Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916854472114176 |
|---|---|
| author | Tang, Xiangru Jin, Qiao Zhu, Kunlun Yuan, Tongxin Zhang, Yichi Zhou, Wangchunshu Qu, Meng Zhao, Yilun Tang, Jian Zhang, Zhuosheng Cohan, Arman Lu, Zhiyong Gerstein, Mark |
| author_facet | Tang, Xiangru Jin, Qiao Zhu, Kunlun Yuan, Tongxin Zhang, Yichi Zhou, Wangchunshu Qu, Meng Zhao, Yilun Tang, Jian Zhang, Zhuosheng Cohan, Arman Lu, Zhiyong Gerstein, Mark |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04247 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy Tang, Xiangru Jin, Qiao Zhu, Kunlun Yuan, Tongxin Zhang, Yichi Zhou, Wangchunshu Qu, Meng Zhao, Yilun Tang, Jian Zhang, Zhuosheng Cohan, Arman Lu, Zhiyong Gerstein, Mark Computers and Society Artificial Intelligence Computation and Language Machine Learning 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. |
| title | Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy |
| topic | Computers and Society Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2402.04247 |