在数字治疗干预的背景下，例如互联网交付的认知行为治疗（ICBT）用于治疗抑郁和焦虑，广泛的研究表明，人类支持者或教练的参与如何协助接受治疗的人，改善用户参与治疗并导致更有效的健康结果而不是不受支持的干预措施。该研究旨在最大限度地提高这一人类支持的影响和结果，研究了通过AI和机器学习领域（ML）领域的最新进展提供的新机遇如何有助于有效地支持ICBT支持者的工作实践。本文报告了采访研究的详细调查结果，与15个ICBT支持者加深了解其现有的工作实践和信息需求，旨在有意义地向抑郁和焦虑治疗的背景下提供有用，可实现的ML申请。分析贡献（1）一组六个主题，总结了ICBT支持者在为其精神卫生客户提供有效，个性化反馈方面的策略和挑战;并回应这些学习，（2）对于ML方法如何帮助支持和解决挑战和信息需求，为每个主题提供具体机会。它依赖于在支持者LED客户审查实践中引入新的机器生成的数据见解的潜在社会，情感和务实含义的思考。

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。

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.

To address the widespread problem of uncivil behavior, many online discussion platforms employ human moderators to take action against objectionable content, such as removing it or placing sanctions on its authors. This reactive paradigm of taking action against already-posted antisocial content is currently the most common form of moderation, and has accordingly underpinned many recent efforts at introducing automation into the moderation process. Comparatively less work has been done to understand other moderation paradigms -- such as proactively discouraging the emergence of antisocial behavior rather than reacting to it -- and the role algorithmic support can play in these paradigms. In this work, we investigate such a proactive framework for moderation in a case study of a collaborative setting: Wikipedia Talk Pages. We employ a mixed methods approach, combining qualitative and design components for a holistic analysis. Through interviews with moderators, we find that despite a lack of technical and social support, moderators already engage in a number of proactive moderation behaviors, such as preemptively intervening in conversations to keep them on track. Further, we explore how automation could assist with this existing proactive moderation workflow by building a prototype tool, presenting it to moderators, and examining how the assistance it provides might fit into their workflow. The resulting feedback uncovers both strengths and drawbacks of the prototype tool and suggests concrete steps towards further developing such assisting technology so it can most effectively support moderators in their existing proactive moderation workflow.

数据对于机器学习（ML）模型的开发和评估至关重要。但是，在部署所得模型时，使用有问题或不适当的数据集可能会造成危害。为了通过对数据集进行更故意的反思和创建过程的透明度来鼓励负责任的练习，研究人员和从业人员已开始倡导增加数据文档，并提出了几个数据文档框架。但是，几乎没有研究这些数据文档框架是否满足创建和消费数据集的ML从业者的需求。为了解决这一差距，我们着手了解ML从业人员的数据文档感知，需求，挑战和Desiderata，目的是推导设计要求，以便为将来的数据文档框架提供信息。我们对一家大型国际技术公司的14名ML从业者进行了一系列半结构化访谈。我们让他们回答从数据集的数据表中提取的问题列表（Gebru，2021）。我们的发现表明，目前的数据文档方法在很大程度上是临时的，而且本质上是近视的。参与者表达了对数据文档框架的需求，可以适应其上下文，并将其集成到现有的工具和工作流程中，并尽可能自动化。尽管事实上，数据文档框架通常是从负责人的AI的角度出发的，但参与者并未在他们被要求回答的问题与负责的AI含义之间建立联系。此外，参与者通常会在数据集消费者的需求中优先考虑，并提供了不熟悉其数据集可能需要知道的信息。基于这些发现，我们为将来的数据文档框架得出了七个设计要求。

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。

在公共危机时期，寻求信息对于人们的自我保健和福祉至关重要。广泛的研究调查了经验理解和技术解决方案，以促进受影响地区的家庭公民寻求信息。但是，建立有限的知识是为了支持需要在其东道国发生危机的国际移民。当前的论文对居住在日本和美国（n = 14）的两名中国移民（n = 14）进行了访谈研究。参与者反思了他们在共同大流行期间寻求经验的信息。反思补充了两周的自我追踪，参与者保持了相关信息寻求实践的记录。我们的数据表明，参与者经常绕开语言绕道，或访问普通话资源以获取有关其东道国疫情爆发的信息。他们还进行了战略性利用普通话信息，以进行选择性阅读，交叉检查以及对日语或英语的共同信息的上下文化解释。尽管这种做法增强了参与者对共同相关信息收集和感官的有效性，但他们有时会通过有时认识的方式使人们处于不利地位。此外，参与者缺乏对审查以移民为导向的信息的认识或偏爱，尽管该信息可用，这些信息是由东道国公共当局发布的。在这些发现的基础上，我们讨论了改善国际移民在非本地语言和文化环境中寻求共同相关信息的解决方案。我们主张包容性危机基础设施，这些基础设施将吸引以当地语言流利程度，信息素养和利用公共服务的经验的不同水平的人们。

