Brainrot: Deskilling and Addiction are Overlooked AI Risks

TL;DR

This paper highlights overlooked AI risks like deskilling and addiction related to generative AI, emphasizing the need for safety measures addressing cognitive and mental health impacts.

Contribution

It identifies and quantifies overlooked risks of deskilling and addiction in AI safety discourse, proposing initial ideas for mitigation strategies.

Findings

Deskilling and addiction are significant but under-addressed AI risks.

Quantitative analysis shows increasing dependence on GenAI affects cognition.

Discussion on potential safety and regulation measures for mental health risks.

Abstract

The scope of AI safety and alignment work in generative artificial intelligence (GenAI) has so far mostly been limited to harms related to: (a) discrimination and hate speech, (b) harmful/inappropriate (violent, sexual, illegal) content, (c) information hazards, and (d) use cases related to malicious actors, such as cybersecurity, child abuse, and chemical, biological, radiological, and nuclear threats. The public conversation around AI, on the other hand, has also been focusing on threats to our cognition, mental health, and welfare at large, related to over-relying on new technologies, most recently, those related to GenAI. Examples include deskilling associated with cognitive offloading and the atrophy of critical thinking as a result of over-reliance on GenAI systems, and addiction associated with attachment and dependence on GenAI systems. Such risks are rarely addressed, if at all, in the AI safety and alignment literature. In this paper, we highlight and quantify this discrepancy and discuss some initial thoughts on how safety and alignment work could address cognitive and mental health concerns. Finally, we discuss how information campaigns and regulation can be used to mitigate such prominent risks.