Dispensing with Humans in Human-Computer Interaction Research

Igor Steinmacher M. Gerosa Anita Sarma + 1 lainnya

18 November 2023

Research within sociotechnical domains, such as software engineering, fundamentally requires the human perspective. Nevertheless, traditional qualitative data collection methods suffer from difficulties in participant recruitment, scaling, and labor intensity. This vision paper proposes a novel approach to qualitative data collection in software engineering research by harnessing the capabilities of artificial intelligence (AI), especially large language models (LLMs) like ChatGPT and multimodal foundation models. We explore the potential of AI-generated synthetic text as an alternative source of qualitative data, discussing how LLMs can replicate human responses and behaviors in research settings. We discuss AI applications in emulating humans in interviews, focus groups, surveys, observational studies, and user evaluations. We discuss open problems and research opportunities to implement this vision. In the future, an integrated approach where both AI and human-generated data coexist will likely yield the most effective outcomes.

L. Kolbe Stephan Diederich A. Brendel + 1 lainnya

2022

Conversational agents (CAs), described as software with which humans interact through natural language, have increasingly attracted interest in both academia and practice because of improved capabilities driven by advances in artificial intelligence and, specifically, natural language processing. CAs are used in contexts such as peoples private lives, education, and healthcare, as well as in organizations to innovate or automate tasks for example, in marketing, sales, or customer service. In addition to these application contexts, CAs take on different forms in terms of their embodiment, the communication mode, and their (often human-like) design. Despite their popularity, many CAs are unable to fulfill expectations, and fostering a positive user experience is challenging. To better understand how CAs can be designed to fulfill their intended purpose and how humans interact with them, a number of studies focusing on human-computer interaction have been carried out in recent years, which have contributed to our understanding of this technology. However, currently, a structured overview of this research is lacking, thus impeding the systematic identification of research gaps and knowledge on which future studies can build. To address this issue, we conducted an organizing and assessing review of 262 studies, applying a sociotechnical lens to analyze CA research regarding user interaction, context, agent design, as well as CA perceptions and outcomes. This study contributes an overview of the status quo of CA research, identifies four research streams through cluster analysis, and proposes a research agenda comprising six avenues and sixteen directions to move the field forward

E. Silva Hossein Hassani

27 Maret 2023

ChatGPT, a conversational AI interface that utilizes natural language processing and machine learning algorithms, is taking the world by storm and is the buzzword across many sectors today. Given the likely impact of this model on data science, through this perspective article, we seek to provide an overview of the potential opportunities and challenges associated with using ChatGPT in data science, provide readers with a snapshot of its advantages, and stimulate interest in its use for data science projects. The paper discusses how ChatGPT can assist data scientists in automating various aspects of their workflow, including data cleaning and preprocessing, model training, and result interpretation. It also highlights how ChatGPT has the potential to provide new insights and improve decision-making processes by analyzing unstructured data. We then examine the advantages of ChatGPT’s architecture, including its ability to be fine-tuned for a wide range of language-related tasks and generate synthetic data. Limitations and issues are also addressed, particularly around concerns about bias and plagiarism when using ChatGPT. Overall, the paper concludes that the benefits outweigh the costs and ChatGPT has the potential to greatly enhance the productivity and accuracy of data science workflows and is likely to become an increasingly important tool for intelligence augmentation in the field of data science. ChatGPT can assist with a wide range of natural language processing tasks in data science, including language translation, sentiment analysis, and text classification. However, while ChatGPT can save time and resources compared to training a model from scratch, and can be fine-tuned for specific use cases, it may not perform well on certain tasks if it has not been specifically trained for them. Additionally, the output of ChatGPT may be difficult to interpret, which could pose challenges for decision-making in data science applications.

Qiang Yang

14 April 2021

From predictive medicine to autonomous driving, advances in Artificial Intelligence (AI) promise to improve people’s lives and improve society. As systems that utilize these advancesincreasingly migrated from research labs into the real world, new challenges emerged. For example, when and how should predictive models fit into physicians’ decision-makingworkflow such that the predictions impact them appropriately? These are challenges of translation: translating AI systems from systems that demonstrate remarkable technological achievements into real-world, socio-technical systems that serve human ends. My research focuses on this critical translation; on the user experience (UX) design of AI systems. The prevalence of AI suggests that the UX design community has effective design methods and tools to excel in this translation. While this is true in many cases, some challenges persist. For example, designers struggle with accounting for AI systems’ unpredictable errors, and these errors damage UX and even lead to undesirable societal impacts. UX designersroutinely grapple with technologies’ unanticipated technical or human failures, with a focus on mitigating technologies unintended consequences. What makes AI different fromother interactive technologies? – A critical first step in systematically addressing the UX design challenges of AI systems is to articulate what makes these systems so difficult to design in the first place. This dissertation delineates whether, when, and how UX of AI systems is uniquely difficult to design. I synthesize prior UX and AI research, my own experience designing human-AI interactions, my studies of experienced AI innovation teams in the industry, and my observations from teaching human-AI interaction. I trace the nebulous UX design challenges of AI back to just two root challenges: uncertainty around AI systems’ capabilities and the complexity of what systems might output. I present a framework that unravels their effects on design processes; namely AI systems’ “design complexity framework”. Using the framework,I identify four levels of AI systems. On each level, designers are likely to encounter a different subset of design challenges: Current design methods are most effective in eliciting, addressing, and evaluating the UX issues of Level 1 systems (probabilistic systems, systems with known capability with few possible outputs); Current methods are least effective for Level 4 systems (evolving, adaptive systems, systems that can learn from new data postdeployment and can produce complex outputs that resist abstraction or simulation). Level 2 and 3 are two intermediate levels. I further demonstrate the usefulness of this framework for UX research and practice through two case studies. In both cases, I engaged stakeholders in their real-world contexts and addressed a critical challenge in fitting cutting-edge AI systems into people’s everydaylives. The first is the design of a clinical decision-support system that can effectively collaborate with doctors in making life-and-death treatment decisions. It exemplifies Level 1 systems. The second project is an investigation of how Natural Language Generation systems might seamlessly serve the authors’ communicative intent. This illustrates Level 4 systems. It reveals the limits of UX design methods and processes widely in use today. By teasing apart the challenges of routine UX design and those distinctively needed for AI systems, the framework helps UX researchers and design tool makers to address AI systems’design challenges in a targeted fashion.

Jiaxi Liu

18 Juni 2024

The release of GPT-4 has garnered widespread attention across various fields, signaling the impending widespread adoption and application of Large Language Models (LLMs). However, previous research has predominantly focused on the technical principles of ChatGPT and its social impact, overlooking its effects on human–computer interaction and user psychology. This paper explores the multifaceted impacts of ChatGPT on human–computer interaction, psychology, and society through a literature review. The author investigates ChatGPT’s technical foundation, including its Transformer architecture and RLHF (Reinforcement Learning from Human Feedback) process, enabling it to generate human-like responses. In terms of human–computer interaction, the author studies the significant improvements GPT models bring to conversational interfaces. The analysis extends to psychological impacts, weighing the potential of ChatGPT to mimic human empathy and support learning against the risks of reduced interpersonal connections. In the commercial and social domains, the paper discusses the applications of ChatGPT in customer service and social services, highlighting the improvements in efficiency and challenges such as privacy issues. Finally, the author offers predictions and recommendations for ChatGPT’s future development directions and its impact on social relationships.