移情如何影响创造性问题解决？我们引入了基于上下文特定的情感模仿和透视图的计算同理干预，以良好的北极熊的形式出现了虚拟药物的观点。在与1,006名参与者进行的在线实验中，随机分配到情绪启发干预（具有控制的启发条件和愤怒启发条件）和计算同理心干预（具有控制虚拟代理和移情虚拟药物）中，我们研究了愤怒和移情的影响参与者在基于Wordle的单词游戏中的表现。我们发现，分配给愤怒启发条件的参与者在多个绩效指标上的表现要比分配给控制条件的参与者明显差。但是，我们发现同理心虚拟代理抵消了愤怒条件引起的绩效下降，以至于分配给移情虚拟代理和愤怒条件的参与者与在控制启发条件下的参与者的表现没有不同，并且比分配给参与者的参与者明显更好控制虚拟药物和愤怒启发条件。尽管移情减少了愤怒的负面影响，但我们没有发现移情虚拟药物会影响被分配到控制启发条件的参与者的表现的证据。通过引入计算同理心干预的框架并进行两乘两个阶乘设计随机实验，我们提供了严格的经验证据，即计算同理心可以抵消愤怒对创造性问题解决的负面影响。

超现实视觉效果的技术的最新进展引起了人们的关注，即政治演讲的深层视频很快将与真实的视频录制无法视觉区分。通信研究中的传统观念预测，当故事的同一版本被视为视频而不是文字时，人们会更频繁地跌倒假新闻。在这里，我们评估了41,822名参与者在一个实验中如何将真实的政治演讲与捏造区分开来，在该实验中，演讲被随机显示为文本，音频和视频的排列。我们发现获得音频和视觉沟通方式的访问提高了参与者的准确性。在这里，人类的判断更多地依赖于话语，视听线索比所说的语音内容。但是，我们发现反思性推理调节了参与者考虑视觉信息的程度：认知反射测试的表现较低与对所说内容的过度依赖有关。

本文详细概述了将连续学习（CL）应用于单课的人类机器人互动（HRI）会议（AVG。31 +-10分钟）的案例研究，其中机器人的心理健康教练是积极的（n = 20）参与者的心理学（PP）练习。我们介绍了互动会议后与参与者进行的简短半结构访谈记录的数据的主题分析（TA）的结果，以及对统计结果的分析，证明了参与者的个性如何影响他们如何看待机器人的方式及其互动。

情绪可以提供自然的交流方式，以补充许多领域中社交机器人（例如文本和语音）现有的多模式能力。我们与112、223和151名参与者进行了三项在线研究，以调查使用情绪作为搜救（SAR）机器人的交流方式的好处。在第一个实验中，我们研究了通过机器人的情绪传达与SAR情况有关的信息的可行性，从而导致了从SAR情况到情绪的映射。第二项研究使用控制控制理论是推导此类映射的替代方法。此方法更灵活，例如允许对不同的情绪集和不同机器人进行调整。在第三个实验中，我们使用LED作为表达通道为外观受限的室外现场研究机器人创建了情感表达。在各种模拟的SAR情况下，使用这些情感表达式，我们评估了这些表达式对参与者（采用救援人员的作用）的影响。我们的结果和提议的方法提供了（a）有关情感如何帮助在SAR背景下传达信息的见解，以及（b）在（模拟）SAR通信环境中添加情绪为传播方式的有效性的证据。

情绪分析中最突出的任务是为文本分配情绪，并了解情绪如何在语言中表现出来。自然语言处理的一个重要观察结果是，即使没有明确提及情感名称，也可以通过单独参考事件来隐式传达情绪。在心理学中，被称为评估理论的情感理论类别旨在解释事件与情感之间的联系。评估可以被形式化为变量，通过他们认为相关的事件的人们的认知评估来衡量认知评估。其中包括评估事件是否是新颖的，如果该人认为自己负责，是否与自己的目标以及许多其他人保持一致。这样的评估解释了哪些情绪是基于事件开发的，例如，新颖的情况会引起惊喜或不确定后果的人可能引起恐惧。我们在文本中分析了评估理论对情绪分析的适用性，目的是理解注释者是否可以可靠地重建评估概念，如果可以通过文本分类器预测，以及评估概念是否有助于识别情感类别。为了实现这一目标，我们通过要求人们发短信描述触发特定情绪并披露其评估的事件来编译语料库。然后，我们要求读者重建文本中的情感和评估。这种设置使我们能够衡量是否可以纯粹从文本中恢复情绪和评估，并为判断模型的绩效指标提供人体基准。我们将文本分类方法与人类注释者的比较表明，两者都可以可靠地检测出具有相似性能的情绪和评估。我们进一步表明，评估概念改善了文本中情绪的分类。

