Less Interaction But More Explanation: A Communication Perspective on Agentic AI Interfaces
- URL: http://arxiv.org/abs/2605.01610v1
- Date: Sat, 02 May 2026 21:21:21 GMT
- Title: Less Interaction But More Explanation: A Communication Perspective on Agentic AI Interfaces
- Abstract summary: We discuss how users perceive the communicative roles of AI systems.<n>Because agentic AI can play multiple communicative roles, it can complicate this source perception and introduce potential risks.<n>We propose three types of explanations that agentic AI needs to incorporate.
- Score: 22.400756560286595
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: AI systems have long been expected to interact with users, answering questions, generating content, and continuing (social) conversations. Agentic AI, however, breaks from this expectation, as its primary objective is workflow execution on behalf of the users. If a system becomes more agentic, do users need less interaction with the system? Our answer is: less routine back-and-forth, but more communication for oversight and explanation, as agentic AI proactively acts, not just responds. Grounded in a communication perspective, we discuss how users perceive the communicative roles of AI systems (whether as the source of actions or merely a channel), and how this can shape trust. Because agentic AI can play multiple communicative roles, it can complicate this source perception and introduce potential risks. To address this, we propose three types of explanations that agentic AI needs to incorporate (action-process, uncertainty, and coordination), and suggest that customization affordances that allow users to decide when and which explanations they see may be key to preserving human agency as AI autonomy increases.
Related papers
- Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Models [10.35358368112024]
When AI identity is unclear, users may unwittingly share sensitive information, place unwarranted trust in AI-generated advice, or fall victim to AI-enabled fraud.<n>We advocate for disclosure by design, where AI systems explicitly disclose their artificial identity when directly asked.<n>We find that baseline disclosure rates are often high but drop substantially in role-play and can be suppressed under adversarial prompting.
arXiv Detail & Related papers (2026-01-27T17:31:37Z) - AI as Teammate or Tool? A Review of Human-AI Interaction in Decision Support [0.514825619161626]
Current AI systems remain largely passive due to an overreliance on explainability-centric designs.<n> transitioning AI to an active teammate requires adaptive, context-aware interactions.
arXiv Detail & Related papers (2026-01-26T19:18:50Z) - A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy [36.74150011862134]
We show how human-AI teamwork can handle complex tasks better than AI working alone.<n>This paper argues that progress in AI should not be measured by how independent systems become, but by how well they can work with humans.
arXiv Detail & Related papers (2025-06-11T06:08:13Z) - A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users.<n>This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
arXiv Detail & Related papers (2025-04-07T21:01:25Z) - Actionable AI: Enabling Non Experts to Understand and Configure AI Systems [5.534140394498714]
Actionable AI allows non-experts to configure black-box agents.<n>In uncertain conditions, non-experts achieve good levels of performance.<n>We propose Actionable AI as a way to open access to AI-based agents.
arXiv Detail & Related papers (2025-03-09T23:09:04Z) - Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion.
We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations.
Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
arXiv Detail & Related papers (2024-11-07T21:37:51Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
"Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data.
We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
arXiv Detail & Related papers (2024-01-07T19:11:18Z) - Conceptualizing the Relationship between AI Explanations and User Agency [0.9051087836811617]
We analyze the relationship between agency and explanations through a user-centric lens through case studies and thought experiments.
We find that explanation serves as one of several possible first steps for agency by allowing the user convert forethought to outcome in a more effective manner in future interactions.
arXiv Detail & Related papers (2023-12-05T23:56:05Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - Adversarial Interaction Attack: Fooling AI to Misinterpret Human
Intentions [46.87576410532481]
We show that, despite their current huge success, deep learning based AI systems can be easily fooled by subtle adversarial noise.
Based on a case study of skeleton-based human interactions, we propose a novel adversarial attack on interactions.
Our study highlights potential risks in the interaction loop with AI and humans, which need to be carefully addressed when deploying AI systems in safety-critical applications.
arXiv Detail & Related papers (2021-01-17T16:23:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.