Dynamic Estimation of Rater Reliability using Multi-Armed Bandits

One of the critical success factors for supervised machine learning is the quality of target values, or predictions, associated with training instances. Predictions can be discrete labels (such as a binary variable specifying whether a blog post is positive or negative) or continuous ratings (for instance, how boring a video is on a 10-point scale). In some areas, predictions are readily available, while in others, the eort of human workers has to be involved. For instance, in the task of emotion recognition from speech, a large corpus of speech recordings is usually available, and humans denote which emotions are present in which recordings

Reinforcement Learning for Machine Translation: from Simulations to Real-World Applications

If a machine translation is wrong, how we can tell the underlying model to fix it? Answering this question requires (1) a machine learning algorithm to define update rules, (2) an interface for feedback to be submitted, and (3) expertise on the side of the human who gives the feedback. This thesis investigates solutions for machine learning updates, the suitability of feedback interfaces, and the dependency on reliability and expertise for different types of feedback. We start with an interactive online learning scenario where a machine translation (MT) system receives bandit feedback (i.e. only once per source) instead of references for learning. Policy gradient algorithms for statistical and neural MT are developed to learn from absolute and pairwise judgments. Our experiments on domain adaptation with simulated online feedback show that the models can largely improve under weak feedback, with variance reduction techniques being very effective. In production environments offline learning is often preferred over online learning. We evaluate algorithms for counterfactual learning from human feedback in a study on eBay product title translations. Feedback is either collected via explicit star ratings from users, or implicitly from the user interaction with cross-lingual product search. Leveraging implicit feedback turns out to be more successful due to lower levels of noise. We compare the reliability and learnability of absolute Likert-scale ratings with pairwise preferences in a smaller user study, and find that absolute ratings are overall more effective for improvements in down-stream tasks. Furthermore, we discover that error markings provide a cheap and practical alternative to error corrections. In a generalized interactive learning framework we propose a self-regulation approach, where the learner, guided by a regulator module, decides which type of feedback to choose for each input. The regulator is reinforced to find a good trade-off between supervision effect and cost. In our experiments, it discovers strategies that are more efficient than active learning and standard fully supervised learning

How Technology Impacts and Compares to Humans in Socially Consequential Arenas

One of the main promises of technology development is for it to be adopted by people, organizations, societies, and governments -- incorporated into their life, work stream, or processes. Often, this is socially beneficial as it automates mundane tasks, frees up more time for other more important things, or otherwise improves the lives of those who use the technology. However, these beneficial results do not apply in every scenario and may not impact everyone in a system the same way. Sometimes a technology is developed which produces both benefits and inflicts some harm. These harms may come at a higher cost to some people than others, raising the question: {\it how are benefits and harms weighed when deciding if and how a socially consequential technology gets developed?} The most natural way to answer this question, and in fact how people first approach it, is to compare the new technology to what used to exist. As such, in this work, I make comparative analyses between humans and machines in three scenarios and seek to understand how sentiment about a technology, performance of that technology, and the impacts of that technology combine to influence how one decides to answer my main research question.Comment: Doctoral thesis proposal. arXiv admin note: substantial text overlap with arXiv:2110.08396, arXiv:2108.12508, arXiv:2006.1262

Lindsey the Tour Guide Robot: Adaptive Long-Term Autonomy in Social Environments

This project proposes a framework for online adaptation of robot behaviours deployed autonomously in social settings with the goal of increasing the overall users' engagement during the interactions. One of the most critical aspects to address for robots deployed in ``the real world'' is the necessity of interacting with people, whether intentionally or not. Interacting with people requires a wide range of capabilities, from perceiving the different people's intentions and emotional states to generating appropriate behaviours for the specific context of the interaction. Moreover, it requires that robots learn and adapt from experience while interacting with their users. In this project, a mobile robot is embedded in a long-term study in a public museum. The robot has been deployed for more than a year, to date, as an autonomous tour guide to the museum's visitors, with its tasks being guiding people to the position of various exhibits and giving a description of each item. The long-term scenario allows studying how people interact with a robot in an unconstrained setting and give the opportunity of improving the current state-of-the-art robotics autonomy in a social setting. The initial data collection shows that users' engagement during the robotised tours steeply declines after the initial moments of the interaction. The first main contribution of this project is to investigate whether it is possible to automatically assess the users' engagement from the robot point-of-view during the interactions. A dataset of robot ego-centric videos was collected and manually annotated by independent coders with continuous engagement values. From it, an end-to-end regression model was trained to predict engagement from the robot point of view from a single camera. Experimental evaluation shows that the model accurately estimates the engagement level of people during an interaction, even in diverse environments and with different robots. Once the robot can detect the engagement state of users during the interactions, it can potentially plan tangential behaviours to influence the users' attentional state itself. The second contribution of this work is devising an online reinforcement learning algorithm that allows the robot to adapt its behaviour online from the feedback obtained during the interactions. The feedback is obtained from users' engagement values estimated from the robot head camera. In the experimental evaluation, the robot delivers the usual tours to the users with the difference that the choice of some actions is left to the adaptive learning algorithm. Results show that after a few months of exploration, the robot successfully learns a policy that leads people to stay in the interaction for longer

