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1. Consistent Joint Decision-Making with Heterogeneous Learning Models

   Faghihi, Hossein; Kordjamshidi, Parisa (March 2024, Association for Computational Linguistics)

   This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming(ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions’ prior probability, confidence (uncertainty), and the models’ expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets. 

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2. Design Decision Framework for AI Explanations

   Oghenemaro Anuyah and Ronald Metoyer (October 2021, Mensh and Computer Workshop on Human-Centered Artificial Intelligence, 2021)

   Explanations can help users of Artificial Intelligent (AI) systems gain a better understanding of the reasoning behind the model’s decision, facilitate their trust in AI, and assist them in making informed decisions. Due to its numerous benefits in improving how users interact and collaborate with AI, this has stirred the AI/ML community towards developing understandable or interpretable models to a larger degree, while design researchers continue to study and research ways to present explanations of these models’ decisions in a coherent form. However, there is still the lack of intentional design effort from the HCI community around these explanation system designs. In this paper, we contribute a framework to support the design and validation of explainable AI systems; one that requires carefully thinking through design decisions at several important decision points. This framework captures key aspects of explanations ranging from target users, to the data, to the AI models in use. We also discuss how we applied our framework to design an explanation interface for trace link prediction of software artifacts. 

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3. xGAIL: Explainable Generative Adversarial Imitation Learning for Explainable Human Decision Analysis

   https://doi.org/10.1145/3394486.3403186  

   Pan, Menghai; Huang, Weixiao; Li, Yanhua; Zhou, Xun; Luo, Jun (August 2020, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining)

   To make daily decisions, human agents devise their own "strategies" governing their mobility dynamics (e.g., taxi drivers have preferred working regions and times, and urban commuters have preferred routes and transit modes). Recent research such as generative adversarial imitation learning (GAIL) demonstrates successes in learning human decision-making strategies from their behavior data using deep neural networks (DNNs), which can accurately mimic how humans behave in various scenarios, e.g., playing video games, etc. However, such DNN-based models are "black box" models in nature, making it hard to explain what knowledge the models have learned from human, and how the models make such decisions, which was not addressed in the literature of imitation learning. This paper addresses this research gap by proposing xGAIL, the first explainable generative adversarial imitation learning framework. The proposed xGAIL framework consists of two novel components, including Spatial Activation Maximization (SpatialAM) and Spatial Randomized Input Sampling Explanation (SpatialRISE), to extract both global and local knowledge from a well-trained GAIL model that explains how a human agent makes decisions. Especially, we take taxi drivers' passenger-seeking strategy as an example to validate the effectiveness of the proposed xGAIL framework. Our analysis on a large-scale real-world taxi trajectory data shows promising results from two aspects: i) global explainable knowledge of what nearby traffic condition impels a taxi driver to choose a particular direction to find the next passenger, and ii) local explainable knowledge of what key (sometimes hidden) factors a taxi driver considers when making a particular decision. 

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4. Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications

   https://doi.org/10.34133/2021/9846470  

   Riera, Luis G.; Carroll, Matthew E.; Zhang, Zhisheng; Shook, Johnathon M.; Ghosal, Sambuddha; Gao, Tianshuang; Singh, Arti; Bhattacharya, Sourabh; Ganapathysubramanian, Baskar; Singh, Asheesh K.; et al (June 2021, Plant Phenomics)

   Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean ( Glycine max L. (Merr.)) pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multiview image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars. 

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5. JupyterLab in Retrograde: Contextual Notifications That Highlight Fairness and Bias Issues for Data Scientists

   Harrison, Galen; Bryson, Kevin; Bamba, Ahmad Emmanuel; Dovichi, Luca; Binion, Aleksander Herrmann; Borem, Arthur; Ur, Blase (May 2024, Proceedings of the CHI Conference on Human Factors in Computing Systems)

   Current algorithmic fairness tools focus on auditing completed models, neglecting the potential downstream impacts of iterative decisions about cleaning data and training machine learning models. In response, we developed Retrograde, a JupyterLab environment extension for Python that generates real-time, contextual notifications for data scientists about decisions they are making regarding protected classes, proxy variables, missing data, and demographic differences in model performance. Our novel framework uses automated code analysis to trace data provenance in JupyterLab, enabling these notifications. In a between-subjects online experiment, 51 data scientists constructed loan-decision models with Retrograde providing notifications continuously throughout the process, only at the end, or never. Retrograde’s notifications successfully nudged participants to account for missing data, avoid using protected classes as predictors, minimize demographic differences in model performance, and exhibit healthy skepticism about their models. 

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