is created by aggregating publicly available datasets from the UK Department for Transport.
  arXiv  Detail & Related papers  (2022-05-20T21:15:26Z)
• Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach [80.8446673089281]
  We propose a new learning paradigm with graph representation and learning. Our framework contains two modules: 1) a backbone network (e.g., feedforward neural nets) as a lower model takes features as input and outputs predicted labels; 2) a graph neural network as an upper model learns to extrapolate embeddings for new features via message passing over a feature-data graph built from observed data.
  arXiv  Detail & Related papers  (2021-10-09T09:02:45Z)
• Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles [69.9674326582747]
  This paper presents a visual framework to investigate neural network models subjected to adversarial examples. We show how observing these elements can quickly pinpoint exploited areas in a model.
  arXiv  Detail & Related papers  (2021-03-18T13:04:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.