A Large-Scale, Automated Study of Language Surrounding Artificial Intelligence
This work addresses the need for scalable and long-term insights into public and expert perceptions of AI/ML, though it is incremental as it builds on prior small-scale studies by automating and expanding the analysis.
The authors tackled the problem of understanding how language and perceptions around AI/ML evolve over time by conducting a large-scale automated analysis of news articles and scientific publications from 2011 to 2019, using word association measurements to identify shifts and quantify their strength, which revealed emerging application areas like blockchain and cybersecurity.
This work presents a large-scale analysis of artificial intelligence (AI) and machine learning (ML) references within news articles and scientific publications between 2011 and 2019. We implement word association measurements that automatically identify shifts in language co-occurring with AI/ML and quantify the strength of these word associations. Our results highlight the evolution of perceptions and definitions around AI/ML and detect emerging application areas, models, and systems (e.g., blockchain and cybersecurity). Recent small-scale, manual studies have explored AI/ML discourse within the general public, the policymaker community, and researcher community, but are limited in their scalability and longevity. Our methods provide new views into public perceptions and subject-area expert discussions of AI/ML and greatly exceed the explanative power of prior work.