ACM Transactions on Interactive Intelligent Systems最新文献_第3页

XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning XAutoML:用于理解和验证自动机器学习的可视化分析工具

4区计算机科学

ACM Transactions on Interactive Intelligent Systems Pub Date : 2023-09-28 DOI: 10.1145/3625240

Marc-André Zöller, Waldemar Titov, Thomas Schlegel, Marco F. Huber

{"title":"XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning","authors":"Marc-André Zöller, Waldemar Titov, Thomas Schlegel, Marco F. Huber","doi":"10.1145/3625240","DOIUrl":"https://doi.org/10.1145/3625240","url":null,"abstract":"In the last ten years, various automated machine learning (AutoML) systems have been proposed to build end-to-end machine learning (ML) pipelines with minimal human interaction. Even though such automatically synthesized ML pipelines are able to achieve competitive performance, recent studies have shown that users do not trust models constructed by AutoML due to missing transparency of AutoML systems and missing explanations for the constructed ML pipelines. In a requirements analysis study with 36 domain experts, data scientists, and AutoML researchers from different professions with vastly different expertise in ML, we collect detailed informational needs for AutoML. We propose XAutoML, an interactive visual analytics tool for explaining arbitrary AutoML optimization procedures and ML pipelines constructed by AutoML. XAutoML combines interactive visualizations with established techniques from explainable artificial intelligence (XAI) to make the complete AutoML procedure transparent and explainable. By integrating XAutoML with JupyterLab, experienced users can extend the visual analytics with ad-hoc visualizations based on information extracted from XAutoML. We validate our approach in a user study with the same diverse user group from the requirements analysis. All participants were able to extract useful information from XAutoML, leading to a significantly increased understanding of ML pipelines produced by AutoML and the AutoML optimization itself.","PeriodicalId":48574,"journal":{"name":"ACM Transactions on Interactive Intelligent Systems","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135343679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

2022 TiiS Best Paper Announcement 2022年度最佳论文公告

4区计算机科学

ACM Transactions on Interactive Intelligent Systems Pub Date : 2023-09-11 DOI: 10.1145/3615590

Michelle Zhou, Shlomo Berkovsky

引用次数: 0

Generalisable Dialogue-based Approach for Active Learning of Activities of Daily Living 基于对话的日常生活活动主动学习方法

IF 3.4 4区计算机科学

ACM Transactions on Interactive Intelligent Systems Pub Date : 2023-08-14 DOI: 10.1145/3616017

Ronnie Smith, M. Dragone

{"title":"Generalisable Dialogue-based Approach for Active Learning of Activities of Daily Living","authors":"Ronnie Smith, M. Dragone","doi":"10.1145/3616017","DOIUrl":"https://doi.org/10.1145/3616017","url":null,"abstract":"While Human Activity Recognition systems may benefit from Active Learning by allowing users to self-annotate their Activities of Daily Living (ADLs), many proposed methods for collecting such annotations are for short-term data collection campaigns for specific datasets. We present a reusable dialogue-based approach to user interaction for active learning in activity recognition systems, which utilises semantic similarity measures and a dataset of natural language descriptions of common activities (which we make publicly available). Our approach involves system-initiated dialogue, including follow-up questions to reduce ambiguity in user responses where appropriate. We apply this approach to two active learning scenarios: (i) using an existing CASAS dataset, demonstrating long-term usage; and (ii) using an online activity recognition system, which tackles the issue of online segmentation and labelling. We demonstrate our work in context, in which a natural language interface provides knowledge that can help interpret other multi-modal sensor data. We provide results highlighting the potential of our dialogue- and semantic similarity-based approach. We evaluate our work: (i) quantitatively, as an efficient way to seek users’ input for active learning of ADLs; and (ii) qualitatively, through a user study in which users were asked to compare our approach and an established method. Results show the potential of our approach as a hands-free interface for annotation of sensor data as part of an active learning system. We provide insights into the challenges of active learning for activity recognition under real-world conditions and identify potential ways to address them.","PeriodicalId":48574,"journal":{"name":"ACM Transactions on Interactive Intelligent Systems","volume":"110 1","pages":"1 - 37"},"PeriodicalIF":3.4,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82427498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When Biased Humans Meet Debiased AI: A Case Study in College Major Recommendation 当有偏见的人类遇到无偏见的人工智能:大学专业推荐的案例研究

IF 3.4 4区计算机科学

ACM Transactions on Interactive Intelligent Systems Pub Date : 2023-08-01 DOI: 10.1145/3611313