Exploring Diversity and Fairness in Machine Learning

With algorithms, artificial intelligence, and machine learning becoming ubiquitous in our society, we need to start thinking about the implications and ethical concerns of new machine learning models. In fact, two types of biases that impact machine learning models are social injustice bias (bias created by society) and measurement bias (bias created by unbalanced sampling). Biases against groups of individuals found in machine learning models can be mitigated through the use of diversity and fairness constraints. This dissertation introduces models to help humans make decisions by enforcing diversity and fairness constraints. This work starts with a call to action. Bias is rife in hiring, and since algorithms are being used in multiple companies to filter applicants, we need to pay special attention to this application. Inspired by this hiring application, I introduce new multi-armed bandit frameworks to help assign human resources in the hiring process while enforcing diversity through a submodular utility function. These frameworks increase diversity while using less resources compared to original admission decisions of the Computer Science graduate program at the University of Maryland. Moving outside of hiring I present a contextual multi-armed bandit algorithm that enforces group fairness by learning a societal bias term and correcting for it. This algorithm is tested on two real world datasets and shows marked improvement over other in-use algorithms. Additionally I take a look at fairness in traditional machine learning domain adaptation. I provide the first theoretical analysis of this setting and test the resulting model on two deal world datasets. Finally I explore extensions to my core work, delving into suicidality, comprehension of fairness definitions, and student evaluations

