Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors

• URL: http://arxiv.org/abs/2404.03713v2
• Date: Thu, 13 Feb 2025 09:48:12 GMT
• Title: Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors
• Authors: Angus Nicolson, Lisa Schut, J. Alison Noble, Yarin Gal,
• Abstract summary: Concept Vector Activations (CAVs) are learnt using a probe dataset of concept exemplars. We investigate three properties of CAVs: inconsistency across layers, (2) entanglement with other concepts, and (3) spatial dependency. We introduce tools designed to detect the presence of these properties, provide insight into how each property can lead to misleading explanations, and provide recommendations to mitigate their impact.
• Score: 35.37586279472797
• License:
• Abstract: Concept-based explanations translate the internal representations of deep learning models into a language that humans are familiar with: concepts. One popular method for finding concepts is Concept Activation Vectors (CAVs), which are learnt using a probe dataset of concept exemplars. In this work, we investigate three properties of CAVs: (1) inconsistency across layers, (2) entanglement with other concepts, and (3) spatial dependency. Each property provides both challenges and opportunities in interpreting models. We introduce tools designed to detect the presence of these properties, provide insight into how each property can lead to misleading explanations, and provide recommendations to mitigate their impact. To demonstrate practical applications, we apply our recommendations to a melanoma classification task, showing how entanglement can lead to uninterpretable results and that the choice of negative probe set can have a substantial impact on the meaning of a CAV. Further, we show that understanding these properties can be used to our advantage. For example, we introduce spatially dependent CAVs to test if a model is translation invariant with respect to a specific concept and class. Our experiments are performed on natural images (ImageNet), skin lesions (ISIC 2019), and a new synthetic dataset, Elements. Elements is designed to capture a known ground truth relationship between concepts and classes. We release this dataset to facilitate further research in understanding and evaluating interpretability methods.

Related papers

• CoLiDR: Concept Learning using Aggregated Disentangled Representations [29.932706137805713]
  Interpretability of Deep Neural Networks using concept-based models offers a promising way to explain model behavior through human-understandable concepts. A parallel line of research focuses on disentangling the data distribution into its underlying generative factors, in turn explaining the data generation process. While both directions have received extensive attention, little work has been done on explaining concepts in terms of generative factors to unify mathematically disentangled representations and human-understandable concepts.
  arXiv  Detail & Related papers  (2024-07-27T16:55:14Z)
• Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models [53.337728969143086]
  Recommendation systems harness user-item interactions like clicks and reviews to learn their representations. Previous studies improve recommendation accuracy and interpretability by modeling user preferences across various aspects and intents. We introduce a chain-based prompting approach to uncover semantic aspect-aware interactions.
  arXiv  Detail & Related papers  (2023-12-26T15:44:09Z)
• Interpreting Pretrained Language Models via Concept Bottlenecks [55.47515772358389]
  Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. The lack of interpretability due to their black-box'' nature poses challenges for responsible implementation. We propose a novel approach to interpreting PLMs by employing high-level, meaningful concepts that are easily understandable for humans.
  arXiv  Detail & Related papers  (2023-11-08T20:41:18Z)
• Uncovering Unique Concept Vectors through Latent Space Decomposition [0.0]
  Concept-based explanations have emerged as a superior approach that is more interpretable than feature attribution estimates. We propose a novel post-hoc unsupervised method that automatically uncovers the concepts learned by deep models during training. Our experiments reveal that the majority of our concepts are readily understandable to humans, exhibit coherency, and bear relevance to the task at hand.
  arXiv  Detail & Related papers  (2023-07-13T17:21:54Z)
• Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability [25.545486537295144]
  Concept-based interpretability methods aim to explain deep neural network model predictions using a predefined set of semantic concepts. Despite their popularity, they suffer from limitations that are not well-understood and articulated by the literature. We analyze three commonly overlooked factors in concept-based explanations.
  arXiv  Detail & Related papers  (2022-07-20T01:59:39Z)
• Discovering Concepts in Learned Representations using Statistical Inference and Interactive Visualization [0.76146285961466]
  Concept discovery is important for bridging the gap between non-deep learning experts and model end-users. Current approaches include hand-crafting concept datasets and then converting them to latent space directions. In this study, we offer another two approaches to guide user discovery of meaningful concepts, one based on multiple hypothesis testing, and another on interactive visualization.
  arXiv  Detail & Related papers  (2022-02-09T22:29:48Z)
• Translational Concept Embedding for Generalized Compositional Zero-shot Learning [73.60639796305415]
  Generalized compositional zero-shot learning means to learn composed concepts of attribute-object pairs in a zero-shot fashion. This paper introduces a new approach, termed translational concept embedding, to solve these two difficulties in a unified framework.
  arXiv  Detail & Related papers  (2021-12-20T21:27:51Z)
• DISSECT: Disentangled Simultaneous Explanations via Concept Traversals [33.65478845353047]
  DISSECT is a novel approach to explaining deep learning model inferences. By training a generative model from a classifier's signal, DISSECT offers a way to discover a classifier's inherent "notion" of distinct concepts. We show that DISSECT produces CTs that disentangle several concepts and are coupled to its reasoning due to joint training.
  arXiv  Detail & Related papers  (2021-05-31T17:11:56Z)
• A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics [131.93113552146195]
  We present a new dataset, Handwritten arithmetic with INTegers (HINT), to examine machines' capability of learning generalizable concepts. In HINT, machines are tasked with learning how concepts are perceived from raw signals such as images. We undertake extensive experiments with various sequence-to-sequence models, including RNNs, Transformers, and GPT-3.
  arXiv  Detail & Related papers  (2021-03-02T01:32:54Z)
• Contrastive Explanations for Model Interpretability [77.92370750072831]
  We propose a methodology to produce contrastive explanations for classification models. Our method is based on projecting model representation to a latent space. Our findings shed light on the ability of label-contrastive explanations to provide a more accurate and finer-grained interpretability of a model's decision.
  arXiv  Detail & Related papers  (2021-03-02T00:36:45Z)
• Explainable Recommender Systems via Resolving Learning Representations [57.24565012731325]
  Explanations could help improve user experience and discover system defects. We propose a novel explainable recommendation model through improving the transparency of the representation learning process.
  arXiv  Detail & Related papers  (2020-08-21T05:30:48Z)

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.