Public discourse on AI has become polarized; exaggerated positions on AI in traditional and social media threaten the development of AI Literacy among the general public. In this article, I introduce the VET Framework, a method for categorizing AI discourse along the dimensions of valence, effectiveness, and trajectory. I show how this framework can be used to identify, compare, and critique prevalent narratives of AI Hype, AI Doom, AI Denial, and AI Normalcy. Using VET, I analyze how each of these four stances exaggerates some aspects of the current state and/or likely evolution of AI, and illustrate how the VET framework can serve as an AI Literacy tool by supporting the “vetting” of polarized AI discourse.

Despite widespread discussion of AGI, there is no clear framework for measuring progress toward it. This ambiguity fuels subjective claims, makes it difficult to track progress, and risks hindering responsible governance. As a starting point to address this gap, we present a framework for understanding system capabilities in relation to human cognitive abilities. Drawing from decades of research in psychology, neuroscience, and cognitive science, we introduce a Cognitive Taxonomy that deconstructs general intelligence into 10 key cognitive faculties. We then propose a rigorous evaluation protocol in which a system's performance is measured across a suite of targeted, held-out cognitive tasks, generating a 'cognitive profile' that can be used to understand a system's strengths and weaknesses. We hope this framework will provide a practical roadmap and an initial step toward more rigorous, empirical evaluation of AGI.

The tendency of users to anthropomorphise large language models (LLMs) is of growing interest to AI developers, researchers, and policy-makers. Here, we present a novel method for empirically evaluating anthropomorphic LLM behaviours in realistic and varied settings. Going beyond single-turn static benchmarks, we contribute three methodological advances in state-of-the-art (SOTA) LLM evaluation. First, we develop a multi-turn evaluation of 14 anthropomorphic behaviours. Second, we present a scalable, automated approach by employing simulations of user interactions. Third, we conduct an interactive, large-scale human subject study (N=1101) to validate that the model behaviours we measure predict real users' anthropomorphic perceptions. We find that all SOTA LLMs evaluated exhibit similar behaviours, characterised by relationship-building (e.g., empathy and validation) and first-person pronoun use, and that the majority of behaviours only first occur after multiple turns. Our work lays an empirical foundation for investigating how design choices influence anthropomorphic model behaviours and for progressing the ethical debate on the desirability of these behaviours. It also showcases the necessity of multi-turn evaluations for complex social phenomena in human-AI interaction.

As AI systems quickly improve in both breadth and depth of performance, they lend themselves to creating increasingly powerful and realistic agents, including the possibility of agents modeled on specific people. We anticipate that within our lifetimes it may become common practice for people to create custom AI agents to interact with loved ones and/or the broader world after death; indeed, the past year has seen a boom in startups purporting to offer such services. We call these generative ghosts since such agents will be capable of generating novel content rather than merely parroting content produced by their creator while living. In this paper, we reflect on the history of technologies for AI afterlives, including current early attempts by individual enthusiasts and startup companies to create generative ghosts. We then introduce a novel design space detailing potential implementations of generative ghosts. We use this analytic framework to ground a discussion of the practical and ethical implications of various approaches to designing generative ghosts, including potential positive and negative impacts on individuals and society. Based on these considerations, we lay out a research agenda for the AI and HCI research communities to better understand the risk/benefit landscape of this novel technology to ultimately empower people who wish to create and interact with AI afterlives to do so in a beneficial manner.

Machine learning can predict human behavior well when substantial structured data and well-defined outcomes are available, but these models are typically limited to specific outcomes and cannot readily be applied to new domains. We test whether large language models (LLMs) can support a more general-purpose approach by building person-specific simulations (i.e., "generative agents") grounded in self-report data. Using data from a diverse national sample of 1,052 Americans, we build agents from (i) two-hour, semi-structured interviews (elicited using the American Voices Project interview schedule), (ii) structured surveys (the General Social Survey and Big Five personality inventory), or (iii) both sources combined. On held-out General Social Survey items, agent accuracy reached 83

AI technologies are likely to impact an array of existing practices (and give rise to a host of novel ones) around end-of-life planning, remembrance, and legacy in ways that will have profound legal, economic, emotional, and religious ramifications. At this critical moment of technological change, there is an opportunity for the HCI community to shape the discourse on this important topic through value-sensitive and community-centered approaches. This workshop will bring together a broad group of academics and practitioners with varied perspectives including HCI, AI, and other relevant disciplines (e.g., law, economics, religious studies, etc.) to support community-building, agenda-setting, and prototyping activities among scholars and practitioners interested in the nascent topic of how advances in AI will change socio-technical practices around death, remembrance, and legacy.

