
arXiv: 2502.18359
Thanks to advances in large language models, a new type of software agent, the artificial intelligence (AI) agent, has entered the marketplace. Companies such as OpenAI, Google, Microsoft, and Salesforce promise their AI Agents will go from generating passive text to executing tasks. Instead of a travel itinerary, an AI Agent would book all aspects of your trip. Instead of generating text or images for social media post, an AI Agent would post the content across a host of social media outlets. The potential power of AI Agents has fueled legal scholars' fears that AI Agents will enable rogue commerce, human manipulation, rampant defamation, and intellectual property harms. These scholars are calling for regulation before AI Agents cause havoc. This Article addresses the concerns around AI Agents head on. It shows that core aspects of how one piece of software interacts with another creates ways to discipline AI Agents so that rogue, undesired actions are unlikely, perhaps more so than rules designed to govern human agents. It also develops a way to leverage the computer-science approach to value-alignment to improve a user's ability to take action to prevent or correct AI Agent operations. That approach offers and added benefit of helping AI Agents align with norms around user-AI Agent interactions. These practices will enable desired economic outcomes and mitigate perceived risks. The Article also argues that no matter how much AI Agents seem like human agents, they need not, and should not, be given legal personhood status. In short, humans are responsible for AI Agents' actions, and this Article provides a guide for how humans can build and maintain responsible AI Agents.
FOS: Computer and information sciences, Computer Science - Computers and Society, Computers and Society (cs.CY)
FOS: Computer and information sciences, Computer Science - Computers and Society, Computers and Society (cs.CY)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 5 | |
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| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
