mlsanityData to 14 Oct 2026

Actionable AI: Enabling Non Experts to Understand and Configure AI Systems

arXiv 2503.06803 ↗First posted 9 Mar 2025cs.HC cs.AIcited by 0

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Abstract (arXiv)

Interaction between humans and AI systems raises the question of how people understand AI systems. This has been addressed with explainable AI, the interpretability arising from users' domain expertise, or collaborating with AI in a stable environment. In the absence of these elements, we discuss designing Actionable AI, which allows non-experts to configure black-box agents. In this paper, we experiment with an AI-powered cartpole game and observe 22 pairs of participants to configure it via direct manipulation. Our findings suggest that, in uncertain conditions, non-experts were able to achieve good levels of performance. By influencing the behaviour of the agent, they exhibited an operational understanding of it, which proved sufficient to reach their goals. Based on this, we derive implications for designing Actionable AI systems. In conclusion, we propose Actionable AI as a way to open access to AI-based agents, giving end users the agency to influence such agents towards their own goals.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 42 references that are in the map. First posted on arXiv 9 Mar 2025.

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References · 13

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  2. 2021A Survey of Human-in-the-loop for Machine Learningcited by 215
  3. 2017What Does Explainable AI Really Mean? A New Conceptualization of Perspectivescited by 72
  4. 2023Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision supportcited by 66
  5. 2022How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Modelscited by 44
  6. 2019Leveraging Human Guidance for Deep Reinforcement Learning Taskscited by 42
  7. 2021Human-AI Collaboration with Bandit Feedbackcited by 36
  8. 2021Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recoverycited by 32
  9. 2019Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creatorscited by 31
  10. 2022Understanding User Perceptions, Collaborative Experience and User Engagement in Different Human-AI Interaction Designs for Co-Creative Systemscited by 8
  11. 2019Unexplainability and Incomprehensibility of Artificial Intelligencecited by 5
  12. 2021Expose Uncertainty, Instill Distrust, Avoid Explanations: Towards Ethical Guidelines for AIcited by 3
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  1. 2020Why robots should be technical: Correcting mental models through technical architecture conceptscited by 2

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