ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS (TIIS) 发表有关包含某种形式的机器智能的交互式系统的设计、实现或评估的研究论文。 TIIS 文章来自广泛的研究领域和社区。一篇文章可以采用交互式智能系统的几种互补观点中的任何一种，重点关注：智能技术、用户与系统的交互，或同时关注这两个方面。

ACM Transactions on Interactive Intelligent Systems (TIIS) publishes research papers on the design, implementation, or evaluation of interactive intelligent systems that incorporate some form of machine intelligence. Articles in TIIS come from a wide range of research fields and communities. Each paper may adopt any of several complementary perspectives on interactive intelligent systems, focusing on intelligent technologies, user-system interaction, or both aspects simultaneously.

《ACM Transactions on Interactive Intelligent Systems》 (TIIS) 发表关于交互式智能系统的设计、实现或评估的研究论文，这些系统包含某种形式的机器智能。TIIS 的文章涵盖广泛的研究领域和社区。每篇文章可以从多个互补的视角探讨交互式智能系统，重点关注智能技术、用户与系统的交互，或同时关注这两个方面。

ACM Transactions on Interactive Intelligent Systems

This article presents a conceptual framework for human-robot trust which uses computational representations inspired by game theory to represent a definition of trust, derived from social psychology. This conceptual framework generates several testable hypotheses related to human-robot trust. This article examines these hypotheses and a series of experiments we have conducted which both provide support for and also conflict with our framework for trust. We also discuss the methodological challenges associated with investigating trust. The article concludes with a description of the important areas for future research on the topic of human-robot trust.

Alan R. Wagner

Paul Robinette

Ayanna Howard

Modeling the Human-Robot Trust Phenomenon

建模人机信任现象

It is essential that users understand how algorithmic decisions are made, as we increasingly delegate important decisions to intelligent systems. Prior work has often taken a techno-centric approach, focusing on new computational techniques to support transparency. In contrast, this article employs empirical methods to better understand user reactions to transparent systems to motivate user-centric designs for transparent systems. We assess user reactions to transparency feedback in four studies of an emotional analytics system. In Study 1, users anticipated that a transparent system would perform better but unexpectedly retracted this evaluation after experience with the system. Study 2 offers an explanation for this paradox by showing that the benefits of transparency are context dependent. On the one hand, transparency can help users form a model of the underlying algorithm's operation. On the other hand, positive accuracy perceptions may be undermined when transparency reveals algorithmic errors. Study 3 explored real-time reactions to transparency. Results confirmed Study 2, in showing that users are both more likely to consult transparency information and to experience greater system insights when formulating a model of system operation. Study 4 used qualitative methods to explore real-time user reactions to motivate transparency design principles. Results again suggest that users may benefit from initially simplified feedback that hides potential system errors and assists users in building working heuristics about system operation. We use these findings to motivate new progressive disclosure principles for transparency in intelligent systems and discuss theoretical implications.

Aaron Springer

Steve Whittaker

ACM Trans. Interact. Intell. Syst.

Progressive Disclosure

渐进式披露

Multimodal scene search of conversations is essential for unlocking valuable insights into social dynamics and enhancing our communication. While experts in conversational analysis have their own knowledge and skills to find key scenes, a lack of comprehensive, user-friendly tools that streamline the processing of diverse multimodal queries impedes efficiency and objectivity. To address this gap, we developed ConverSearch , a visual-programming-based tool based on insights for effective interface and implementation design derived from a formative study with experts. The tool allows experts to integrate various machine-learning algorithms to capture human behavioral cues without the need for coding. Our user study, employing the System Usability Scale (SUS) and satisfaction metrics, demonstrated high user preference, reflecting the tool’s ease of use and effectiveness in supporting scene search tasks. Additionally, through a deployment trial within industrial organizations, we confirmed the tool’s objectivity, reusability, and potential to enhance expert workflows. This suggests the advantages of expert-AI collaboration in domains requiring human contextual understanding and demonstrates how customizable, transparent tools yielding reusable artifacts can support expert-driven tasks in complex, multimodal environments.

Riku Arakawa

Kiyosu Maeda

Hiromu Yakura

ConverSearch: Supporting Experts in Human Behavior Analysis of Conversational Videos with a Multimodal Scene Search Tool

ConverSearch：支持专家使用多模态场景搜索工具进行对话视频中的人类行为分析

Large language models (LLMs) match and sometimes exceed human performance in many domains. This study explores the potential of LLMs to augment human judgment in a forecasting task. We evaluate the effect on human forecasters of two LLM assistants: one designed to provide high-quality (‘superforecasting’) advice, and the other designed to be overconfident and base-rate neglecting, thus providing noisy forecasting advice. We compare participants using these assistants to a control group that received a less advanced model that did not provide numerical predictions or engage in explicit discussion of predictions. Participants (N = 991) answered a set of six forecasting questions and had the option to consult their assigned LLM assistant throughout. Our preregistered analyses show that interacting with each of our frontier LLM assistants significantly enhances prediction accuracy by between 24% and 28% compared to the control group. Exploratory analyses showed a pronounced outlier effect in one forecasting item, without which we find that the superforecasting assistant increased accuracy by 41%, compared with 29% for the noisy assistant. We further examine whether LLM forecasting augmentation disproportionately benefits less skilled forecasters, degrades the wisdom-of-the-crowd by reducing prediction diversity, or varies in effectiveness with question difficulty. Our data do not consistently support these hypotheses. Our results suggest that access to a frontier LLM assistant, even a noisy one, can be a helpful decision aid in cognitively demanding tasks compared to a less powerful model that does not provide specific forecasting advice. However, the effects of outliers suggest that further research into the robustness of this pattern is needed.