This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user, across one or more domains, in line with the user's expectations. The paper starts by considering the technology itself, providing an overview of AI assistants, their technical foundations and potential range of applications. It then explores questions around AI value alignment, well-being, safety and malicious uses. Extending the circle of inquiry further, we next consider the relationship between advanced AI assistants and individual users in more detail, exploring topics such as manipulation and persuasion, anthropomorphism, appropriate relationships, trust and privacy. With this analysis in place, we consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants. Finally, we conclude by providing a range of recommendations for researchers, developers, policymakers and public stakeholders.

AI-generated images are proliferating as a new visual medium. However, state-of-the-art image generation models do not output alternative (alt) text with their images, rendering them largely inaccessible to screen reader users (SRUs). Moreover, less is known about what information would be most desirable to SRUs in this new medium. To address this, we invited AI image creators and SRUs to evaluate alt text prepared from various sources and write their own alt text for AI images. Our mixed-methods analysis makes three contributions. First, we highlight creators’ perspectives on alt text, as creators are well-positioned to write descriptions of their images. Second, we illustrate SRUs’ alt text needs particular to the emerging medium of AI images. Finally, we discuss the promises and pitfalls of utilizing text prompts written as input for AI models in alt text generation, and areas where broader digital accessibility guidelines could expand to account for AI images.

Recent advances in general-purpose AI underscore the urgent need to align AI systems with human goals and values. Yet, the lack of a clear, shared understanding of what constitutes "alignment" limits meaningful progress and cross-disciplinary collaboration. In this position paper, we argue that the research community should explicitly define and critically reflect on "alignment" to account for the bidirectional and dynamic relationship between humans and AI. Through a systematic review of over 400 papers spanning HCI, NLP, ML, and more, we examine how alignment is currently defined and operationalized. Building on this analysis, we introduce the Bidirectional Human-AI Alignment framework, which not only incorporates traditional efforts to align AI with human values but also introduces the critical, underexplored dimension of aligning humans with AI – supporting cognitive, behavioral, and societal adaptation to rapidly advancing AI technologies. Our findings reveal significant gaps in current literature, especially in long-term interaction design, human value modeling, and mutual understanding. We conclude with three central challenges and actionable recommendations to guide future research toward more nuanced, reciprocal, and human-AI alignment approaches.

As systems graduate from labs to the open world, moving beyond prompting is central to ensuring that AI is useful, usable, and safe for end users as well as experts such as AI developers and researchers.

The development of increasingly agentic and human-like AI assistants, capable of performing a wide range of tasks on user's behalf over time, has sparked heightened interest in the nature and bounds of human interactions with AI. Such systems may indeed ground a transition from task-oriented interactions with AI, at discrete time intervals, to ongoing relationships - where users develop a deeper sense of connection with and attachment to the technology. This paper investigates what it means for relationships between users and advanced AI assistants to be appropriate and proposes a new framework to evaluate both users' relationships with AI and developers' design choices. We first provide an account of advanced AI assistants, motivating the question of appropriate relationships by exploring several distinctive features of this technology. These include anthropomorphic cues and the longevity of interactions with users, increased AI agency, generality and context ambiguity, and the forms and depth of dependence the relationship could engender. Drawing upon various ethical traditions, we then consider a series of values, including benefit, flourishing, autonomy and care, that characterise appropriate human interpersonal relationships. These values guide our analysis of how the distinctive features of AI assistants may give rise to inappropriate relationships with users. Specifically, we discuss a set of concrete risks arising from user-AI assistant relationships that: (1) cause direct emotional or physical harm to users, (2) limit opportunities for user personal development, (3) exploit user emotional dependence, and (4) generate material dependencies without adequate commitment to user needs. We conclude with a set of recommendations to address these risks.

AbstractAccelerating text input in augmentative and alternative communication (AAC) is a long-standing area of research with bearings on the quality of life in individuals with profound motor impairments. Recent advances in large language models (LLMs) pose opportunities for re-thinking strategies for enhanced text entry in AAC. In this paper, we present SpeakFaster, consisting of an LLM-powered user interface for text entry in a highly-abbreviated form, saving 57% more motor actions than traditional predictive keyboards in offline simulation. A pilot study on a mobile device with 19 non-AAC participants demonstrated motor savings in line with simulation and relatively small changes in typing speed. Lab and field testing on two eye-gaze AAC users with amyotrophic lateral sclerosis demonstrated text-entry rates 29–60% above baselines, due to significant saving of expensive keystrokes based on LLM predictions. These findings form a foundation for further exploration of LLM-assisted text entry in AAC and other user interfaces.