인공지능과 대화하기: 일대일 그리고 그룹 상용작용을 위한 대화형 에이전트 시스템 개발

학위논문(박사) -- 서울대학교대학원 : 사회과학대학 언론정보학과, 2022.2. 이준환."인간-컴퓨터 상호작용"과 "사용자 경험"을 넘어, "인간-인공지능 상호작용" 그리고 "알고리즘 경험"의 시대가 도래하고 있다. 기술의 발전은 우리가 의사소통하고 협업하는 방식의 패러다임을 전환했다. 기계 에이전트는 인간 커뮤니케이션에서 적극적이며 주도적인 역할을 수행한다. 하지만 효과적인 AI 기반 커뮤니케이션과 토론 시스템 디자인에 대한 이해와 논의는 부족한 것이 사실이다. 이에 본 연구는 인간-컴퓨터 상호작용의 관점에서 다양한 형태의 커뮤니케이션을 지원할 수 있는 기술적 방법을 탐색하는 것을 목표로 한다. 이를 위해 저자는 일대일 그리고 그룹 상호작용을 지원하는 대화형 에이전트를 제시한다. 구체적으로 본 연구는 1) 일대일 상호작요에서 사용자 관여를 높이는 대화형 에이전트, 2) 일상적인 소셜 그룹 토론을 지원하는 에이전트, 3) 숙의 토론을 가능하게 하는 에이전트를 디자인 및 개발하고 그 효과를 정량적 그리고 정성적으로 검증했다. 시스템을 디자인함에 있어서 인간-컴퓨터 상호작용뿐 아니라, 커뮤니케이션학, 심리학, 그리고 데이터 과학을 접목한 다학제적 접근 방식이 적용되었다. 첫 번째 연구는 일대일 상호작용 상황에서 사용자의 관여 증진을 위한 대화형 에이전트의 효과를 검증했다. 설문조사라는 맥락에서 수행된 이 연구는 웹 설문조사에서 응답자의 불성실로 인해 발생하는 응답 데이터 품질의 문제를 극복하기 위한 새로운 인터랙션 방법으로 텍스트 기반 대화형 에이전트의 가능성을 탐색하는 것을 목표로 했다. 이를 위해 2 (인터페이스: 웹 對 챗봇) X 2 (대화 스타일: 포멀 對 캐쥬얼) 실험을 진행했으며, 만족화 이론에 근거하여 응답 데이터의 품질을 평가했다. 그 결과, 챗봇 설문조사의 참여자가 웹 설문조사의 참여자보다 더 높은 수준의 관여를 보이고, 결과적으로 더 높은 품질의 데이터를 생성하는 것을 확인할 수 있었다. 하지만 이런 챗봇의 데이터 품질에 대한 효과는 챗봇이 친구 같고 캐쥬얼한 대화체를 사용할 때만 나타났다. 이 결과는 대화형 인터랙티비티가 인터페이스뿐 아니라 대화 스타일이라는 효과적인 메세지 전략을 동반할 때 발생하는 것을 의미한다. 두 번째 연구는 일상적인 소셜 채팅 그룹에서 집단의 의사결정과정과 토론을 지원하는 대화형 시스템에 대한 것이다. 이를 위해 GroupfeedBot이라는 대화형 에이전트를 제작하였으며, GroupfeedBot은 (1) 토론 시간을 관리하고, (2) 구성원들의 균등한 참여를 촉진하며, (3) 구성원들의 다양한 의견을 요약 및 조직화하는 기능을 갖고 있다. 해당 에이전트를 평가하기 위해 다양한 태스크 (추론, 의사결정, 자유 토론, 문제 해결 과제)와 그룹 규모(소규모, 중규모)에 관하여 사용자 조사를 시행했다. 그 결과 의견의 다양성 측면에서 GroupfeedBot으로 토론한 집단이 기본 에이전트와 토론한 집단보다 더 다양한 의견을 생성했지만 산출된 결과의 품질과 메시지 양에 있어서는 차이가 없는 것을 확인할 수 있었다. 균등한 참여에 대한 GroupfeedBot의 효과는 태스크의 특성에 따라 다르게 나타났는데, 특히 자유 토론 과제에서 GroupfeedBot이 참여자들의 균등한 참여를 촉진했다. 세 번째 연구는 숙의 토론을 지원하는 대화형 시스템에 대한 것이다. 세 번째 연구에서 개발된 DebateBot은 GroupfeeedBot과 달리 더 진지한 사회적 맥락에서 적용되었다. DebateBot은 (1) 생각하기-짝짓기-공유하기 (Think-Pair-Share) 전략에 따라 토론을 구조화하고, (2) 과묵한 토론자에게 의견을 요청함으로써 동등한 참여를 촉진하는 두 가지 주요 기능을 수행했다. 사용자 평가 결과 DebateBot은 그룹 상호작용을 개선함으로써 심의 토론을 가능하게 했다. 토론 구조화는 토론의 질에 긍정적인 효과를 발휘하였고, 참여자 촉진은 진정한 합의 도달에 기여하였으며, 그룹 구성원들의 주관적 만족도를 향상했다. 본 연구는 이 세 가지 연구의 결과들을 바탕으로 인간-인공지능 커뮤니케이션에 대한 다양한 시사점들을 도출하였으며, 이를 TAMED (Task-Agent-Message-Information Exchange-Relationship Dynamics) 모델로 정리하였다.The advancements in technology shift the paradigm of how individuals communicate and collaborate. Machines play an active role in human communication. However, we still lack a generalized understanding of how exactly to design effective machine-driven communication and discussion systems. How should machine agents be designed differently when interacting with a single user as opposed to when interacting with multiple users? How can machine agents be designed to drive user engagement during dyadic interaction? What roles can machine agents perform for the sake of group interaction contexts? How should technology be implemented in support of the group decision-making process and to promote group dynamics? What are the design and technical issues which should be considered for the sake of creating human-centered interactive systems? In this thesis, I present new interactive systems in the form of a conversational agent, or a chatbot, that facilitate dyadic and group interactions. Specifically, I focus on: 1) a conversational agent to engage users in dyadic communication, 2) a chatbot called GroupfeedBot that facilitates daily social group discussion, 3) a chatbot called DebateBot that enables deliberative discussion. My approach to research is multidisciplinary and informed by not only in HCI, but also communication, psychology and data science. In my work, I conduct in-depth qualitative inquiry and quantitative data analysis towards understanding issues that users have with current systems, before developing new computational techniques that meet those user needs. Finally, I design, build, and deploy systems that use these techniques to the public in order to achieve real-world impact and to study their use by different usage contexts. The findings of this thesis are as follows. For a dyadic interaction, participants interacting with a chatbot system were more engaged as compared to those with a static web system. However, the conversational agent leads to better user engagement only when the messages apply a friendly, human-like conversational style. These results imply that the chatbot interface itself is not quite sufficient for the purpose of conveying conversational interactivity. Messages should also be carefully designed to convey such. Unlike dyadic interactions, which focus on message characteristics, other elements of the interaction should be considered when designing agents for group communication. In terms of messages, it is important to synthesize and organize information given that countless messages are exchanged simultaneously. In terms of relationship dynamics, rather than developing a rapport with a single user, it is essential to understand and facilitate the dynamics of the group as a whole. In terms of task performance, technology should support the group's decision-making process by efficiently managing the task execution process. Considering the above characteristics of group interactions, I created the chatbot agents that facilitate group communication in two different contexts and verified their effectiveness. GroupfeedBot was designed and developed with the aim of enhancing group discussion in social chat groups. GroupfeedBot possesses the feature of (1) managing time, (2) encouraging members to participate evenly, and (3) organizing the members’ diverse opinions. The group which discussed with GroupfeedBot tended to produce more diverse opinions compared to the group discussed with the basic chatbot. Some effects of GroupfeedBot varied by the task's characteristics. GroupfeedBot encouraged the members to contribute evenly to the discussions, especially for the open-debating task. On the other hand, DebateBot was designed and developed to facilitate deliberative discussion. In contrast to GroupfeedBot, DebateBot was applied to more serious and less casual social contexts. Two main features were implemented in DebateBot: (1) structure discussion and (2) request opinions from reticent discussants.This work found that a chatbot agent which structures discussions and promotes even participation can improve discussions, resulting in higher quality deliberative discussion. Overall, adding structure to the discussion positively influenced the discussion quality, and the facilitation helped groups reach a genuine consensus and improved the subjective satisfaction of the group members. The findings of this thesis reflect the importance of understanding human factors in designing AI-infused systems. By understanding the characteristics of individual humans and collective groups, we are able to place humans at the heart of the system and utilize AI technology in a human-friendly way.1. Introduction 1.1 Background 1.2 Rise of Machine Agency 1.3 Theoretical Framework 1.4 Research Goal 1.5 Research Approach 1.6 Summary of Contributions 1.7 Thesis Overview 2. Related Work 2.1 A Brief History of Conversational Agents 2.2 TAMED Framework 3. Designing Conversational Agents for Dyadic Interaction 3.1 Background 3.2 Related Work 3.3 Method 3.4 Results 3.5 Discussion 3.6 Conclusion 4. Designing Conversational Agents for Social Group Discussion 4.1 Background 4.2 Related Work 4.3 Needfinding Survey for Facilitator Chatbot Agent 4.4 GroupfeedBot: A Chatbot Agent For Facilitating Discussion in Group Chats 4.5 Qualitative Study with Small-Sized Group 4.6 User Study With Medium-Sized Group 4.7 Discussion 4.8 Conclusion 5. Designing Conversational Agents for Deliberative Group Discussion 5.1 Background 5.2 Related Work 5.3 DebateBot 5.4 Method 5.5 Results 5.6 Discussion and Design Implications 5.7 Conclusion 6. Discussion 6.1 Designing Conversational Agents as a Communicator 6.2 Design Guidelines Based on TAMED Model 6.3 Technical Considerations 6.4 Human-AI Collaborative System 7. Conclusion 7.1 Research Summary 7.2 Summary of Contributions 7.3 Future Work 7.4 Conclusion박

