Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness

• URL: http://arxiv.org/abs/2001.05573v1
• Date: Mon, 13 Jan 2020 18:45:48 GMT
• Title: Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness
• Authors: Michael Hind, Dennis Wei, Yunfeng Zhang
• Abstract summary: This paper builds upon an alternative consumer-driven approach called TED that asks for explanations to be provided in training data, along with target labels. Experiments are conducted to investigate some practical considerations with TED, including its performance with different classification algorithms.
• Score: 35.520178007455556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many proposed methods for explaining machine learning predictions are in fact challenging to understand for nontechnical consumers. This paper builds upon an alternative consumer-driven approach called TED that asks for explanations to be provided in training data, along with target labels. Using semi-synthetic data from credit approval and employee retention applications, experiments are conducted to investigate some practical considerations with TED, including its performance with different classification algorithms, varying numbers of explanations, and variability in explanations. A new algorithm is proposed to handle the case where some training examples do not have explanations. Our results show that TED is robust to increasing numbers of explanations, noisy explanations, and large fractions of missing explanations, thus making advances toward its practical deployment.

Related papers

• Selective Explanations [14.312717332216073]
  A machine learning model is trained to predict feature attribution scores with only one inference. Despite their efficiency, amortized explainers can produce inaccurate predictions and misleading explanations. We propose selective explanations, a novel feature attribution method that detects when amortized explainers generate low-quality explanations.
  arXiv  Detail & Related papers  (2024-05-29T23:08:31Z)
• Evaluating the Utility of Model Explanations for Model Development [54.23538543168767]
  We evaluate whether explanations can improve human decision-making in practical scenarios of machine learning model development. To our surprise, we did not find evidence of significant improvement on tasks when users were provided with any of the saliency maps. These findings suggest caution regarding the usefulness and potential for misunderstanding in saliency-based explanations.
  arXiv  Detail & Related papers  (2023-12-10T23:13:23Z)
• Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis [6.606409729669314]
  Post-hoc explainability methods aim to clarify predictions of black-box machine learning models. We conduct a user study to evaluate comprehensibility and predictability in two widely used tools: LIME and SHAP. We find that the comprehensibility of SHAP is significantly reduced when explanations are provided for samples near a model's decision boundary.
  arXiv  Detail & Related papers  (2023-09-21T11:54:20Z)
• Explaining Explainability: Towards Deeper Actionable Insights into Deep Learning through Second-order Explainability [70.60433013657693]
  Second-order explainable AI (SOXAI) was recently proposed to extend explainable AI (XAI) from the instance level to the dataset level. We demonstrate for the first time, via example classification and segmentation cases, that eliminating irrelevant concepts from the training set based on actionable insights from SOXAI can enhance a model's performance.
  arXiv  Detail & Related papers  (2023-06-14T23:24:01Z)
• Explainable Data-Driven Optimization: From Context to Decision and Back Again [76.84947521482631]
  Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. We introduce a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We demonstrate our approach by explaining key problems in operations management such as inventory management and routing.
  arXiv  Detail & Related papers  (2023-01-24T15:25:16Z)
• Explanation as a process: user-centric construction of multi-level and multi-modal explanations [0.34410212782758043]
  We present a process-based approach that combines multi-level and multi-modal explanations. We use Inductive Logic Programming, an interpretable machine learning approach, to learn a comprehensible model.
  arXiv  Detail & Related papers  (2021-10-07T19:26:21Z)
• On the Objective Evaluation of Post Hoc Explainers [10.981508361941335]
  Modern trends in machine learning research have led to algorithms that are increasingly intricate to the degree that they are considered to be black boxes. In an effort to reduce the opacity of decisions, methods have been proposed to construe the inner workings of such models in a human-comprehensible manner. We propose a framework for the evaluation of post hoc explainers on ground truth that is directly derived from the additive structure of a model.
  arXiv  Detail & Related papers  (2021-06-15T19:06:51Z)
• Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations [64.85696493596821]
  In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction. We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss. Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-18T12:57:34Z)
• Evaluating Explanations: How much do explanations from the teacher aid students? [103.05037537415811]
  We formalize the value of explanations using a student-teacher paradigm that measures the extent to which explanations improve student models in learning. Unlike many prior proposals to evaluate explanations, our approach cannot be easily gamed, enabling principled, scalable, and automatic evaluation of attributions.
  arXiv  Detail & Related papers  (2020-12-01T23:40:21Z)
• Explanations of Black-Box Model Predictions by Contextual Importance and Utility [1.7188280334580195]
  We present the Contextual Importance (CI) and Contextual Utility (CU) concepts to extract explanations easily understandable by experts as well as novice users. This method explains the prediction results without transforming the model into an interpretable one. We show the utility of explanations in car selection example and Iris flower classification by presenting complete (i.e. the causes of an individual prediction) and contrastive explanation.
  arXiv  Detail & Related papers  (2020-05-30T06:49:50Z)

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.