TY - JOUR AU - Anuga, Ashita AU - Nguyen, Minh AU - Perini, Dominick AU - Svetovidov, Andrei AU - Tolman, Amanda AU - Wani, Qasim AU - Batarseh, Feras A. PY - 2021/04/18 Y2 - 2024/03/29 TI - Technology Policy Recommendations Using Artificial Intelligence JF - The International FLAIRS Conference Proceedings JA - FLAIRS VL - 34 IS - 0 SE - Posters DO - 10.32473/flairs.v34i1.128499 UR - https://journals.flvc.org/FLAIRS/article/view/128499 SP - AB - <p>Conventionally, the approach to policy making includes<br>weighing the costs and benefits (i.e., tradeoffs) of certain<br>choices to calculate expected outcomes. However, quantifying<br>choices is not always straightforward without understanding<br>many factors such as time, causal effects, and associations<br>- making it difficult to label policy as either a<br>failure or a success. Accordingly, our work proposes utilizing<br>Artificial Intelligence (AI) algorithms to assess the impact<br>of policy (state-level science and technology policies as<br>an example). Our approach allows for an efficient policy<br>generating process, providing policymakers with insights<br>based on previous legislation and historical data for their respective<br>states. Leveraging AI this way stimulates humanlike<br>learning which can yield better results with the subjective<br>behavior of public policy. Our approach consists of collecting<br>datasets relevant to science and technology policies,<br>utilizing AI to create methods for determining the best path<br>forward, testing the validity of the algorithms using AI assurance,<br>and measuring attributions to determine which<br>components contribute to the outcomes most effectively.<br>Using AI provides context relevant to the impacts of certain<br>policies, and an overall data-driven approach that mitigates<br>depending solely on expert’s judgment, subjective experiences,<br>or ad-hoc processes.</p> ER -