机器学习（ML）技术在教育方面越来越普遍，从预测学生辍学，到协助大学入学以及促进MOOC的兴起。考虑到这些新颖用途的快速增长，迫切需要调查ML技术如何支持长期以来的教育原则和目标。在这项工作中，我们阐明了这一复杂的景观绘制，以对教育专家的访谈进行定性见解。这些访谈包括对过去十年中著名应用ML会议上发表的ML教育（ML4ED）论文的深入评估。我们的中心研究目标是批判性地研究这些论文的陈述或暗示教育和社会目标如何与他们解决的ML问题保持一致。也就是说，技术问题的提出，目标，方法和解释结果与手头的教育问题保持一致。我们发现，在ML生命周期的两个部分中存在跨学科的差距，并且尤其突出：从教育目标和将预测转换为干预措施的ML问题的提出。我们使用这些见解来提出扩展的ML生命周期，这也可能适用于在其他领域中使用ML。我们的工作加入了越来越多的跨教育和ML研究的荟萃分析研究，以及对ML社会影响的批判性分析。具体而言，它填补了对机器学习的主要技术理解与与学生合作和政策合作的教育研究人员的观点之间的差距。

The need for AI systems to provide explanations for their behaviour is now widely recognised as key to their adoption. In this paper, we examine the problem of trustworthy AI and explore what delivering this means in practice, with a focus on healthcare applications. Work in this area typically treats trustworthy AI as a problem of Human-Computer Interaction involving the individual user and an AI system. However, we argue here that this overlooks the important part played by organisational accountability in how people reason about and trust AI in socio-technical settings. To illustrate the importance of organisational accountability, we present findings from ethnographic studies of breast cancer screening and cancer treatment planning in multidisciplinary team meetings to show how participants made themselves accountable both to each other and to the organisations of which they are members. We use these findings to enrich existing understandings of the requirements for trustworthy AI and to outline some candidate solutions to the problems of making AI accountable both to individual users and organisationally. We conclude by outlining the implications of this for future work on the development of trustworthy AI, including ways in which our proposed solutions may be re-used in different application settings.

本报告介绍了Waymo关于系统疲劳风险管理框架的建议，该框架解决了在ADS技术的公路测试期间疲劳诱导的风险的预防，监测和缓解。所提出的框架仍然可以灵活地纳入持续的改进，并受到最先进的实践，研究，学习和经验的信息（内部和外部的Waymo）。疲劳是涉及人类驾驶员的大部分公路撞车事故的公认因素，缓解疲劳引起的风险仍然是全球研究的公开关注。虽然提出的框架是专门针对SAE 4级广告技术的公路测试而设计的，但它对较低的自动化也具有含义和适用性。

Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction. While previous work explored the potential of automatically monitoring exercises for AI and robotic coaches, the deployment of these systems remains a challenge. Previous work described the lack of involving stakeholders to design such functionalities as one of the major causes. In this paper, we present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patient's exercises in an effective and acceptable way with four therapists and five post-stroke survivors. Through iterative questionnaires and interviews, we found that both post-stroke survivors and therapists appreciated the potential benefits of AI and robotic coaches to achieve more systematic management and improve their self-efficacy and motivation on rehabilitation therapy. In addition, our evaluation sheds light on several practical concerns (e.g. a possible difficulty with the interaction for people with cognitive impairment, system failures, etc.). We discuss the value of early involvement of stakeholders and interactive techniques that complement system failures, but also support a personalized therapy session for the better deployment of AI and robotic exercise coaches.

Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization. However, pre-training objectives tailored for abstractive text summarization have not been explored. Furthermore there is a lack of systematic evaluation across diverse domains. In this work, we propose pre-training large Transformer-based encoder-decoder models on massive text corpora with a new selfsupervised objective. In PEGASUS, important sentences are removed/masked from an input document and are generated together as one output sequence from the remaining sentences, similar to an extractive summary. We evaluated our best PEGASUS model on 12 downstream summarization tasks spanning news, science, stories, instructions, emails, patents, and legislative bills. Experiments demonstrate it achieves state-of-the-art performance on all 12 downstream datasets measured by ROUGE scores. Our model also shows surprising performance on low-resource summarization, surpassing previous state-of-the-art results on 6 datasets with only 1000 examples. Finally we validated our results using human evaluation and show that our model summaries achieve human performance on multiple datasets.

