Technology Policy Recommendations Using Artificial Intelligence

Authors

  • Ashita Anuga Virginia Tech
  • Minh Nguyen Virginia Tech
  • Dominick Perini Virginia Tech
  • Andrei Svetovidov Virginia Tech
  • Amanda Tolman Radford University
  • Qasim Wani Virginia Tech
  • Feras A. Batarseh Virginia Tech https://orcid.org/0000-0002-6062-2747

DOI:

https://doi.org/10.32473/flairs.v34i1.128499

Keywords:

Technology Policy, Internet Freedoms, Data Privacy, Data democracy

Abstract

Conventionally, the approach to policy making includes
weighing the costs and benefits (i.e., tradeoffs) of certain
choices to calculate expected outcomes. However, quantifying
choices is not always straightforward without understanding
many factors such as time, causal effects, and associations
- making it difficult to label policy as either a
failure or a success. Accordingly, our work proposes utilizing
Artificial Intelligence (AI) algorithms to assess the impact
of policy (state-level science and technology policies as
an example). Our approach allows for an efficient policy
generating process, providing policymakers with insights
based on previous legislation and historical data for their respective
states. Leveraging AI this way stimulates humanlike
learning which can yield better results with the subjective
behavior of public policy. Our approach consists of collecting
datasets relevant to science and technology policies,
utilizing AI to create methods for determining the best path
forward, testing the validity of the algorithms using AI assurance,
and measuring attributions to determine which
components contribute to the outcomes most effectively.
Using AI provides context relevant to the impacts of certain
policies, and an overall data-driven approach that mitigates
depending solely on expert’s judgment, subjective experiences,
or ad-hoc processes.

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Published

2021-04-18

How to Cite

Anuga, A., Nguyen, M., Perini, D., Svetovidov, A., Tolman, A., Wani, Q., & Batarseh, F. A. (2021). Technology Policy Recommendations Using Artificial Intelligence. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128499

Issue

Section

Poster Abstracts