Interactive Machine Learning with Applications in Health Informatics

Recent years have witnessed unprecedented growth of health data, including millions of biomedical research publications, electronic health records, patient discussions on health forums and social media, fitness tracker trajectories, and genome sequences. Information retrieval and machine learning techniques are powerful tools to unlock invaluable knowledge in these data, yet they need to be guided by human experts. Unlike training machine learning models in other domains, labeling and analyzing health data requires highly specialized expertise, and the time of medical experts is extremely limited. How can we mine big health data with little expert effort? In this dissertation, I develop state-of-the-art interactive machine learning algorithms that bring together human intelligence and machine intelligence in health data mining tasks. By making efficient use of human expert's domain knowledge, we can achieve high-quality solutions with minimal manual effort. I first introduce a high-recall information retrieval framework that helps human users efficiently harvest not just one but as many relevant documents as possible from a searchable corpus. This is a common need in professional search scenarios such as medical search and literature review. Then I develop two interactive machine learning algorithms that leverage human expert's domain knowledge to combat the curse of "cold start" in active learning, with applications in clinical natural language processing. A consistent empirical observation is that the overall learning process can be reliably accelerated by a knowledge-driven "warm start", followed by machine-initiated active learning. As a theoretical contribution, I propose a general framework for interactive machine learning. Under this framework, a unified optimization objective explains many existing algorithms used in practice, and inspires the design of new algorithms.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147518/1/raywang_1.pd