mlsanityData to 14 Oct 2026

Human-Centered Automation

arXiv 2405.15960 ↗First posted 24 May 2024cs.HC cs.AIcited by 0

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Abstract (arXiv)

The rapid advancement of Generative Artificial Intelligence (AI), such as Large Language Models (LLMs) and Multimodal Large Language Models (MLLM), has the potential to revolutionize the way we work and interact with digital systems across various industries. However, the current state of software automation, such as Robotic Process Automation (RPA) frameworks, often requires domain expertise and lacks visibility and intuitive interfaces, making it challenging for users to fully leverage these technologies. This position paper argues for the emerging area of Human-Centered Automation (HCA), which prioritizes user needs and preferences in the design and development of automation systems. Drawing on empirical evidence from human-computer interaction research and case studies, we highlight the importance of considering user perspectives in automation and propose a framework for designing human-centric automation solutions. The paper discusses the limitations of existing automation approaches, the challenges in integrating AI and RPA, and the benefits of human-centered automation for productivity, innovation, and democratizing access to these technologies. We emphasize the importance of open-source solutions and provide examples of how HCA can empower individuals and organizations in the era of rapidly progressing AI, helping them remain competitive. The paper also explores pathways to achieve more advanced and context-aware automation solutions. We conclude with a call to action for researchers and practitioners to focus on developing automation technologies that adapt to user needs, provide intuitive interfaces, and leverage the capabilities of high-end AI to create a more accessible and user-friendly future of automation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 51 references that are in the map. First posted on arXiv 24 May 2024.

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4 of 51 references.

References · 4

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2018From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbotscited by 85
  4. 2020X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformerscited by 59

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  5. 2023Who's Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbook
  6. 2023ProAgent: From Robotic Process Automation to Agentic Process Automation
  7. 2026A Framework of User Experience Principles for Human-AI Agent Interaction in the Workplace
  8. 2026From Human-Human Collaboration to Human-Agent Collaboration: A Vision, Design Philosophy, and an Empirical Framework for Achieving Successful Partnerships Between Humans and LLM Agents