Clarice Wang, Kathryn Wang, Andrew Bian, Rashidul Islam, Kamrun Keya, James R. Foulds, Shimei Pan

{"title":"When Biased Humans Meet Debiased AI: A Case Study in College Major Recommendation","authors":"Clarice Wang, Kathryn Wang, Andrew Bian, Rashidul Islam, Kamrun Keya, James R. Foulds, Shimei Pan","doi":"10.1145/3611313","DOIUrl":"https://doi.org/10.1145/3611313","url":null,"abstract":"Currently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI algorithms, e.g., along lines of gender, age, and race. While most research in this domain focuses on developing fair AI algorithms, in this work, we examine the challenges which arise when humans and fair AI interact. Our results show that due to an apparent conflict between human preferences and fairness, a fair AI algorithm on its own may be insufficient to achieve its intended results in the real world. Using college major recommendation as a case study, we build a fair AI recommender by employing gender debiasing machine learning techniques. Our offline evaluation showed that the debiased recommender makes fairer career recommendations without sacrificing its accuracy in prediction. Nevertheless, an online user study of more than 200 college students revealed that participants on average prefer the original biased system over the debiased system. Specifically, we found that perceived gender disparity is a determining factor for the acceptance of a recommendation. In other words, we cannot fully address the gender bias issue in AI recommendations without addressing the gender bias in humans. We conducted a follow-up survey to gain additional insights into the effectiveness of various design options that can help participants to overcome their own biases. Our results suggest that making fair AI explainable is crucial for increasing its adoption in the real world.","PeriodicalId":48574,"journal":{"name":"ACM Transactions on Interactive Intelligent Systems","volume":"93 1","pages":"1 - 28"},"PeriodicalIF":3.4,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73796948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrity Based Explanations for Fostering Appropriate Trust in AI Agents 基于诚信的人工智能主体适当信任培养解释

IF 3.4 4区计算机科学

ACM Transactions on Interactive Intelligent Systems Pub Date : 2023-07-24 DOI: https://dl.acm.org/doi/10.1145/3610578

Siddharth Mehrotra, Carolina Centeio Jorge, Catholijn M. Jonker, Myrthe L. Tielman

{"title":"Integrity Based Explanations for Fostering Appropriate Trust in AI Agents","authors":"Siddharth Mehrotra, Carolina Centeio Jorge, Catholijn M. Jonker, Myrthe L. Tielman","doi":"https://dl.acm.org/doi/10.1145/3610578","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3610578","url":null,"abstract":"Appropriate trust is an important component of the interaction between people and AI systems, in that ‘inappropriate’ trust can cause disuse, misuse or abuse of AI. To foster appropriate trust in AI, we need to understand how AI systems can elicit appropriate levels of trust from their users. Out of the aspects that influence trust, this paper focuses on the effect of showing integrity. In particular, this paper presents a study of how different integrity-based explanations made by an AI agent affect the appropriateness of trust of a human in that agent. To explore this, (1) we provide a formal definition to measure appropriate trust, (2) present a between-subject user study with 160 participants who collaborated with an AI agent in such a task. In the study, the AI agent assisted its human partner in estimating calories on a food plate by expressing its integrity through explanations focusing on either honesty, transparency or fairness. Our results show that (a) an agent who displays its integrity by being explicit about potential biases in data or algorithms achieved appropriate trust more often compared to being honest about capability or transparent about the decision-making process, and (b) subjective trust builds up and recovers better with honesty-like integrity explanations. Our results contribute to the design of agent-based AI systems that guide humans to appropriately trust them, a formal method to measure appropriate trust, and how to support humans in calibrating their trust in AI.","PeriodicalId":48574,"journal":{"name":"ACM Transactions on Interactive Intelligent Systems","volume":"55 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrity Based Explanations for Fostering Appropriate Trust in AI Agents 基于诚信的人工智能主体适当信任培养解释

IF 3.4 4区计算机科学

ACM Transactions on Interactive Intelligent Systems Pub Date : 2023-07-24 DOI: 10.1145/3610578

