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1. Communicating Uncertain Information from Deep Learning Models in Human Machine Teams

   Subramanian, H. V. ; Canfield, C ; Shank, D. B. ; Andrews, L. ; Dagli, C. ( October 2020 , Proceedings of the American Society for Engineering Management 2020 International Annual Conference)

   Keathley, H. ; Enos, J. ; Parrish, M. (Ed.)

   The role of human-machine teams in society is increasing, as big data and computing power explode. One popular approach to AI is deep learning, which is useful for classification, feature identification, and predictive modeling. However, deep learning models often suffer from inadequate transparency and poor explainability. One aspect of human systems integration is the design of interfaces that support human decision-making. AI models have multiple types of uncertainty embedded, which may be difficult for users to understand. Humans that use these tools need to understand how much they should trust the AI. This study evaluates one simple approach for communicating uncertainty, a visual confidence bar ranging from 0-100%. We perform a human-subject online experiment using an existing image recognition deep learning model to test the effect of (1) providing single vs. multiple recommendations from the AI and (2) including uncertainty information. For each image, participants described the subject in an open textbox and rated their confidence in their answers. Performance was evaluated at four levels of accuracy ranging from the same as the image label to the correct category of the image. The results suggest that AI recommendations increase accuracy, even if the human and AI have different definitions of accuracy. In addition, providing multiple ranked recommendations, with or without the confidence bar, increases operator confidence and reduces perceived task difficulty. More research is needed to determine how people approach uncertain information from an AI system and develop effective visualizations for communicating uncertainty. 

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2. Combining human predictions with model probabilities via confusion matrices and calibration

   Kerrigan, Gavin ; Smyth, Padhraic ; Steyvers, Mark ( December 2021 , Advances in Neural Information Processing Systems (NeurIPS 2021))

   An increasingly common use case for machine learning models is augmenting the abilities of human decision makers. For classification tasks where neither the human nor model are perfectly accurate, a key step in obtaining high performance is combining their individual predictions in a manner that leverages their relative strengths. In this work, we develop a set of algorithms that combine the probabilistic output of a model with the class-level output of a human. We show theoretically that the accuracy of our combination model is driven not only by the individual human and model accuracies, but also by the model's confidence. Empirical results on image classification with CIFAR-10 and a subset of ImageNet demonstrate that such human-model combinations consistently have higher accuracies than the model or human alone, and that the parameters of the combination method can be estimated effectively with as few as ten labeled datapoints. 

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3. Combining human predictions with model probabilities via confusion matrices and calibration

   Kerrigan, G. ; Smyth, P. ; Steyvers, M. ( January 2021 , Advances in Neural Information Processing Systems, 34)

   An increasingly common use case for machine learning models is augmenting the abilities of human decision makers. For classification tasks where neither the human nor model are perfectly accurate, a key step in obtaining high performance is combining their individual predictions in a manner that leverages their relative strengths. In this work, we develop a set of algorithms that combine the probabilistic output of a model with the class-level output of a human. We show theoretically that the accuracy of our combination model is driven not only by the individual human and model accuracies, but also by the model's confidence. Empirical results on image classification with CIFAR-10 and a subset of ImageNet demonstrate that such human-model combinations consistently have higher accuracies than the model or human alone, and that the parameters of the combination method can be estimated effectively with as few as ten labeled datapoints. 

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4. The ZTF Source Classification Project. III. A Catalog of Variable Sources

   https://doi.org/10.3847/1538-4365/ad33c6  

   Healy, Brian_F ; Coughlin, Michael_W ; Mahabal, Ashish_A ; Jegou_du_Laz, Theophile ; Drake, Andrew ; Graham, Matthew_J ; Hillenbrand, Lynne_A ; van_Roestel, Jan ; Szkody, Paula ; Zielske, LeighAnna ; et al ( April 2024 , The Astrophysical Journal Supplement Series)

   The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGBoost (XGB) machine-learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed forSCoPeis open source and adaptable to future time-domain surveys.

    

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5. Active Bayesian Assessment of Black-Box Classifiers

   Ji, D ; Logan IV, R ; Smyth, P ; Steyvers, M ( February 2021 , Proceedings of the AAAI Conference on Artificial Intelligence)

   Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications. In many such situations there is a critical need to both reliably assess the performance of these pre-trained models and to perform this assessment in a label-efficient manner (given that labels may be scarce and costly to collect). In this paper, we introduce an active Bayesian approach for assessment of classifier performance to satisfy the desiderata of both reliability and label-efficiency. We begin by developing inference strategies to quantify uncertainty for common assessment metrics such as accuracy, misclassification cost, and calibration error. We then propose a general framework for active Bayesian assessment using inferred uncertainty to guide efficient selection of instances for labeling, enabling better performance assessment with fewer labels. We demonstrate significant gains from our proposed active Bayesian approach via a series of systematic empirical experiments assessing the performance of modern neural classifiers (e.g., ResNet and BERT) on several standard image and text classification datasets. 

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