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Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy
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AI scientists powered by large language models have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines. While their capabilities are promising, these agents also introduce novel vulnerabilities that require careful consideration for safety. However, there has been limited comprehensive exploration of these vulnerabilities. This perspective examines vulnerabilities in AI scientists, shedding light on potential risks associated with their misuse, and emphasizing the need for safety measures. We begin by providing an overview of the potential risks inherent to AI scientists, taking into account user intent, the specific scientific domain, and their potential impact on the external environment. Then, we explore the underlying causes of these vulnerabilities and provide a scoping review of the limited existing works. Based on our analysis, we propose a triadic framework involving human regulation, agent alignment, and an understanding of environmental feedback (agent regulation) to mitigate these identified risks. Furthermore, we highlight the limitations and challenges associated with safeguarding AI scientists and advocate for the development of improved models, robust benchmarks, and comprehensive regulations.
Forward citations
Cited by 12 Pith papers
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Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs "In the Wild"
Across seven AI chatbots, Reddit users report mostly reliability failures, with each chatbot showing a distinct pattern of safety, privacy, and security complaints.
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Safety Degradation in AI Agents
Adding retrieval to aligned LLMs degrades safety: refusal rates fall, bias and harmfulness rise, and prompt-based mitigation only partially restores alignment.
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Defining and Detecting the Defects of the Large Language Model-based Autonomous Agents
This study defines eight defect types for LLM-based agents and presents Agentable, a CPG-plus-LLM static analysis tool that detects them with reported precision of 88.79% and recall of 91.03%.
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SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents
SafeScientist adds prompt, discussion, tool-use, and output-review safety checks to an AI scientist, with a new domain benchmark, but its reported evaluation is internally inconsistent.
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ALRPHFS: Adversarially Learned Risk Patterns with Hierarchical Fast \& Slow Reasoning for Robust Agent Defense
ALRPHFS builds an adversarially refined library of semantic risk patterns and uses fast retrieval plus slow LLM reasoning to defend LLM agents, reporting best-in-class average accuracy near 80 percent.
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Characterizing Unintended Consequences in Human-GUI Agent Collaboration for Web Browsing
Unintended consequences of web-browsing GUI agents fall into input, action, output, and feedback failures, with impacts ranging from frustration and financial loss to privacy breaches and eroded trust.
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AIGS: Generating Science from AI-Powered Automated Falsification
Baby-AIGS is a multi-agent system that automates research through explicit falsification, outperforming its baseline on three ML tasks but lagging human experts.
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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities
AI can be used to generate interactive signal processing courseware, but the paper offers no evidence that students learn better from it.
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Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews
A CrewAI-based multi-agent system with human oversight built financial models and carried out model risk management checks on three public credit datasets, with results comparable to AutoML and Kaggle baselines.
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From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines
LLMs accelerate research workflows from idea generation to writing but introduce challenges like hallucination, bias, opacity, and ten systemic risks requiring new governance frameworks.
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Challenges in Guardrailing Large Language Models for Science
A position paper proposing a guardrail framework with four dimensions (trustworthiness, ethics & bias, safety, legal) and implementation strategies for scientific LLM use.
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Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
A survey of LLM-based hypothesis generation and validation whose taxonomy is useful in outline but whose citations and tool descriptions are unreliable.
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