Incivility remains a major challenge for online discussion platforms, to such an extent that even conversations between well-intentioned users can often derail into uncivil behavior. Traditionally, platforms have relied on moderators to -- with or without algorithmic assistance -- take corrective actions such as removing comments or banning users. In this work we propose a complementary paradigm that directly empowers users by proactively enhancing their awareness about existing tension in the conversation they are engaging in and actively guides them as they are drafting their replies to avoid further escalation. As a proof of concept for this paradigm, we design an algorithmic tool that provides such proactive information directly to users, and conduct a user study in a popular discussion platform. Through a mixed methods approach combining surveys with a randomized controlled experiment, we uncover qualitative and quantitative insights regarding how the participants utilize and react to this information. Most participants report finding this proactive paradigm valuable, noting that it helps them to identify tension that they may have otherwise missed and prompts them to further reflect on their own replies and to revise them. These effects are corroborated by a comparison of how the participants draft their reply when our tool warns them that their conversation is at risk of derailing into uncivil behavior versus in a control condition where the tool is disabled. These preliminary findings highlight the potential of this user-centered paradigm and point to concrete directions for future implementations.

Deepfakes are computationally-created entities that falsely represent reality. They can take image, video, and audio modalities, and pose a threat to many areas of systems and societies, comprising a topic of interest to various aspects of cybersecurity and cybersafety. In 2020 a workshop consulting AI experts from academia, policing, government, the private sector, and state security agencies ranked deepfakes as the most serious AI threat. These experts noted that since fake material can propagate through many uncontrolled routes, changes in citizen behaviour may be the only effective defence. This study aims to assess human ability to identify image deepfakes of human faces (StyleGAN2:FFHQ) from nondeepfake images (FFHQ), and to assess the effectiveness of simple interventions intended to improve detection accuracy. Using an online survey, 280 participants were randomly allocated to one of four groups: a control group, and 3 assistance interventions. Each participant was shown a sequence of 20 images randomly selected from a pool of 50 deepfake and 50 real images of human faces. Participants were asked if each image was AI-generated or not, to report their confidence, and to describe the reasoning behind each response. Overall detection accuracy was only just above chance and none of the interventions significantly improved this. Participants' confidence in their answers was high and unrelated to accuracy. Assessing the results on a per-image basis reveals participants consistently found certain images harder to label correctly, but reported similarly high confidence regardless of the image. Thus, although participant accuracy was 62% overall, this accuracy across images ranged quite evenly between 85% and 30%, with an accuracy of below 50% for one in every five images. We interpret the findings as suggesting that there is a need for an urgent call to action to address this threat.

工人花费大量时间学习如何做出正确的决定。但是，评估给定决策的功效可能很复杂 - 例如，决策结果通常是长期的，并且以复杂的方式与原始决策有关。令人惊讶的是，即使学习良好的决策策略很困难，它们通常可以以简单明了的形式表达。为了关注顺序决策，我们设计了一种新颖的机器学习算法，该算法能够从跟踪数据中提取“最佳实践”，并以可解释的“提示”的形式向人类传达其见解。我们的算法选择了最能弥合人类工人所采取的行动与最佳政策所采取的行动之间差距的提示，以说明行动对实现更高绩效的影响的方式。我们通过一系列参与者管理虚拟厨房的一系列随机对照实验来评估我们的方法。我们的实验表明，我们算法产生的提示可以显着改善相对于直观基准的人类性能。此外，我们讨论了许多经验见解，这些见解可以帮助告知针对人类界面的算法设计。例如，我们发现参与者不仅盲目地遵循我们的技巧的证据。相反，他们将他们与自己的经验结合在一起，以发现改善性能的其他策略。

Intelligent agents have great potential as facilitators of group conversation among older adults. However, little is known about how to design agents for this purpose and user group, especially in terms of agent embodiment. To this end, we conducted a mixed methods study of older adults' reactions to voice and body in a group conversation facilitation agent. Two agent forms with the same underlying artificial intelligence (AI) and voice system were compared: a humanoid robot and a voice assistant. One preliminary study (total n=24) and one experimental study comparing voice and body morphologies (n=36) were conducted with older adults and an experienced human facilitator. Findings revealed that the artificiality of the agent, regardless of its form, was beneficial for the socially uncomfortable task of conversation facilitation. Even so, talkative personality types had a poorer experience with the "bodied" robot version. Design implications and supplementary reactions, especially to agent voice, are also discussed.

