ai.txt: A Domain-Specific Language for Guiding AI Interactions with the Internet
- URL: http://arxiv.org/abs/2505.07834v1
- Date: Fri, 02 May 2025 00:33:00 GMT
- Title: ai.txt: A Domain-Specific Language for Guiding AI Interactions with the Internet
- Authors: Yuekang Li, Wei Song, Bangshuo Zhu, Dong Gong, Yi Liu, Gelei Deng, Chunyang Chen, Lei Ma, Jun Sun, Toby Walsh, Jingling Xue,
- Abstract summary: We introduce ai.txt, a domain-specific language designed to regulate interactions between AI models, agents, and web content.<n>Our approach aims to aid the governance of AI-Internet interactions, promoting responsible AI use in digital ecosystems.
- Score: 44.29685364907017
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce ai.txt, a novel domain-specific language (DSL) designed to explicitly regulate interactions between AI models, agents, and web content, addressing critical limitations of the widely adopted robots.txt standard. As AI increasingly engages with online materials for tasks such as training, summarization, and content modification, existing regulatory methods lack the necessary granularity and semantic expressiveness to ensure ethical and legal compliance. ai.txt extends traditional URL-based access controls by enabling precise element-level regulations and incorporating natural language instructions interpretable by AI systems. To facilitate practical deployment, we provide an integrated development environment with code autocompletion and automatic XML generation. Furthermore, we propose two compliance mechanisms: XML-based programmatic enforcement and natural language prompt integration, and demonstrate their effectiveness through preliminary experiments and case studies. Our approach aims to aid the governance of AI-Internet interactions, promoting responsible AI use in digital ecosystems.
Related papers
- Agentic Web: Weaving the Next Web with AI Agents [109.13815627467514]
The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web.<n>In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users.<n>We present a structured framework for understanding and building the Agentic Web.
arXiv Detail & Related papers (2025-07-28T17:58:12Z) - Explainability in Context: A Multilevel Framework Aligning AI Explanations with Stakeholder with LLMs [11.11196150521188]
This paper addresses how trust in AI is influenced by the design and delivery of explanations.<n>The framework consists of three layers: algorithmic and domain-based, human-centered, and social explainability.
arXiv Detail & Related papers (2025-06-06T08:54:41Z) - Conversational Alignment with Artificial Intelligence in Context [0.0]
This article explores what it means for AI agents to be conversationally aligned to human communicative norms and practices.<n>We suggest that current large language model (LLM) architectures, constraints, and affordances may impose fundamental limitations on achieving full conversational alignment.
arXiv Detail & Related papers (2025-05-28T22:14:34Z) - Let's have a chat with the EU AI Act [0.0]
This paper introduces an AI-driven self-assessment bot designed to assist users in navigating the European Union AI Act and related standards.<n>Leveraging a Retrieval-Augmented Generation framework, the bot retrieves relevant regulatory texts and provides tailored guidance.<n>The paper explores the bot's architecture, comparing naive and graph-based RAG models, and discusses its potential impact on AI governance.
arXiv Detail & Related papers (2025-05-17T10:24:08Z) - Internet of Agents: Fundamentals, Applications, and Challenges [66.44234034282421]
We introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale.<n>We analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models.
arXiv Detail & Related papers (2025-05-12T02:04:37Z) - Compliance of AI Systems [0.0]
This paper systematically examines the compliance of AI systems with relevant legislation, focusing on the EU's AI Act.<n>The analysis highlighted many challenges associated with edge devices, which are increasingly being used to deploy AI applications closer and closer to the data sources.<n>The importance of data set compliance is highlighted as a cornerstone for ensuring the trustworthiness, transparency, and explainability of AI systems.
arXiv Detail & Related papers (2025-03-07T16:53:36Z) - AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act [2.1897070577406734]
Despite its importance, there is a lack of standards and guidelines to assist with drawing up AI and risk documentation aligned with the AI Act.
We propose AI Cards as a novel holistic framework for representing a given intended use of an AI system.
arXiv Detail & Related papers (2024-06-26T09:51:49Z) - Policy Learning with a Language Bottleneck [65.99843627646018]
We introduce Policy Learning with a Language Bottleneck (PLLB), a framework enabling AI agents to generate linguistic rules.<n>PLLBB alternates between a *rule generation* step guided by language models, and an *update* step where agents learn new policies guided by rules.<n>We show thatPLLB agents are able to learn more interpretable and generalizable behaviors, but can also share the learned rules with human users.
arXiv Detail & Related papers (2024-05-07T08:40:21Z) - AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents [74.17623527375241]
We introduce a novel framework, called AutoGuide, which automatically generates context-aware guidelines from offline experiences.<n>As a result, our guidelines facilitate the provision of relevant knowledge for the agent's current decision-making process.<n>Our evaluation demonstrates that AutoGuide significantly outperforms competitive baselines in complex benchmark domains.
arXiv Detail & Related papers (2024-03-13T22:06:03Z) - Responsible Artificial Intelligence: A Structured Literature Review [0.0]
The EU has recently issued several publications emphasizing the necessity of trust in AI.
This highlights the urgent need for international regulation.
This paper introduces a comprehensive and, to our knowledge, the first unified definition of responsible AI.
arXiv Detail & Related papers (2024-03-11T17:01:13Z) - AI Usage Cards: Responsibly Reporting AI-generated Content [25.848910414962337]
Given AI systems like ChatGPT can generate content that is indistinguishable from human-made work, the responsible use of this technology is a growing concern.
We propose a three-dimensional model consisting of transparency, integrity, and accountability to define the responsible use of AI.
Second, we introduce AI Usage Cards'', a standardized way to report the use of AI in scientific research.
arXiv Detail & Related papers (2023-02-16T08:41:31Z) - "No, to the Right" -- Online Language Corrections for Robotic
Manipulation via Shared Autonomy [70.45420918526926]
We present LILAC, a framework for incorporating and adapting to natural language corrections online during execution.
Instead of discrete turn-taking between a human and robot, LILAC splits agency between the human and robot.
We show that our corrections-aware approach obtains higher task completion rates, and is subjectively preferred by users.
arXiv Detail & Related papers (2023-01-06T15:03:27Z)
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.