Activities per year
Abstract
The widespread adoption of generative artificial intelligence (AI) has fundamentally transformed technological landscapes and societal structures in recent years. Our objective is to identify the primary methodologies that may be used to help predict the economic and social impacts of generative AI adoption. Through a comprehensive literature review, we uncover a range of methodologies poised to assess the multifaceted impacts of this technological revolution. We explore Agent-Based Simulation (ABS), Econometric Models, Input-Output Analysis, Reinforcement Learning (RL) for Decision-Making Agents, Surveys and Interviews, Scenario Analysis, Policy Analysis, and the Delphi Method. Our findings have allowed us to identify these approaches' main strengths and weaknesses and their adequacy in coping with uncertainty, robustness, and resource requirements.
Original language | English |
---|---|
Publisher | Cornell University (ArXiv) |
Number of pages | 6 |
DOIs | |
Publication status | Submitted - 14 Nov 2024 |
Keywords
- Generative AI
- AI adoption
- methods
- prediction
- methodology
Activities
- 1 Oral presentation
-
Socio-Economic Consequences of Generative AI: A Review of Methodological Approaches
Carlos Costa (Speaker), João Tiago Aparício (Speaker) & Manuela Aparicio (Speaker)
26 Jun 2024Activity: Talk or presentation › Oral presentation