Assessing Public Support for Generative AI Legislation in California
2024
- 149Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage149
- Downloads86
- Abstract Views63
Thesis / Dissertation Description
This study investigates the current climate of public opinion among California voters regarding proposed legislation to establish safety protocols and safeguards for the use of generative artificial intelligence (GenAI) in state government operations. As the rapid advancement of GenAI has outpaced the development of comprehensive regulatory frameworks, there is a pressing need to examine whether voters would endorse or reject such legislative proposals upon being informed about the absence of adequate safety measures. The relevant literature surrounding the study informed of proposed safety guidelines for this innovative technology, but no forcing mechanisms or current legislation to support them. Utilizing a mixed-methods approach involving surveys and expert interviews, the research aims to ascertain the potential impact of informing the public about the importance of pre-deployment testing, cybersecurity practices, hacking protections, emergency shut-off commands, and restrictions on deepfakes generated by AI. By assessing public support for these proposed safeguards, the study seeks to inform policymaking and raise awareness about the responsible development and deployment of transformative GenAI technologies.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know