Open AccessTechnology Policy Recommendations Using Artificial Intelligence
Author(s) -
Ashita Anuga,
Minh N. H. Nguyen,
Dominick J. Perini,
Andrei Svetovidov,
Amanda Tolman,
Qasim Wani,
Feras A. Batarseh
Publication year - 2021
Publication title -
proceedings of the ... international florida artificial intelligence research society conference
Language(s) - English
Resource type - Journals
eISSN - 2334-0762
pISSN - 2334-0754
DOI - 10.32473/flairs.v34i1.128499
Subject(s) - computer science , context (archaeology) , process (computing) , legislation , attribution , artificial intelligence , data science , management science , machine learning , knowledge management , psychology , political science , engineering , social psychology , paleontology , law , biology , operating system
Conventionally, the approach to policy making includesweighing the costs and benefits (i.e., tradeoffs) of certainchoices to calculate expected outcomes. However, quantifyingchoices is not always straightforward without understandingmany factors such as time, causal effects, and associations- making it difficult to label policy as either afailure or a success. Accordingly, our work proposes utilizingArtificial Intelligence (AI) algorithms to assess the impactof policy (state-level science and technology policies asan example). Our approach allows for an efficient policygenerating process, providing policymakers with insightsbased on previous legislation and historical data for their respectivestates. Leveraging AI this way stimulates humanlikelearning which can yield better results with the subjectivebehavior of public policy. Our approach consists of collectingdatasets relevant to science and technology policies,utilizing AI to create methods for determining the best pathforward, testing the validity of the algorithms using AI assurance,and measuring attributions to determine whichcomponents contribute to the outcomes most effectively.Using AI provides context relevant to the impacts of certainpolicies, and an overall data-driven approach that mitigatesdepending solely on expert’s judgment, subjective experiences,or ad-hoc processes.