Philipp Schoenegger

Peter S. Park

Ezra Karger

Sean Trott

Philip E. Tetlock

AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy

人工智能增强预测：大型语言模型助手提升人类预测准确性

Recent work has proposed artificial intelligence (AI) models that can learn to decide whether to make a prediction for a task instance or to delegate it to a human by considering both parties’ capabilities. In simulations with synthetically generated or context-independent human predictions, delegation can help improve the performance of human-AI teams—compared to humans or the AI model completing the task alone. However, so far, it remains unclear how humans perform and how they perceive the task when individual instances of a task are delegated to them by an AI model. In an experimental study with 196 participants, we show that task performance and task satisfaction improve for the instances delegated by the AI model, regardless of whether humans are aware of the delegation. Additionally, we identify humans’ increased levels of self-efficacy as the underlying mechanism for these improvements in performance and satisfaction, and one dimension of cognitive ability as a moderator to this effect. In particular, AI delegation can buffer potential negative effects on task performance and task satisfaction for humans with low visual processing ability. Our findings provide initial evidence that allowing AI models to take over more management responsibilities can be an effective form of human-AI collaboration in workplaces. 1

Monika Westphal

Patrick Hemmer

Michael Vössing

Max Schemmer

Sebastian Vetter

Gerhard Satzger

Towards Understanding AI Delegation: The Role of Self-Efficacy and Visual Processing Ability

走向理解人工智能委托：自我效能与视觉处理能力的作用

Building higher-quality image classification models requires better performance analysis (PA) to help understand their behaviors. We propose ConfusionLens, a dynamic and interactive visualization interface that augments a conventional confusion matrix with focus+context visualization. This interface allows users to seamlessly switch table layouts among three views (overall view, class-level view, and between-class view) while observing all of the instance images in a single screen. We designed and implemented a ConfusionLens prototype that supports hundreds of instances, and then conducted a user study (N=14) to evaluate it compared to the conventional confusion matrix with a split view of instances. Results show that ConfusionLens achieved faster task-completion time in observing instance-level performance and higher accuracy in observing between-class confusion. Moreover, we conducted an expert interview (N=6) to investigate the applicability of our interface to practical PA tasks, and then implemented several extensions of ConfusionLens based on the feedback. Feedback on these extensions from users experienced in image classification (N=5) demonstrated their general usefulness and highlighted their beneficial use in PA tasks.

Kazuyuki Fujita

Keito Uwaseki

Hongyu Bu

Kazuki Takashima

Yoshifumi Kitamura

ConfusionLens: Focus+Context Visualization Interface for Performance Analysis of Multiclass Image Classifiers

ConfusionLens：多类图像分类器性能分析的焦点+上下文可视化界面

Autonomous negotiating agents, which can interact with other agents, aim to solve decision-making problems involving participants with conflicting interests. Designing agents capable of negotiating with human partners requires considering some factors, such as emotional states and arguments. For this purpose, we introduce an extended taxonomy of argument types capturing human speech acts during the negotiation. We propose an argument-based automated negotiating agent that can extract human arguments from a chat-based environment using a hierarchical classifier. Consequently, the proposed agent can understand the received arguments and adapt its strategy accordingly while negotiating with its human counterparts. We initially conducted human-agent negotiation experiments to construct a negotiation corpus to train our classifier. According to the experimental results, it is seen that the proposed hierarchical classifier successfully extracted the arguments from the given text. Moreover, we conducted a second experiment where we tested the performance of the designed negotiation strategy considering the human opponent’s arguments and emotions. Our results showed that the proposed agent beats the human negotiator and gains higher utility than the baseline agent.

Abbreviation	ACM Trans. Interact. Intell. Syst.
Journal Impact	3.64
Quartiles(Global)	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE(Q2)

ISSN	2160-6455, 2160-6463
h-index	45

Publication Information	Publisher: Association for Computing Machinery (ACM)，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2011，Proportion of original research papers: 100.00%，Self Citation Rate:2.80%， Gold OA Rate: 4.40%

ACM Transactions on Interactive Intelligent Systems

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Modeling the Human-Robot Trust Phenomenon

Progressive Disclosure

ConverSearch: Supporting Experts in Human Behavior Analysis of Conversational Videos with a Multimodal Scene Search Tool

AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy

Towards Understanding AI Delegation: The Role of Self-Efficacy and Visual Processing Ability

ConverSearch: Supporting Experts in Human Behavior Analysis of Conversational Videos with a Multimodal Scene Search Tool

AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy

ConfusionLens: Focus+Context Visualization Interface for Performance Analysis of Multiclass Image Classifiers

Towards Understanding AI Delegation: The Role of Self-Efficacy and Visual Processing Ability

Taking into Account Opponent’s Arguments in Human-Agent Negotiations