Neural Information Processing Systems http://nips.cc/

Models for collecting and aggregating categorical data on crowdsourcing platforms typically fall into two broad categories: those assuming agents honest and consistent but with heterogeneous error rates, and those assuming agents strategic and seek to maximize their expected reward. The former often leads to tractable aggregation of elicited data, while the latter usually focuses on optimal elicitation and does not consider aggregation. In this paper, we develop a Bayesian model, wherein agents have differing quality of information, but also respond to incentives. Our model generalizes both categories and enables the joint exploration of optimal elicitation and aggregation. This model enables our exploration, both analytically and experimentally, of optimal aggregation of categorical data and optimal multiple-choice interface design.

As large language models (LLMs) become ubiquitous in workplace tools and decision-making processes, ensuring explainability and fostering user trust are critical. Although advancements in LLM engineering continue, human-centered design is still catching up, particularly when it comes to embedding transparency and trust into AI interfaces. This study evaluates user experiences with two distinct AI interfaces - node-tree interfaces and chatbot interfaces - to assess their performance in exploratory, follow-up inquiry, decision-making, and problem-solving tasks. Our design-driven approach introduces a node-tree interface that visually structures AI-generated responses into hierarchically organized, interactive nodes, allowing users to navigate, refine, and follow up on complex information. In a comparative study with n=20 business users, we observed that while the chatbot interface effectively supports linear, step-by-step queries, it is the node-tree interface that enhances brainstorming. Quantitative and qualitative findings indicate that node-tree interfaces not only improve task performance and decision-making support but also promote higher levels of user trust by preserving context. Our findings suggest that adaptive AI interfaces capable of switching between structured visualizations and conversational formats based on task requirements can significantly enhance transparency and user confidence in AI-powered systems. This work contributes actionable insights to the fields of human-robot interaction and AI design, particularly for enterprise applications where trust-building is critical for teams.

Human reliability remains a critical concern in safety-critical domains such as nuclear power, where operational failures are often linked to human error. While conventional human reliability analysis (HRA) methods have been widely adopted, they rely heavily on expert judgment for identifying human failure events (HFEs) and assigning performance influencing factors (PIFs). This reliance introduces challenges related to reproducibility, subjectivity, and limited integration of interface-level data. In particular, current approaches lack the capacity to rigorously assess how human-machine interface design contributes to operator performance variability and error susceptibility. To address these limitations, this study proposes a framework for risk-informed human failure event identification and interface-induced risk assessment driven by AutoGraph (InSight-R). By linking empirical behavioral data to the interface-embedded knowledge graph (IE-KG) constructed by the automated graph-based execution framework (Auto-Graph), the InSight-R framework enables automated HFE identification based on both error-prone and time-deviated operational paths. Furthermore, we discuss the relationship between designer-user conflicts and human error. This framework offers actionable insights for interface design optimization and contributes to the advancement of mechanism-driven HRA methodologies. Keywords: Knowledge-Graph-Driven, Automated, Interface-Induced Risk, Human Error Identification 1 Introduction Human error remains a leading contributor to failures in complex socio-technical systems such as nuclear power plants, aviation, and healthcare, where safety-critical operations depend on accurate and timely human decisions [1, 2]. Human reliability analysis (HRA) methods have been widely used to model operator behavior and assess the likelihood of human failure events (HFEs) [3]. However, prevailing HRA approaches are often constrained by their reliance on expert judgment, particularly in the identification of HFEs and the assignment of performance influencing factors (PIFs) [3, 4]. In traditional HRA frameworks such as the integrated human event analysis system for event and condition assessment (IDHEAS-ECA), HFEs are primarily determined through expert elicitation, a process that, while practical, suffers from limited reproducibility, insufficient transparency, and weak theoretical grounding [5].

Generative UI is transforming interface design by facilitating AI-driven collaborative workflows between designers and computational systems. This study establishes a working definition of Generative UI through a multi-method qualitative approach, integrating insights from a systematic literature review of 127 publications, expert interviews with 18 participants, and analyses of 12 case studies. Our findings identify five core themes that position Generative UI as an iterative and co-creative process. We highlight emerging design models, including hybrid creation, curation-based workflows, and AI-assisted refinement strategies. Additionally, we examine ethical challenges, evaluation criteria, and interaction models that shape the field. By proposing a conceptual foundation, this study advances both theoretical discourse and practical implementation, guiding future HCI research toward responsible and effective generative UI design practices.