Siddharth Mehrotra, Carolina Centeio Jorge, C. Jonker, M. Tielman

{"title":"Integrity Based Explanations for Fostering Appropriate Trust in AI Agents","authors":"Siddharth Mehrotra, Carolina Centeio Jorge, C. Jonker, M. Tielman","doi":"10.1145/3610578","DOIUrl":"https://doi.org/10.1145/3610578","url":null,"abstract":"Appropriate trust is an important component of the interaction between people and AI systems, in that ‘inappropriate’ trust can cause disuse, misuse or abuse of AI. To foster appropriate trust in AI, we need to understand how AI systems can elicit appropriate levels of trust from their users. Out of the aspects that influence trust, this paper focuses on the effect of showing integrity. In particular, this paper presents a study of how different integrity-based explanations made by an AI agent affect the appropriateness of trust of a human in that agent. To explore this, (1) we provide a formal definition to measure appropriate trust, (2) present a between-subject user study with 160 participants who collaborated with an AI agent in such a task. In the study, the AI agent assisted its human partner in estimating calories on a food plate by expressing its integrity through explanations focusing on either honesty, transparency or fairness. Our results show that (a) an agent who displays its integrity by being explicit about potential biases in data or algorithms achieved appropriate trust more often compared to being honest about capability or transparent about the decision-making process, and (b) subjective trust builds up and recovers better with honesty-like integrity explanations. Our results contribute to the design of agent-based AI systems that guide humans to appropriately trust them, a formal method to measure appropriate trust, and how to support humans in calibrating their trust in AI.","PeriodicalId":48574,"journal":{"name":"ACM Transactions on Interactive Intelligent Systems","volume":"65 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76625860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Does this Explanation Help? Designing Local Model-agnostic Explanation Representations and an Experimental Evaluation using Eye-tracking Technology 这个解释有帮助吗?基于眼动追踪技术的局部模型不可知解释表征设计与实验评价

IF 3.4 4区计算机科学

ACM Transactions on Interactive Intelligent Systems Pub Date : 2023-07-13 DOI: 10.1145/3607145

Miguel Angel Meza Martínez, Mario Nadj, Moritz Langner, Peyman Toreini, A. Maedche

{"title":"Does this Explanation Help? Designing Local Model-agnostic Explanation Representations and an Experimental Evaluation using Eye-tracking Technology","authors":"Miguel Angel Meza Martínez, Mario Nadj, Moritz Langner, Peyman Toreini, A. Maedche","doi":"10.1145/3607145","DOIUrl":"https://doi.org/10.1145/3607145","url":null,"abstract":"In Explainable Artificial Intelligence (XAI) research, various local model-agnostic methods have been proposed to explain individual predictions to users in order to increase the transparency of the underlying Artificial Intelligence (AI) systems. However, the user perspective has received less attention in XAI research, leading to a (1) lack of involvement of users in the design process of local model-agnostic explanations representations and (2) a limited understanding of how users visually attend them. Against this backdrop, we refined representations of local explanations from four well-established model-agnostic XAI methods in an iterative design process with users. Moreover, we evaluated the refined explanation representations in a laboratory experiment using eye-tracking technology as well as self-reports and interviews. Our results show that users do not necessarily prefer simple explanations and that their individual characteristics, such as gender and previous experience with AI systems, strongly influence their preferences. In addition, users find that some explanations are only useful in certain scenarios making the selection of an appropriate explanation highly dependent on context. With our work, we contribute to ongoing research to improve transparency in AI.","PeriodicalId":48574,"journal":{"name":"ACM Transactions on Interactive Intelligent Systems","volume":"9 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73060419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Does this Explanation Help? Designing Local Model-agnostic Explanation Representations and an Experimental Evaluation using Eye-tracking Technology 这个解释有帮助吗?基于眼动追踪技术的局部模型不可知解释表征设计与实验评价

IF 3.4 4区计算机科学

ACM Transactions on Interactive Intelligent Systems Pub Date : 2023-07-13 DOI: https://dl.acm.org/doi/10.1145/3607145

Miguel Angel Meza Martínez, Mario Nadj, Moritz Langner, Peyman Toreini, Alexander Maedche

{"title":"Does this Explanation Help? Designing Local Model-agnostic Explanation Representations and an Experimental Evaluation using Eye-tracking Technology","authors":"Miguel Angel Meza Martínez, Mario Nadj, Moritz Langner, Peyman Toreini, Alexander Maedche","doi":"https://dl.acm.org/doi/10.1145/3607145","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3607145","url":null,"abstract":"In Explainable Artificial Intelligence (XAI) research, various local model-agnostic methods have been proposed to explain individual predictions to users in order to increase the transparency of the underlying Artificial Intelligence (AI) systems. However, the user perspective has received less attention in XAI research, leading to a (1) lack of involvement of users in the design process of local model-agnostic explanations representations and (2) a limited understanding of how users visually attend them. Against this backdrop, we refined representations of local explanations from four well-established model-agnostic XAI methods in an iterative design process with users. Moreover, we evaluated the refined explanation representations in a laboratory experiment using eye-tracking technology as well as self-reports and interviews. Our results show that users do not necessarily prefer simple explanations and that their individual characteristics, such as gender and previous experience with AI systems, strongly influence their preferences. In addition, users find that some explanations are only useful in certain scenarios making the selection of an appropriate explanation highly dependent on context. With our work, we contribute to ongoing research to improve transparency in AI.","PeriodicalId":48574,"journal":{"name":"ACM Transactions on Interactive Intelligent Systems","volume":"57 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VERB: Visualizing and Interpreting Bias Mitigation Techniques Geometrically for Word Representations 动词:可视化和解释的偏见缓解技术的几何字表示

