Towards the Terminator Economy: Assessing Job Exposure to AI through LLMs
AI and related technologies are reshaping jobs and tasks, either by automating or augmenting human skills in the workplace. Many researchers have been working on estimating if and to what extent jobs and tasks are exposed to the risk of being automatized by AI-related technologies. Our work tackles this issue through a data-driven approach by: (i) developing a reproducible framework that uses cutting-edge open-source large language models to assess the current capabilities of AI and robotics in performing job-related tasks; (ii) formalizing and computing a measure of AI exposure by occupation, the Task Exposure to AI (TEAI) index, and a measure of Task Replacement by AI (TRAI), both validated through a human user evaluation and compared with the state of the art. Our results show that the TEAI index is positively correlated with cognitive, problem-solving and management skills, while it is negatively correlated with social skills. Applying the index to the US, we obtain that about one-third of US employment is highly exposed to AI, primarily in high-skill jobs requiring a graduate or postgraduate level of education. We also find that AI exposure is positively associated with both employment and wage growth in 2003-2023, suggesting that AI has an overall positive effect on productivity. Considering specifically the TRAI index, we find that even in high-skill occupations, AI exhibits high variability in task substitution, suggesting that AI and humans complement each other within the same occupation, while the allocation of tasks within occupations is likely to change. All results, models, and code are freely available online to allow the community to reproduce our results, compare outcomes, and use our work as a benchmark to monitor AI's progress over time.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs
LLMs are useful because they generalize so well. But can you have too much of a good thing? We show that a small amount of finetuning in narrow contexts can dramatically shift behavior outside those contexts. In one expe…
Terminators: Terms of Service Parsing and Auditing Agents
Terms of Service (ToS) documents are often lengthy and written in complex legal language, making them difficult for users to read and understand. To address this challenge, we propose Terminators, a modular agentic frame…
Term ExtractionA Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies
In recent years, significant concern has emerged regarding the potential threat that Large Language Models (LLMs) pose to democratic societies through their persuasive capabilities. We expand upon existing research by co…
Language ModelingLanguage ModellingLarge Language ModelPersuasivenessMind-proofing Your Phone: Navigating the Digital Minefield with GreaseTerminator
Digital harms are widespread in the mobile ecosystem. As these devices gain ever more prominence in our daily lives, so too increases the potential for malicious attacks against individuals. The last line of defense agai…
Urban and non-urban contributions to the social cost of carbon
The social cost of carbon (SCC) serves as a concise gauge of climate change's economic impact, often reported at the global and country level. SCC values are disproportionately high for less-developed, populous countries…