DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models

Cheng, Furui; Yao, Ming; Qu, Huamin

doi:10.1109/tvcg.2020.3030342

Cited by 80 publications

(70 citation statements)

References 32 publications

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“…The domain of ML-fairness related visualization work has seen significant attention in the last few years [17,38,55], which can be observed by the growing research interest [16,60]. To support this need for visualization-based ML-fairness methods, an interview study [37] with domain experts found that for resolving fairness problems, people want to have automatic tools to detect bias.…”

Section: Related Workmentioning

confidence: 99%

Visual Identification of Problematic Bias in Large Label Spaces

Bäuerle¹,

Turker²,

Burke³

et al. 2022

Preprint

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While the need for well-trained, fair ML systems is increasing ever more, measuring fairness for modern models and datasets is becoming increasingly difficult as they grow at an unprecedented pace. One key challenge in scaling common fairness metrics to such models and datasets is the requirement of exhaustive ground truth labeling, which cannot always be done. Indeed, this often rules out the application of traditional analysis metrics and systems. At the same time, ML-fairness assessments cannot be made algorithmically, as fairness is a highly subjective matter. Thus, domain experts need to be able to extract and reason about bias throughout models and datasets to make informed decisions. While visual analysis tools are of great help when investigating potential bias in DL models, none of the existing approaches have been designed for the specific tasks and challenges that arise in large label spaces. Addressing the lack of visualization work in this area, we propose guidelines for designing visualizations for such large label spaces, considering both technical and ethical issues. Our proposed visualization approach can be integrated into classical model and data pipelines, and we provide an implementation of our techniques open-sourced as a TensorBoard plug-in. With our approach, different models and datasets for large label spaces can be systematically and visually analyzed and compared to make informed fairness assessments tackling problematic bias.

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Section: Related Workmentioning

confidence: 99%

Visual Identification of Problematic Bias in Large Label Spaces

Bäuerle¹,

Turker²,

Burke³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…After obtaining qualified counterfactual examples, some work (Cheng et al, 2020;Wachter et al, 2520Wachter et al, 2017Verma et al, 2020) provides them as counterfactual explanation directly. However, since counterfactual examples do not provide explanations explicitly, it could be difficult for users to understand.…”

Section: Contrastive Explanation Generationmentioning

confidence: 99%

“…Meanwhile, the generated examples should be proximal to the original instance as described in (Cheng et al, 2020), which means only a small change needs to be made. We do not expect a big change that transforms a large portion of the original, in which way there will be no difference with merely presenting an example of counter classes and the corresponding explanation will be uninformative or useless.…”

Section: Counterfactual Example Generationmentioning

confidence: 99%

“…• We consider the rationales in inputs and regard them as important perturbations for generating counterfactual examples rather than just discarding them like previous post-hoc work (Hendricks et al, 2018;Cheng et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the majority of existing methods (Xu et al, 2020;Cheng et al, 2020;Karimi et al, 2020;Ramamurthy et al, 2020;Atanasova et al, 2020;Kumar and Talukdar, 2020) in natural language processing try to explain the predictions of neural models in a model-intrinsic or model-agnostic (also known as post-hoc) way. While post-hoc models (Chen et al, 2020b;Karimi et al, 2020;Kumar and Talukdar, 2020) provide explanations after making predictions without affecting the overall accuracy, most of them neglect the rationales in inputs and provide textual explanations just in the form of "WHY A".…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference

Chen¹,

Ji²,

Zeng³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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In order to better understand the reason behind model behaviors (i.e., making predictions), most recent work has exploited generative models to provide complementary explanations. However, existing approaches in natural language processing (NLP) mainly focus on "WHY A" rather than contrastive "WHY A NOT B", which is shown to be able to better distinguish confusing candidates and improve model performance in other research fields. In this paper, we focus on generating Contrastive Explanations with counterfactual examples in NLI and propose a novel Knowledge-Aware generation framework (KACE). Specifically, we first identify rationales (i.e., key phrases) from input sentences, and use them as key perturbations for generating counterfactual examples. After obtaining qualified counterfactual examples, we take them along with original examples and external knowledge as input, and employ a knowledge-aware generative pre-trained language model to generate contrastive explanations. Experimental results show that contrastive explanations are beneficial to clarify the difference between predicted answer and other answer options. Moreover, we train an BERT-large based NLI model enhanced with contrastive explanations and achieve an accuracy of 91.9% on SNLI, gaining an improvement of 5.7% against ETPA ("Explain-Then-Predict-Attention") and 0.6% against NILE ("WHY A"). * Work is done during internship at Alibaba Group. † The work is mainly conducted while being at Alibaba Group.

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Model Trees for Identifying Exceptional Players in the NHL and NBA Drafts

Liu

2019

Lecture Notes in Computer Science

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The field of machine learning has seen tremendous progress in recent years, with deep learning models delivering exceptional performance across a range of tasks. However, these models often come at the cost of interpretability, as they operate as opaque "black boxes" that obscure the rationale behind their decisions. This lack of transparency can limit understanding of the models' underlying principles and impede their deployment in sensitive domains, such as healthcare or finance. To address this challenge, our research team has proposed an innovative framework designed to reconcile the trade-off between model performance and interpretability. Our approach is centered around modular operations on high-dimensional data, which enable end-to-end processing while preserving interpretability. By fusing diverse interpretability techniques and modularized data processing, our framework sheds light on the decision-making processes of complex models without compromising their performance. We have extensively tested our framework and validated its superior efficacy in achieving a harmonious balance between computational efficiency and interpretability. Our approach addresses a critical need in contemporary machine learning applications by providing unprecedented insights into the inner workings of complex models, fostering trust, transparency, and accountability in their deployment across diverse domains.

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DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models

Cited by 80 publications

References 32 publications

Visual Identification of Problematic Bias in Large Label Spaces

Visual Identification of Problematic Bias in Large Label Spaces

KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference

Model Trees for Identifying Exceptional Players in the NHL and NBA Drafts

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