Today's text-entry tools offer a plethora of interface technologies to support users in a variety of situations and with a range of different input methods and devices.16 Recent hardware developments have enabled remarkable innovations, such as virtual keyboards that allow users to type in thin air, or to use their body as a surface for text entry. Similarly, advances in machine learning and natural language processing have enabled high-quality text generation for various purposes, such as summarizing, expanding, and co-authoring. As these technologies rapidly develop, there has been a rush to incorporate them into existing systems, often with little thought for the interactivity problems this may cause. The use of large language models (LLMs) to speed up text generation and improve prediction or completion models is becoming increasingly commonplace, with enormous theoretical efficiency savings;29 however, the implementation of these LLMs into text-entry interfaces is crucial to realizing their potential.

This paper introduces a framework for human swarm interaction studies that measures situation awareness in dynamic environments. A tablet-based interface was developed for a user study by implementing the concepts introduced in the framework, where operators guided a robotic swarm in a single-target search task, marking hazardous cells unknown to the swarm. Both subjective and objective situation awareness measures were used, with task performance evaluated based on how close the robots were to the target. The framework enabled a structured investigation of the role of situation awareness in human swarm interaction, leading to key findings such as improved task performance across attempts, showing the interface was learnable, centroid active robot position proved to be a useful task performance metric for assessing situation awareness, perception and projection played a key role in task performance, highlighting their importance in interface design and both subjective and objective situation awareness influenced task performance, emphasizing the need for interfaces that support both. These findings validate our framework as a structured approach for integrating situation awareness concepts into human swarm interaction studies, offering a systematic way to assess situation awareness and task performance. The framework can be applied to other swarming studies to evaluate interface learnability, identify meaningful task performance metrics, and refine interface designs to enhance situation awareness, ultimately improving human swarm interaction in dynamic environments.

Self-supervised speech (SSL) models have recently become widely adopted for many downstream speech processing tasks. The general usage pattern is to employ SSL models as feature extractors, and then train a downstream prediction head to solve a specific task. However, different layers of SSL models have been shown to capture different types of information, and the methods of combining them are not well studied. To this end, we extend the general framework for SSL model utilization by proposing the interface that connects the upstream and downstream. Under this view, the dominant technique of combining features via a layerwise weighted sum can be regarded as a specific interface. We propose several alternative interface designs and demonstrate that the weighted sum interface is suboptimal for many tasks. In particular, we show that a convolutional interface whose depth scales logarithmically with the depth of the upstream model consistently outperforms many other interface designs.

The proliferation of applications using artificial intelligence (AI) systems has led to a growing number of users interacting with these systems through sophisticated interfaces. Human-computer interaction research has long shown that interfaces shape both user behavior and user perception of technical capabilities and risks. Yet, practitioners and researchers evaluating the social and ethical risks of AI systems tend to overlook the impact of anthropomorphic, deceptive, and immersive interfaces on human-AI interactions. Here, we argue that design features of interfaces with adaptive AI systems can have cascading impacts, driven by feedback loops, which extend beyond those previously considered. We first conduct a scoping review of AI interface designs and their negative impact to extract salient themes of potentially harmful design patterns in AI interfaces. Then, we propose Design-Enhanced Control of AI systems (DECAI), a conceptual model to structure and facilitate impact assessments of AI interface designs. DECAI draws on principles from control systems theory -- a theory for the analysis and design of dynamic physical systems -- to dissect the role of the interface in human-AI systems. Through two case studies on recommendation systems and conversational language model systems, we show how DECAI can be used to evaluate AI interface designs.

Various industries such as finance, meteorology, and energy generate vast amounts of heterogeneous data every day. There is a natural demand for humans to manage, process, and display data efficiently. However, it necessitates labor-intensive efforts and a high level of expertise for these data-related tasks. Considering that large language models (LLMs) have showcased promising capabilities in semantic understanding and reasoning, we advocate that the deployment of LLMs could autonomously manage and process massive amounts of data while displaying and interacting in a human-friendly manner. Based on this belief, we propose Data-Copilot, an LLM-based system that connects numerous data sources on one end and caters to diverse human demands on the other end. Acting like an experienced expert, Data-Copilot autonomously transforms raw data into visualization results that best match the user's intent. Specifically, Data-Copilot autonomously designs versatile interfaces (tools) for data management, processing, prediction, and visualization. In real-time response, it automatically deploys a concise workflow by invoking corresponding interfaces step by step for the user's request. The interface design and deployment processes are fully controlled by Data-Copilot itself, without human assistance. Besides, we create a Data-Copilot demo that links abundant data from different domains (stock, fund, company, economics, and live news) and accurately respond to diverse requests, serving as a reliable AI assistant.