IF 3.4 4区计算机科学

ACM Transactions on Interactive Intelligent Systems Pub Date : 2023-06-22 DOI: https://dl.acm.org/doi/10.1145/3604433

Archit Rathore, Sunipa Dev, Jeff M. Phillips, Vivek Srikumar, Yan Zheng, Chin-Chia Michael Yeh, Junpeng Wang, Wei Zhang, Bei Wang

{"title":"VERB: Visualizing and Interpreting Bias Mitigation Techniques Geometrically for Word Representations","authors":"Archit Rathore, Sunipa Dev, Jeff M. Phillips, Vivek Srikumar, Yan Zheng, Chin-Chia Michael Yeh, Junpeng Wang, Wei Zhang, Bei Wang","doi":"https://dl.acm.org/doi/10.1145/3604433","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3604433","url":null,"abstract":"Word vector embeddings have been shown to contain and amplify biases in the data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and attenuate these biases in word representations. In this paper, we utilize interactive visualization to increase the interpretability and accessibility of a collection of state-of-the-art debiasing techniques. To aid this, we present the Visualization of Embedding Representations for deBiasing (“VERB”) system, an open-source web-based visualization tool that helps users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties. In particular, VERB offers easy-to-follow examples that explore the effects of these debiasing techniques on the geometry of high-dimensional word vectors. To help understand how various debiasing techniques change the underlying geometry, VERB decomposes each technique into interpretable sequences of primitive transformations and highlights their effect on the word vectors using dimensionality reduction and interactive visual exploration. VERB is designed to target natural language processing (NLP) practitioners who are designing decision-making systems on top of word embeddings, and also researchers working with the fairness and ethics of machine learning systems in NLP. It can also serve as a visual medium for education, which helps an NLP novice understand and mitigate biases in word embeddings.","PeriodicalId":48574,"journal":{"name":"ACM Transactions on Interactive Intelligent Systems","volume":"54 4","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VERB: Visualizing and Interpreting Bias Mitigation Techniques Geometrically for Word Representations 动词:可视化和解释的偏见缓解技术的几何字表示

IF 3.4 4区计算机科学

ACM Transactions on Interactive Intelligent Systems Pub Date : 2023-06-22 DOI: 10.1145/3604433

Archit Rathore, Yan Zheng, Chin-Chia Michael Yeh

{"title":"VERB: Visualizing and Interpreting Bias Mitigation Techniques Geometrically for Word Representations","authors":"Archit Rathore, Yan Zheng, Chin-Chia Michael Yeh","doi":"10.1145/3604433","DOIUrl":"https://doi.org/10.1145/3604433","url":null,"abstract":"Word vector embeddings have been shown to contain and amplify biases in the data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and attenuate these biases in word representations. In this paper, we utilize interactive visualization to increase the interpretability and accessibility of a collection of state-of-the-art debiasing techniques. To aid this, we present the Visualization of Embedding Representations for deBiasing (“VERB”) system, an open-source web-based visualization tool that helps users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties. In particular, VERB offers easy-to-follow examples that explore the effects of these debiasing techniques on the geometry of high-dimensional word vectors. To help understand how various debiasing techniques change the underlying geometry, VERB decomposes each technique into interpretable sequences of primitive transformations and highlights their effect on the word vectors using dimensionality reduction and interactive visual exploration. VERB is designed to target natural language processing (NLP) practitioners who are designing decision-making systems on top of word embeddings, and also researchers working with the fairness and ethics of machine learning systems in NLP. It can also serve as a visual medium for education, which helps an NLP novice understand and mitigate biases in word embeddings.","PeriodicalId":48574,"journal":{"name":"ACM Transactions on Interactive Intelligent Systems","volume":"39 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90094350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1