ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations

Samangouei, Pouya; Saeedi, Ardavan; Nakagawa, Liam; Silberman, Nathan

doi:10.1007/978-3-030-01249-6_41

Cited by 40 publications

(39 citation statements)

References 15 publications

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“…[62] showed the model's partial decision boundary by traversing the latent space around a specific input, in order to show how the model behaves as the input changes. The methods were initially proposed in the computer vision domain [67,119], whereas Hase and Bansal [62] developed and adapted the method for text and tabular data.…”

Section: Example-based Are Often Called Counterfactual Examples By Pr...mentioning

confidence: 99%

Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies

Lai¹,

Chen²,

Liao³

et al. 2021

Preprint

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As AI systems demonstrate increasingly strong predictive performance, their adoption has grown in numerous domains. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI models and AI assistance elements, and(3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our survey highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other's work and produce generalizable scientific knowledge. We also hope this survey will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.

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Section: Example-based Are Often Called Counterfactual Examples By Pr...mentioning

confidence: 99%

Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies

Lai¹,

Chen²,

Liao³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Zhang et al provided an interpretation for regression saliency maps, as well as an adaptation of the perturbation-based quantitative evaluation of explanation methods [136]. ExplainGAN is a generative model that produces visually perceptible decision-boundary crossing transformations, which provide high-level conceptual insights that illustrate the manner in which a model makes decisions [137]. We proposed a barcode-like timeline to visualize the progress of the probability of substructure detection along with sweep scanning in US videos.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Komatsu

Sakai

Dozen³

et al. 2021

Biomedicines

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Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.

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“…Model Interpretability: PROVER follows a significant body of previous work on developing interpretable neural models for NLP tasks to foster explainability. Several approaches have focused on formalizing the notion of interpretability (Rudin, 2019;Doshi-Velez and Kim, 2017;Hase and Bansal, 2020), tweaking features for local model interpretability (Ribeiro et al, 2016(Ribeiro et al, , 2018 and exploring interpretability in latent spaces (Joshi et al, 2018;Samangouei et al, 2018). Our work can be seen as generating explanations in the form of proofs for an NLP task.…”

Section: Related Workmentioning

confidence: 99%

PRover: Proof Generation for Interpretable Reasoning over Rules

Saha

Ghosh

Srivastava

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Recent work by Clark et al. (2020) shows that transformers can act as "soft theorem provers" by answering questions over explicitly provided knowledge in natural language. In our work, we take a step closer to emulating formal theorem provers, by proposing PROVER, an interpretable transformer-based model that jointly answers binary questions over rule-bases and generates the corresponding proofs. Our model learns to predict nodes and edges corresponding to proof graphs in an efficient constrained training paradigm. During inference, a valid proof, satisfying a set of global constraints is generated. We conduct experiments on synthetic, hand-authored, and human-paraphrased rule-bases to show promising results for QA and proof generation, with strong generalization performance. First, PROVER generates proofs with an accuracy of 87%, while retaining or improving performance on the QA task, compared to RuleTakers (up to 6% improvement on zero-shot evaluation). Second, when trained on questions requiring lower depths of reasoning, it generalizes significantly better to higher depths (up to 15% improvement). Third, PROVER obtains near perfect QA accuracy of 98% using only 40% of the training data. However, generating proofs for questions requiring higher depths of reasoning becomes challenging, and the accuracy drops to 65% for "depth 5", indicating significant scope for future work. 1

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ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations

Cited by 40 publications

References 15 publications

Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies

Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

PRover: Proof Generation for Interpretable Reasoning over Rules

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