However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered trustworthy. In this paper, we synthesize existing conceptualizations of trustworthy AI along six requirements: 1) human agency and oversight, 2) fairness and non-discrimination, 3) transparency and explainability, 4) robustness and accuracy, 5) privacy and security, and 6) accountability. For each one, we provide a definition, describe how it can be established and evaluated, and discuss requirement-specific research challenges. Finally, we conclude this analysis by identifying overarching research challenges across the requirements with respect to 1) interdisciplinary research, 2) conceptual clarity, 3) context-dependency, 4) dynamics in evolving systems, and 5) investigations in real-world contexts. Thus, this paper synthesizes and consolidates a wide-ranging and active discussion currently taking place in various academic sub-communities and public forums. It aims to serve as a reference for a broad audience and as a basis for future research directions.

A paradigm shift is underway in Software Engineering, with AI systems such as LLMs gaining increasing importance for improving software development productivity. This trend is anticipated to persist. In the next five years, we will likely see an increasing symbiotic partnership between human developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-Driven world and explore the key challenges that our research community should address to realize this vision.

This positional paper outlines our vision of 'adaptive architecture', which involves the integration of robotic technology to physically change an architectural space in supporting the changing needs of its occupants, in response to the CHI'24 workshop "HabiTech - Inhabiting Buildings, Data & Technology" call on "How do new technologies enable and empower the inhabitants of multi-occupancy buildings?". Specifically, while adaptive architecture holds promise for enhancing occupant satisfaction, comfort, and overall health and well-being, there remains a range of research challenges of (1) how it can effectively support individual occupants, while (2) mediating the conflicting needs of collocated others, and (3) integrating meaningfully into the sociocultural characteristics of their building community.

Despite this progress, patients often face various health-related challenges in their long-term recovery[9, 10]. More than half of patients develop new physical, psychological, and/or cognitive problems following their ICU admission [7], collectively referred to as Post Intensive Care Syndrome (PICS) [3, 25]. Family members also experience a stressful period, potentially leading to psychological problems addressed as PICS-Family (PICS-F) [2]. Patient and family-centered care (PFCC) at the ICU, including emotional support and follow-up service, could mitigate the symptoms associated with both PICS and PICS-F. In this study, we explored how an emerging technology, i.e., large language models, could support the emotional well-being of people exposed to critical care.

Large language models (LLMs), such as ChatGPT and GPT-4, are gaining wide-spread real world use. Yet, these LLMs are closed source, and little is known about their performance in real-world use cases. In this paper, we apply and evaluate the combination of ChatGPT and GPT-4 for the real-world task of mining insights from a text corpus in order to identify research challenges in the field of HCI. We extract 4,392 research challenges in over 100 topics from the 2023 CHI conference proceedings and visualize the research challenges for interactive exploration. We critically evaluate the LLMs on this practical task and conclude that the combination of ChatGPT and GPT-4 makes an excellent cost-efficient means for analyzing a text corpus at scale. Cost-efficiency is key for flexibly prototyping research ideas and analyzing text corpora from different perspectives, with implications for applying LLMs for mining insights in academia and practice.

We propose a hierarchical framework for collaborative intelligent systems. This framework organizes research challenges based on the nature of the collaborative activity and the information that must be shared, with each level building on capabilities provided by lower levels. We review research paradigms at each level, with a description of classical engineering-based approaches and modern alternatives based on machine learning, illustrated with a running example using a hypothetical personal service robot. We discuss cross-cutting issues that occur at all levels, focusing on the problem of communicating and sharing comprehension, the role of explanation and the social nature of collaboration. We conclude with a summary of research challenges and a discussion of the potential for economic and societal impact provided by technologies that enhance human abilities and empower people and society through collaboration with Intelligent Systems.

There is a bidirectional relationship between culture and AI; AI models are increasingly used to analyse culture, thereby shaping our understanding of culture. On the other hand, the models are trained on collections of cultural artifacts thereby implicitly, and not always correctly, encoding expressions of culture. This creates a tension that both limits the use of AI for analysing culture and leads to problems in AI with respect to cultural complex issues such as bias. One approach to overcome this tension is to more extensively take into account the intricacies and complexities of culture. We structure our discussion using four concepts that guide humanistic inquiry into culture: subjectivity, scalability, contextuality, and temporality. We focus on these concepts because they have not yet been sufficiently represented in AI research. We believe that possible implementations of these aspects into AI research leads to AI that better captures the complexities of culture. In what follows, we briefly describe these four concepts and their absence in AI research. For each concept, we define possible research challenges.

Multiple times over the last decade, this column has covered the issue of the importance of data quality in decision making, both by executives as well as machines. Back in 2014, when the "big data" craze was mesmerizing the C-Suite, the warning was issued in Big Data and the Madness of Crowds. More recently in How Bad Data Is Undermining Big Data Analytics from December 2020. Since then, more and more news has emerged regarding the failures of AI and Machine Learning initiatives with the blame given to faulty data as the reason. The recent demise of IBM Watson Health is the latest example.

This paper discusses recent research progress in robotic grasping and manipulation in the light of the latest Robotic Grasping and Manipulation Competitions (RGMCs). We first provide an overview of past benchmarks and competitions related to the robotics manipulation field. Then, we discuss the methodology behind designing the manipulation tasks in RGMCs. We provide a detailed analysis of key challenges for each task and identify the most difficult aspects based on the competing teams' performance in recent years. We believe that such an analysis is insightful to determine the future research directions for the robotic manipulation domain.

In this column, we recount the history of EAAI (Educational Advances in Artificial Intelligence) mentored undergraduate research challenges from 2014 through the present and share a vision of how such offerings may become more diverse and engage a broader range of faculty mentors and undergraduate researchers. Unlike many academic disciplines, Computer Science undergraduate majors currently are not usually required to take or even offered a research methods course. Even so, many graduate schools desire to admit graduate students with undergraduate research experience. The EAAI Symposium has historically affirmed the value of mentored undergraduate research as an important part of undergraduate AI education. It has expressed this value through the support of a number of mentored undergraduate research challenges, described below.