随着各种公开的AI伦理原则的共识，差距仍然可以随时采用设计和开发负责任的AI系统。我们研究了来自澳大利亚国家科学研究机构（CSIRO）的研究人员和工程师的实践和经验，他们参与设计和开发AI系统的一系列目的。半结构化访谈用于检查参与者的做法如何与澳大利亚政府提出的一套高级AI伦理原则涉及并对齐。原则包括：隐私保护和安全，可靠性和安全性，透明度和解释性，公平性，竞争性，责任，人以人为本的价值观和人类，社会与环境福祉。研究了研究人员和工程师的见解以及在原则的实际应用中为它们提供的挑战。最后，提供了一系列组织响应，以支持实施高级AI道德原则。

In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.

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.

我们提出了一项探索性定性研究，以了解作家如何与下一页建议相互作用。尽管对建议系统对写作的影响进行了一些定量研究，但几乎没有定性的工作来理解作家如何与建议系统互动及其如何影响他们的写作过程 - 特别是针对非本地但英国作家的。我们进行了一项研究，要求业余作家分别写两部电影评论，一本没有建议。我们发现作家以各种复杂的方式与下一页建议互动 - 作家能够抽象建议的多个部分并将其纳入他们的写作中 - 即使他们不同意整个建议。建议系统对写作过程也有各种影响 - 以独特的方式为写作过程的不同方面做出了影响。我们提出了一种用于与GPT-2写作的作家 - 探索互动模型，用于电影评论写作任务，然后是该模型可用于未来研究的方式，并概述了研究和设计的机会。

聊天机器人在商业和科学环境中越来越普遍。他们帮助客户抱怨产品或服务或支持他们找到最佳旅行交易。其他机器人提供心理健康支持或帮助预订医疗预约。本文认为，可以洞悉用户的语言意识形态及其融洽的期望，可用于告知受众群体的语言和互动模式，并确保公平地访问机器人提供的服务。该论点的基础是三种数据的基础：与聊天机器人相互交互，促进健康约会预订，用户对其交互的内省评论以及用户的定性调查评论在与预订机器人交战后。最后，我将定义对话式AI的受众设计，并讨论如何以用户为中心的聊天机器人互动和社会语言知识的理论方法（例如融洽的理论管理）来支持受众设计。

可编程逻辑控制器（PLC）推动对社会至关重要的工业过程，例如水处理和分配，电力和燃料网络。搜索引擎（例如Shodan）强调说，可编程逻辑控制器（PLC）经常暴露于Internet，这是安全设置的错误配置的主要原因之一。这导致了一个问题 - 为什么这些错误配置会发生，具体而言，安全控制的可用性是否起作用？迄今为止，尚未研究配置PLC安全机制的可用性。我们通过基于任务的研究和随后的半结构化访谈（n = 19）介绍了第一次调查。我们探索PLC连接配置和两个关键安全机制的可用性（即访问级别和用户管理）。我们发现使用不熟悉的标签，布局和误导性术语加剧了已经复杂的配置安全机制的过程。我们的结果揭示了对安全控制的各种看法，以及设计约束（例如，安全性和缺乏定期更新）如何（由于这种系统的长期性质），为实现现代HCI和可用性原则的实现提供了重大挑战。基于这些发现，我们提供了设计建议，以在工业环境中与IT同行提供可用的安全性。

As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as 'Constitutional AI'. The process involves both a supervised learning and a reinforcement learning phase. In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses. In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels.

Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 hrs) were conducted based on the DARPA Subterranean Challenge. These missions were to deploy a heterogeneous team of robots for a search task to locate and identify artifacts such as climbing rope, drills and mannequins representing human survivors. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Team) compared to autonomous missions without human operator input (Robot-Autonomy). Human-Robot Teams were often in directed autonomy mode (70% of mission time), found more items, traversed more distance, covered more unique ground, and had a higher time between safety-related events. Human-Robot Teams were faster at finding the first artifact, but slower to respond to information from the robot team. In routine conditions, scores were comparable for artifacts, distance, and coverage. Reasons for intervention included creating waypoints to prioritise high-yield areas, and to navigate through error-prone spaces. After observing robot autonomy, operators reported increases in robot competency and trust, but that robot behaviour was not always transparent and understandable, even after high mission performance.