counterfactual explanations in explainable ai: a tutorialnew zealand bowling coach 2021

CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines Arjun R. Akula1, Shuai Wang2, Song-Chun Zhu1 1UCLA Center for Vision, Cognition, Learning, and Autonomy 2University of Illinois at Chicago aakula@ucla.edu, shuaiwanghk@gmail.com, sczhu@stat.ucla.edu Counterfactuals about what could have happened are increasingly used in an array of Artificial Intelligence (AI) applications, and especially in explainable AI (XAI). In the area of explainable AI, counterfactual explanation would be contrastive in nature and would be better received by the human receiving the explanation. One commonly used way to measure faithfulness is \\textit{erasure-based} criteria. Most of the explainable AI techniques prevalent today provide outputs that can only be understood and analyzed by AI experts, data scientists, and probably, ML engineers. Counterfactual Explanations in Explainable AI: A Tutorial. Algorithmic approaches to interpreting machine learning models have proliferated in recent years. Evaluate Explainable ML. Recap of interpretability techniques learned in this book. This post is co-authored by Aalok Shanbhag and Ankur Taly.

2020] Back to doing research at Google! [Jul. Machine intelligence can produce formidable algorithms and explainable AI tools. In order to address these issues, they proposed an improved faster, model agnostic technique for finding explainable counterfactual explanations of classifier predictions. According to philosophy, social science, and psychology theories, a common definition of explainability or interpretability is the degree to which a human can understand the reasons behind a decision or an action [Mil19].The explainability of AI/ML algorithms can be achieved by (1) making the entire decision-making process transparent and comprehensible and (2 . Counterfactual explanations (CFEs) are an emerging technique for local, example-based post-hoc explanations methods. The explainability techniques used in explainable AI are heavily influenced by how humans make inferences and form conclusions, which allows them to be replicated within an explainable artificial . We carry out human subject tests that are the first of their . Understanding the properties of an explainable AI system. by. In combination with methods from deep learning, RL is currently applied in a number of different scenarios that have a significant impact in society, including . True. Minute Read. In Chapter 9, The Counterfactual Explanations Method, we used the CEM to explain the distances between the data points; In this chapter, we used everyday, common human cognitive sense to understand the problem; This is an example of how explainable AI and cognitive human input can work together to help a user understand an explanation June 10, 2021. %A Hase, Peter %A Bansal, Mohit %S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics %D 2020 %8 jul %I Association for Computational Linguistics %C Online %F hase-bansal-2020-evaluating %X Algorithmic approaches to interpreting . Human curated counterfactual edits on VQA images for studying effective ways to produce counterfactual explanations. A model is simulatable when a person can predict its behavior on . On left, there's the original factual input [1,0,1], the algorithm generate the possible CF in the first loop, verifies if any changed the output classification (≥0.5), if not, gets the best improvement (shown in orange), and follow to a new round of possible CF generation until generates one that flipped the . Abstract.
[Jan. 2020] Co-instructed a tutorial on Explainable AI in industry at FAT* 2020. Interpret ⭐ 4,275. This work concentrates on post-hoc explanation-by-example solutions to XAI as one approach to explaining black box deep-learning systems. Afterwards, we review combinatorial methods for explainable AI which are based on combinatorial testing-based approaches to fault localization. Recently, artificial intelligence has seen the explosion of deep learning models, which are able to reach super-human performance in several tasks, finding application in many domains. A feature attribution method is a function that will accept . dialog, between the machine and human user . Algorithmic approaches to interpreting machine learning models have proliferated in recent years. In the field of Explainable AI, a recent area of exciting and rapid development has been counterfactual explanations.

We measure human users' ability to predict model behavior. Most explanation techniques, however, face an inherent tradeoff between fidelity and interpretability: a high-fidelity explanation for an ML model tends to be complex and hard to interpret, while an interpretable explanation is often inconsistent with the ML model it was meant to explain. In response to this disquiet counterfactual explanations have become massively popular in eXplainable AI (XAI) due to their proposed computational psychological, and legal benefits. Counterfactual Explanation. Mindsdb ⭐ 4,093.
Given a datapoint A and its prediction P from a model, a counterfactual is a datapoint close to A, such that the model predicts it to be in a different class Q (P ≠ Q).

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While recent years have witnessed the emergence of various explainable methods in machine learning, to what degree the explanations really represent the reasoning process behind the model prediction -- namely, the faithfulness of explanation -- is still an open problem. We take a structural approach to the problem of explainable AI, examine the feasibility of these aspects and extend them where appropriate. Authors: Sahil Verma, Keegan Hines, John Dickerson. The Price of Interpretability. Accuracy Pre Sim. Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. Other models, such as so-called counterfactual explanations or heatmaps, are also possible (9, 10). Feature attribution is an important part of post-modeling (also called post hoc) explanation generation and facilitates such desiderata. Using the MDP framework, we introduce the concept of a counterfactual state as a counterfactual explanation. Reinforcement Learning for Counterfactual Explanations.

Explanations are Selected. Logic to generate a counterfactual explanation used by the algorithm above. Common questions asked of an explainable AI system and applying interpretability techniques to answer them. Counterfactual Explanations in Explainable AI: A Tutorial Author(s): Cong WANG (Huawei Technologies)*; Xiao-Hui Li (Huawei Technologies); Haocheng Han (Huawei Technologies); Luning Wang (Huawei Technologies); Caleb Chen Cao (Huawei Technologies); Lei Chen (Hong Kong University of Science and Technology) Understanding the theory of an ML algorithm is enough for XAI.

This paper profiles the recent research work on eXplainable AI (XAI), at the Insight Centre for Data Analytics. explainable artificial intelligence emerges, aiming to explain the predictions and behaviors of deep learning models. Today the most common explainable AI methods used are: The Layer-wise Relevance Propagation (LRP - 2015 (1)).

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