The natural language capabilities demonstrated by large language models (LLMs) highlight an opportunity for new writing-support tools that address the varied needs of people with dyslexia. We present LaMPost, a prototype email editor that draws upon our understanding of these needs to motivate AI-powered writing features, such as outlining main ideas, generating a subject line, suggesting changes, and rewriting a selection. We evaluated LaMPost with 19 adults with dyslexia, identifying promising routes for further exploration (such as the popular "rewrite" and "subject line" features), while also finding that the current generation of LLMs may not yet meet the accuracy and quality thresholds to be useful for writers with dyslexia. In addition, knowledge of the AI did not alter participants' perception of the system nor their feelings of autonomy, expression, and self-efficacy when writing email messages. Our findings provide insight into the benefits and drawbacks of LLMs as writing support for adults with dyslexia, and they offer a foundation to build upon in future research.

A goal of accessible technology (AT) design is often to increase independence, i.e., to enable people with disabilities to accomplish tasks on their own without help. Recent work challenges this view by recognizing the role of ‘help’ in addressing the access needs of people with disabilities. However, empirical evidence examining help is limited to the Global North; we address this gap using a case study of how people with visual impairments (PVI) navigate indoor environments in India. Using interviews with PVI and their companions and a video-diary study, we find that help is a key practice that PVI use to navigate indoor environments. We uncover how help is a situated phenomenon shaped by socio-material and cultural factors unique to the Indian context. We discuss the value of help in the context of broader HCI and Accessibility literature on mixed-ability and collaborative interactions. We also discuss implications our findings on help have for AT design.

Advances in deep learning systems have allowed large models to match or surpass human accuracy on a number of skills such as image classification, basic programming, and standardized test taking. As the performance of the most capable models begin to saturate on tasks where humans already achieve high accuracy, it becomes necessary to benchmark models on increasingly complex abilities. One such task is forecasting the future outcome of events. In this work we describe experiments using a novel dataset of real world events and associated human predictions, an evaluation metric to measure forecasting ability, and the accuracy of a number of different LLM based forecasting designs on the provided dataset. Additionally, we analyze the performance of the LLM forecasters against human predictions and find that models still struggle to make accurate predictions about the future. Our follow-up experiments indicate this is likely due to models' tendency to guess that most events are unlikely to occur (which tends to be true for many prediction datasets, but does not reflect actual forecasting abilities). We reflect on next steps for developing a systematic and reliable approach to studying LLM forecasting.

We propose a framework for classifying the capabilities and behavior of Artificial General Intelligence (AGI) models and their precursors. This framework introduces levels of AGI performance, generality, and autonomy, providing a common language to compare models, assess risks, and measure progress along the path to AGI. To develop our framework, we analyze existing definitions of AGI, and distill six principles that a useful ontology for AGI should satisfy. With these principles in mind, we propose "Levels of AGI" based on depth (performance) and breadth (generality) of capabilities, and reflect on how current systems fit into this ontology. We discuss the challenging requirements for future benchmarks that quantify the behavior and capabilities of AGI models against these levels. Finally, we discuss how these levels of AGI interact with deployment considerations such as autonomy and risk, and emphasize the importance of carefully selecting Human-AI Interaction paradigms for responsible and safe deployment of highly capable AI systems.

We are at a transformational junction in computing, in the midst of an explosion in capabilities of foundational AI models that may soon match or exceed typical human abilities for a wide variety of cognitive tasks, a milestone often termed Artifcial General Intelligence (AGI). Achieving AGI (or even closely approaching it) will transform computing, with ramifcations permeating through all aspects of society. This is a critical moment not only for Machine Learning research, but also for the feld of Human-Computer Interaction (HCI). In this talk, I will defne what I mean (and what I do NOT mean) by “AGI” (and related concepts, like superintelligence), and my journey from AGI skeptic to believing we are within fve to ten years of reaching this milestone. I will then discuss how this new era of computing necessitates a new sociotechnical research agenda on methods and interfaces for studying and interacting with AGI. For instance, how can we extend status quo design and prototyping methods for envisioning novel experiences at the limits of our current imaginations? What novel interaction modalities might AGI (or superintelligence) enable (e.g., “ESP”)? How do we create interfaces for computing systems that may intentionally or unintentionally

Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents: computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent’s experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty-five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors. For example, starting with only a single user-specified notion that one agent wants to throw a Valentine’s Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture—observation, planning, and reflection—each contribute critically to the believability of agent behavior. By fusing large language models with computational interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.