最近十年表明，人们对机器人作为福祉教练的兴趣越来越大。但是，尚未提出针对机器人设计作为促进心理健康的教练的凝聚力和全面的准则。本文详细介绍了基于基于扎根理论方法的定性荟萃分析的设计和道德建议，该方法是通过三项以用户为中心的涉及机器人福祉教练的三个不同的以用户为中心进行的，即：（1）与参与性设计研究一起进行的。 11名参与者由两位潜在用户组成，他们与人类教练一起参加了简短的专注于解决方案的实践研究，以及不同学科的教练，（2）半结构化的个人访谈数据，这些数据来自20名参加积极心理学干预研究的参与者借助机器人福祉教练胡椒，（3）与3名积极心理学研究的参与者以及2名相关的福祉教练进行了一项参与式设计研究。在进行主题分析和定性荟萃分析之后，我们将收集到收敛性和不同主题的数据整理在一起，并从这些结果中提炼了一套设计准则和道德考虑。我们的发现可以在设计机器人心理福祉教练时考虑到关键方面的关键方面。

事实证明，在学习环境中，社会智能代理（SIA）的部署在不同的应用领域具有多个优势。社会代理创作工具使场景设计师能够创造出对SIAS行为的高度控制的量身定制体验，但是，另一方面，这是有代价的，因为该方案及其创作的复杂性可能变得霸道。在本文中，我们介绍了可解释的社会代理创作工具的概念，目的是分析社会代理的创作工具是否可以理解和解释。为此，我们检查了创作工具Fatima-Toolkit是否可以理解，并且从作者的角度来看，其创作步骤可以解释。我们进行了两项用户研究，以定量评估Fatima-Toolkit的解释性，可理解性和透明度，从场景设计师的角度来看。关键发现之一是，法蒂玛 - 库尔基特（Fatima-Toolkit）的概念模型通常是可以理解的，但是基于情感的概念并不那么容易理解和使用。尽管关于Fatima-Toolkit的解释性有一些积极的方面，但仍需要取得进展，以实现完全可以解释的社会代理商创作工具。我们提供一组关键概念和可能的解决方案，可以指导开发人员构建此类工具。

Interaction and cooperation with humans are overarching aspirations of artificial intelligence (AI) research. Recent studies demonstrate that AI agents trained with deep reinforcement learning are capable of collaborating with humans. These studies primarily evaluate human compatibility through "objective" metrics such as task performance, obscuring potential variation in the levels of trust and subjective preference that different agents garner. To better understand the factors shaping subjective preferences in human-agent cooperation, we train deep reinforcement learning agents in Coins, a two-player social dilemma. We recruit participants for a human-agent cooperation study and measure their impressions of the agents they encounter. Participants' perceptions of warmth and competence predict their stated preferences for different agents, above and beyond objective performance metrics. Drawing inspiration from social science and biology research, we subsequently implement a new "partner choice" framework to elicit revealed preferences: after playing an episode with an agent, participants are asked whether they would like to play the next round with the same agent or to play alone. As with stated preferences, social perception better predicts participants' revealed preferences than does objective performance. Given these results, we recommend human-agent interaction researchers routinely incorporate the measurement of social perception and subjective preferences into their studies.

我们回顾了有关模型的文献，这些文献试图解释具有金钱回报的正常形式游戏所描述的社交互动中的人类行为。我们首先涵盖社会和道德偏好。然后，我们专注于日益增长的研究，表明人们对描述行动的语言做出反应，尤其是在激活道德问题时。最后，我们认为行为经济学正处于向基于语言的偏好转变的范式中，这将需要探索新的模型和实验设置。

本文提出了一个论点，说明了为什么我们没有在解释性，可解释性和透明度研究中充分衡量信任。大多数研究要求参与者完成信任量表，以评估他们对已解释/解释的模型的信任。如果信托增加，我们认为这是积极的。但是，这有两个问题。首先，我们通常无法知道参与者是否应该信任该模型。如果模型质量较差，信任肯定应降低。其次，这些量表衡量了感知到的信任，而不是证明信任。本文展示了三种在衡量感知和证明信任方面做得很好的方法。它旨在讨论此主题的起点，而不是成为最终决定。作者引起了批评和讨论。

Using chatbots to deliver recommendations is increasingly popular. The design of recommendation chatbots has primarily been taking an information-centric approach by focusing on the recommended content per se. Limited attention is on how social connection and relational strategies, such as self-disclosure from a chatbot, may influence users' perception and acceptance of the recommendation. In this work, we designed, implemented, and evaluated a social chatbot capable of performing three different levels of self-disclosure: factual information (low), cognitive opinions (medium), and emotions (high). In the evaluation, we recruited 372 participants to converse with the chatbot on two topics: movies and COVID-19 experiences. In each topic, the chatbot performed small talks and made recommendations relevant to the topic. Participants were randomly assigned to four experimental conditions where the chatbot used factual, cognitive, emotional, and adaptive strategies to perform self-disclosures. By training a text classifier to identify users' level of self-disclosure in real-time, the adaptive chatbot can dynamically match its self-disclosure to the level of disclosure exhibited by the users. Our results show that users reciprocate with higher-level self-disclosure when a recommendation chatbot consistently displays emotions throughout the conversation. Chatbot's emotional disclosure also led to increased interactional enjoyment and more positive interpersonal perception towards the bot, fostering a stronger human-chatbot relationship and thus leading to increased recommendation effectiveness, including a higher tendency to accept the recommendation. We discuss the understandings obtained and implications to future design.

