Human-Centered Design

Source papers 20

The Arxiv papers behind this sub-topic. Links may take you off-site to arxiv.org.

• AI & Human Co-Improvement for Safer Co-Superintelligence
  Self-improvement is a goal currently exciting the field of AI, but is fraught with danger, and may take time to fully achieve. We advocate that a more achievable and better goal for humanity is to max…
• AI Assistance Reduces Persistence and Hurts Independent Performance
  People often optimize for long-term goals in collaboration: A mentor or companion doesn’t just answer questions, but also scaffolds learning, tracks progress, and prioritizes the other person’s growth…
• Beyond Hallucinations: The Illusion of Understanding in Large Language Models
  As large language models (LLMs) become deeply integrated into daily life, from casual interactions to high-stakes decision-making, they inherit the ambiguity, biases, and lack of direct access to trut…
• Building Machines that Learn and Think with People
  What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems t…
• Can AI Explanations Make You Change Your Mind?
  In the context of AI-based decision support systems, explanations can help users to judge when to trust the AI’s suggestion, and when to question it. In this way, human oversight can prevent AI errors…
• Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning
  Large Language Models (LLMs) are increasingly used to simulate human users in interactive settings such as therapy, education, and social role-play. While these simulations enable scalable training an…
• Disambiguating Anthropomorphism and Anthropomimesis in Human-Robot Interaction
  Henry Shevlin [[Emotions]] [[Psychology Users]] [[Design Frameworks]] In this preliminary work, we offer an initial disambiguation of the theoretical concepts anthropomorphism and anthropomimesis in…
• DiscussLLM: Teaching Large Language Models When to Speak
  Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating human-like text, yet they largely operate as reactive agents, responding only when directly promp…
• Enhancing user experience in large language models through human-centered design: Integrating theoretical insights with an experimental study to meet diverse software learning needs with a single document knowledge base
  Abstract: This paper begins with a theoretical exploration of the rise of large language models (LLMs) in Human-Computer Interaction (HCI), their impact on user experience (HX) and related challenges.…
• Goal Alignment in LLM-Based User Simulators for Conversational AI
  While current Large Language Models (LLMs) have advanced user simulation capabilities, we reveal that they struggle to consistently demonstrate goal-oriented behavior across multiturn conversations–a …
• Hallucinations Undermine Trust; Metacognition is a Way Forward
  Despite significant strides in factual reliability, errors—often termed hallucinations—remain a major concern for generative AI, especially as LLMs are increasingly expected to be helpful in more comp…
• How AI Impacts Skill Formation
  AI assistance produces significant productivity gains across professional domains, particularly for novice workers. Yet how this assistance affects the development of skills required to effectively su…
• LLM Generated Persona is a Promise with a Catch
  The use of large language models (LLMs) to simulate human behavior has gained significant attention, particularly through personas that approximate individual characteristics. Persona-based simulation…
• Learning "Partner-Aware" Collaborators in Multi-Party Collaboration
  Large Language Models (LLMs) are increasingly bring deployed in agentic settings where they act as collaborators with humans. Therefore, it is increasingly important to be able to evaluate their abili…
• Next Steps for Human-Centered Generative AI: A Technical Perspective
  Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI). We contribute a comprehensive research agenda that lays out future directio…
• Position: Towards Bidirectional Human-AI Alignment
  chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arxiv.org/pdf/2406.09264 [[Human Centered Design]] [[Evaluations]] Recent advances in general-purpose AI underscore the urgent need to ali…
• Quantifying Human-AI Synergy
  We introduce a novel Bayesian Item Response Theory framework to quantify human– AI synergy, separating individual and collaborative ability while controlling for task difficulty in interactive setting…
• Reflections and New Directions for Human-Centered Large Language Models
  Large Language Models (LLMs) are increasingly shaping the private and professional lives of users, with numerous applications in business, education, finance, healthcare, law, and science. With this r…
• Rhetorical XAI: Explaining AI’s Benefits as well as its Use via Rhetorical Design
  Modern AI systems are notoriously opaque, limiting efforts to understand or audit their behaviors [42, 188]. In response, Explainable Artificial Intelligence (XAI) aims to foster trust and accountabil…
• Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender
  For decades, dual-process theories of judgment and decision-making have served as a foundational framework for modeling cognitive processes. These theories propose two distinct decision-making process…