Be Informed and Be Involved

Zhou, Jianlong; Bridon, Constant; Chen, Fang; Khawaji, Ahmad; Wang, Yang

doi:10.1145/2702613.2732769

Cited by 17 publications

(2 citation statements)

References 9 publications

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“…For example, Hoffman et al [74] presented metrics for explainable systems that are grounded in the subjective evaluation of a system (e.g., user trust, satisfaction, and understanding). Zhou, et al [65,75] investigated factors such as uncertainty and correlation that affect user confidence in ML-informed decision-making. Zhou et al [64] found that the explanation of influence of training data points significantly affected user trust in ML-informed decision-making.…”

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confidence: 99%

Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics

et al. 2021

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The most successful Machine Learning (ML) systems remain complex black boxes to end-users, and even experts are often unable to understand the rationale behind their decisions. The lack of transparency of such systems can have severe consequences or poor uses of limited valuable resources in medical diagnosis, financial decision-making, and in other high-stake domains. Therefore, the issue of ML explanation has experienced a surge in interest from the research community to application domains. While numerous explanation methods have been explored, there is a need for evaluations to quantify the quality of explanation methods to determine whether and to what extent the offered explainability achieves the defined objective, and compare available explanation methods and suggest the best explanation from the comparison for a specific task. This survey paper presents a comprehensive overview of methods proposed in the current literature for the evaluation of ML explanations. We identify properties of explainability from the review of definitions of explainability. The identified properties of explainability are used as objectives that evaluation metrics should achieve. The survey found that the quantitative metrics for both model-based and example-based explanations are primarily used to evaluate the parsimony/simplicity of interpretability, while the quantitative metrics for attribution-based explanations are primarily used to evaluate the soundness of fidelity of explainability. The survey also demonstrated that subjective measures, such as trust and confidence, have been embraced as the focal point for the human-centered evaluation of explainable systems. The paper concludes that the evaluation of ML explanations is a multidisciplinary research topic. It is also not possible to define an implementation of evaluation metrics, which can be applied to all explanation methods.

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Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics

et al. 2021

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“…Research in HCI has started to focus on uncertainty visualization and communication recently as well, for the exploration of personal genomics data [29], data analysis [5], machine learning [15,36], bus arrival predictions [13], range anxiety in electric cars [12] and other applications.…”

Section: Visualization Of Uncertaintymentioning

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Input Controls for Entering Uncertain Data

Greis

Schuff

Kleiner

et al. 2017

Proc. ACM Hum.-Comput. Interact.

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Although more and more data is collected automatically, many interfaces still require manual input. When we, for example, enter our daily calorie intake or calculate our ecological footprint, we often have to guess the weight of the food or what distance we have covered with our car. In this paper, we propose a solution to overcome the problem of forcing users to enter a single value when they are unsure about the actual input. On the basis of a slider, we designed four input controls which allow the input of uncertain data in the form of probability distribution functions. To evaluate our input controls, we conducted two studies collecting subjective and objective feedback. Based on the evaluation, we derived implications for their usage. We additionally provide an open-source toolkit with the evaluated input controls that can be included in web applications and customized for different contexts and tasks.

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Physiological Indicators for User Trust in Machine Learning with Influence Enhanced Fact-Checking

Zhou¹,

Hu²,

Li³

et al. 2019

Lecture Notes in Computer Science

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Trustworthy Machine Learning (ML) is one of significant challenges of "black-box" ML for its wide impact on practical applications. This paper investigates the effects of presentation of influence of training data points on machine learning predictions to boost user trust. A framework of fact-checking for boosting user trust is proposed in a predictive decision making scenario to allow users to interactively check the training data points with different influences on the prediction by using parallel coordinates based visualization. This work also investigates the feasibility of physiological signals such as Galvanic Skin Response (GSR) and Blood Volume Pulse (BVP) as indicators for user trust in predictive decision making. A user study found that the presentation of influences of training data points significantly increases the user trust in predictions, but only for training data points with higher influence values under the high model performance condition, where users can justify their actions with more similar facts to the testing data point. The physiological signal analysis showed that GSR and BVP features correlate to user trust under different influence and model performance conditions. These findings suggest that physiological indicators can be integrated into the user interface of AI applications to automatically communicate user trust variations in predictive decision making.

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Be Informed and Be Involved

Cited by 17 publications

References 9 publications

Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics

Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics

Input Controls for Entering Uncertain Data

Physiological Indicators for User Trust in Machine Learning with Influence Enhanced Fact-Checking

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