The Influence of Explainable Artificial Intelligence: Nudging Behaviour or Boosting Capability?
University College London（伦敦大学学院）
Matija Franklin
备注：
Accepted at the ICML-22 Workshop on Human-Machine Collaboration and Teaming held at the Thirty-ninth International Conference on Machine Learning (ICML-22), 7 pages
This article aims to provide a theoretical account and corresponding paradigm for analysing how explainable artificial intelligence (XAI) influences people's behaviour and cognition. It uses insights from research on behaviour change. Two notable frameworks for thinking about behaviour change techniques are nudges - aimed at influencing behaviour - and boosts - aimed at fostering capability. It proposes that local and concept-based explanations are more adjacent to nudges, while global and counterfactual explanations are more adjacent to boosts. It outlines a method for measuring XAI influence and argues for the benefits of understanding it for optimal, safe and ethical human-AI collaboration.
Unveiling Hidden Factors: Explainable AI for Feature Boosting in Speech Emotion Recognition
University of Québec (TÉLUQ)（魁北克大学（TÉLUQ））；Concordia University（康考迪亚大学）；Concordia Institute for Information Systems Engineering（康考迪亚信息系统工程学院）；University of Québec at Montréal（蒙特利尔魁北克大学）；Centre for Research and Intervention on Suicide, Ethical Issues and End-of-Life Practices（自杀、伦理问题与临终实践研究与干预中心）
Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents
Computer Science, University of Toronto（计算机科学，多伦多大学）；University of Toronto（多伦多大学）；King Abdullah University of Science and Technology（国王 Abdullah 科学与技术大学）
本文提出 CopilotLens 交互式框架，通过两级界面重建 AI 编码智能体的思考过程，将代码补全从黑盒建议转变为透明可解释的交互，以帮助开发者加深理解并校准信任。
An Explainable Machine Learning Approach for Age and Gender Estimation in Living Individuals Using Dental Biometrics
University of Essex（埃塞克斯大学）；Tel Aviv University（特拉维夫大学）；Oral Medicine and Oral Surgery（口腔医学与口腔外科）；Universitas Airlangga（艾尔朗加大学）；Boston University（波士顿大学）；King George’s Medical University（乔治国王医科大学）
This paper applies eXplainable Artificial Intelligence (XAI) methods to investigate the socioeconomic disparities in COVID patient mortality. An Extreme Gradient Boosting (XGBoost) prediction model is built based on a de-identified Austin area hospital dataset to predict the mortality of COVID-19 patients. We apply two XAI methods, Shapley Additive exPlanations (SHAP) and Locally Interpretable Model Agnostic Explanations (LIME), to compare the global and local interpretation of feature importance. This paper demonstrates the advantages of using XAI which shows the feature importance and decisi…
Kalasalingam Academy for Research and Education（卡萨斯林甘研究与教育学院）；GE Corporate Research Center（通用电气企业研究中心）；SINTEF；Norwegian University of Science and Technology（挪威科技大学）；SUNY Polytechnic Institute（纽约州立大学理工学院）
This paper presents an eXplainable Fault Detection and Diagnosis System (XFDDS) for incipient faults in PV panels. The XFDDS is a hybrid approach that combines the model-based and data-driven framework. Model-based FDD for PV panels lacks high fidelity models at low irradiance conditions for detecting incipient faults. To overcome this, a novel irradiance based three diode model (IB3DM) is proposed. It is a nine parameter model that provides higher accuracy even at low irradiance conditions, an important aspect for detecting incipient faults from noise. To exploit PV data, extreme gradient boo…
The University of Western Australia（西澳大利亚大学）；Armadale Mental Health Service（阿玛代尔心理健康服务中心）；Bethesda Clinic（贝塞斯达诊所）；Co-HIVE Mental Health（Co-HIVE心理健康中心）；Harry Perkins Institute of Medical Research（哈里·珀金斯医学研究所）；Fiona Stanley Hospital（菲奥娜·斯坦利医院）；University of Western Australia（西澳大利亚大学）
This study investigates the effectiveness of Explainable Artificial Intelligence (XAI) techniques in predicting suicide risks and identifying the dominant causes for such behaviours. Data augmentation techniques and ML models are utilized to predict the associated risk. Furthermore, SHapley Additive exPlanations (SHAP) and correlation analysis are used to rank the importance of variables in predictions. Experimental results indicate that Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models achieve the best results while DT has the best performance with an accur…
X-SHIELD: Regularization for eXplainable Artificial Intelligence
Department of Computer Science and Artificial Intelligence（计算机科学与人工智能系）；Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（数据科学与计算智能安达卢西亚研究机构（DaSCI））；University of Granada（格拉纳达大学）
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods
Computer Science Department, College of Computers and Information Technology, Taif University（计算机科学系，计算机与信息科技学院，泰夫大学）；Department of Computer Science, Taif University（计算机科学系，泰夫大学）
Department of Artificial Intelligence, University of Malta（马耳他大学人工智能系）
With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulati…
Explainable Artificial Intelligence Model for Evaluating Shear Strength Parameters of Municipal Solid Waste Across Diverse Compositional Profiles
The Joint Graduate School of Energy and Environment, King Mongkut's University of Technology Thonburi（泰国国王科技大学吞武里校区能源与环境联合研究生院）；AI Research Group, Department of Civil Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi（泰国国王科技大学吞武里校区工程学院土木工程系人工智能研究组）；Center of Excellence on Energy Technology and Environment (CEE), PERDO, Ministry of Higher Education, Science, Research and Innovation (MHESI)（高等教育、科学、研究与创新部PERDO能源技术与环境卓越中心）；Department of Environmental Engineering, Faculty of Engineering, Kasetsart University（农业大学工程学院环境工程系）
Exploring Machine Learning, Deep Learning, and Explainable AI Methods for Seasonal Precipitation Prediction in South America
Laboratório de Inteligência ARtificial para Aplicações AeroEspaciais e Ambientais (LIAREA)（人工智能应用航空与环境实验室）；Coordenação de Pesquisa Aplicada e Desenvolvimento Tecnológico (COPDT)（应用研究与技术开发协调处）；Programa de Pós-Graduação em Computação Aplicada (PGCAP)（应用计算硕士项目）；Instituto Nacional de Pesquisas Espaciais (INPE)（国家空间科学研究院）；Instituto de Estudos Avançados (IEAv)（高级研究学院）
The black-box nature of machine learning models limits their use in case-critical applications, raising faithful and ethical concerns that lead to trust crises. One possible way to mitigate this issue is to understand how a (mispredicted) decision is carved out from the decision boundary. This paper presents a human-in-the-loop approach to explain machine learning models using verbatim neighborhood manifestation. Contrary to most of the current eXplainable Artificial Intelligence (XAI) systems, which provide hit-or-miss approximate explanations, our approach generates the local decision bounda…
When people buy products online, they primarily base their decisions on the recommendations of others given in online reviews. The current work analyzed these online reviews by sentiment analysis and used the extracted sentiments as features to predict the product ratings by several machine learning algorithms. These predictions were disentangled by various meth-ods of explainable AI (XAI) to understand whether the model showed any bias during prediction. Study 1 benchmarked these algorithms (knn, support vector machines, random forests, gradient boosting machines, XGBoost) and identified rand…