对心理健康支持的需求不断增长，强调了对话代理在全球和中国作为人类支持者的重要性。这些代理可以增加可用性并降低心理健康支持的相对成本。提供的支持可以分为两种主要类型：认知和情感支持。关于该主题的现有工作主要集中在采用认知行为疗法（CBT）原理的构造药物上。此类代理根据预定义的模板和练习来运行，以提供认知支持。但是，使用此类药物对情绪支持的研究是有限的。此外，大多数建设的代理商都以英语运作，强调了在中国进行此类研究的重要性。在这项研究中，我们分析了表情符疾病在减少精神痛苦症状方面的有效性。 Emohaa是一种对话剂，通过基于CBT的练习和指导性对话提供认知支持。它还通过使用户能够发泄所需的情绪问题来支持情感上的支持。该研究包括134名参与者，分为三组：Emohaa（基于CBT），Emohaa（Full）和控制。实验结果表明，与对照组相比，使用Emohaa的参与者在精神困扰症状方面的改善得到了更大的改善。我们还发现，添加情感支持剂对这种改善，主要是抑郁和失眠有互补的影响。根据获得的结果和参与者对平台的满意，我们得出结论，Emohaa是减少精神困扰的实用和有效工具。

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In this paper, we explore how HCI and AI researchers conduct user studies in XAI applications based on a systematic literature review. After identifying and thoroughly analyzing 85 core papers with human-based XAI evaluations over the past five years, we categorize them along the measured characteristics of explanatory methods, namely trust, understanding, fairness, usability, and human-AI team performance. Our research shows that XAI is spreading more rapidly in certain application domains, such as recommender systems than in others, but that user evaluations are still rather sparse and incorporate hardly any insights from cognitive or social sciences. Based on a comprehensive discussion of best practices, i.e., common models, design choices, and measures in user studies, we propose practical guidelines on designing and conducting user studies for XAI researchers and practitioners. Lastly, this survey also highlights several open research directions, particularly linking psychological science and human-centered XAI.

在过去的几年中，围绕种族类人体机器人的有问题实践的讨论已经上升。为了彻底理解机器人在人类机器人互动（HRI）社区中如何理解机器人的“性别” - 即如何被操纵，在哪些环境中以及其对人们的看法和人们产生哪些影响的影响，为基础建立基础。与机器人的互动 - 我们对文献进行了范围的评论。我们确定了553篇与我们从5个不同数据库中检索的评论相关的论文。审查论文的最终样本包括2005年至2021年之间的35篇论文，其中涉及3902名参与者。在本文中，我们通过报告有关其性别的目标和假设的信息（即操纵性别的定义和理由），对机器人的“性别”（即性别提示和操纵检查），对性别的定义和理由进行彻底总结这些论文。 （例如，参与者的人口统计学，受雇的机器人）及其结果（即主要和互动效应）。该评论表明，机器人的“性别”不会影响HRI的关键构建，例如可爱和接受，而是对刻板印象产生最强烈的影响。我们利用社会机器人技术和性别研究中的不同认识论背景来提供有关审查结果的全面跨学科观点，并提出了在HRI领域前进的方法。

There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a 'good' explanation. There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations towards the explanation process. This paper argues that the field of explainable artificial intelligence should build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.

我们介绍了Sparrow，这是一个寻求信息的对话代理，与提示的语言模型基线相比，训练有素，更有帮助，正确和无害。我们使用从人类反馈中的强化学习来培训我们的模型，以帮助人类评估者判断代理人的行为。首先，为了使我们的代理人更有帮助和无害，我们将良好对话的要求分解为代理人应遵循的自然语言规则，并分别向评估者询问每个规则。我们证明，这种崩溃使我们能够收集对代理行为的更多针对性的人类判断，并允许更有效的规则条件奖励模型。其次，我们的代理商在收集对模型声明的偏好判决时提供了支持事实主张的来源的证据。对于事实问题，麻雀提供的证据支持了78％的时间。比基线比基线更享受麻雀，同时对人类的对抗性探测更具弹性，在探测时只有8％的时间违反了我们的规则。最后，我们进行了广泛的分析，表明尽管我们的模型学会遵守我们的规则，但它可以表现出分布偏见。