AI Policy: A Naturally Good Time to Experiment and Collaborate

 As we navigate the rapidly evolving landscape of artificial intelligence (AI), a unique opportunity has arisen for social science researchers. With governments at various levels rolling out AI policies, we find ourselves in the midst of a grand natural experiment that can offer valuable insights into the impacts of these policies on society.

Natural experiments, as defined by the National Institutes of Health (NIH), involve the evaluation of exposures or changes not directly manipulated by researchers, but rather occurring naturally in the environment. They present the chance to study effects that would be difficult or unethical to engineer in controlled settings. These experiments hinge on the existence of exposed and unexposed groups within a population, allowing for the assessment of outcomes based on variation in exposure.

In the current era of AI proliferation, the formulation and implementation of AI policies serve as a distinct form of exposure. These policies, whether at local, state, or federal levels, or within large organizations, can be seen as interventions that will inevitably generate observable societal impacts.

However, harnessing the full potential of this moment requires more than just vigilant observation and data collection. It calls for a three-pronged approach that emphasizes collaboration across sectors, agreement on baselines, and data sharing across borders.

Collaboration across sectors: The NIH encourages partnerships and collaborations between researchers and the communities impacted by these policies. In the context of AI, this means creating synergies between tech companies, government agencies, non-governmental organizations, and the general public. These collaborations can foster a comprehensive understanding of policy impacts and contribute to more informed, balanced, and effective AI policies.

Agreement on baselines: Any meaningful study necessitates well-established baseline measurements. For AI policies, this could mean agreement on aspects like pre-policy AI adoption levels, societal awareness of AI, or economic indicators. By establishing a clear understanding of the societal context before policy implementation, researchers can more accurately assess the impacts and efficacy of AI policies.

Data sharing across borders: AI is a global phenomenon, and its impact is not limited by national boundaries. As such, understanding the effects of AI policies requires a global perspective. By encouraging data sharing across borders, we can promote a more holistic understanding of AI policy impacts. This not only aids in conducting more robust and comprehensive research but also contributes to a global body of knowledge that can inform future policy development worldwide.

In conclusion, as AI policies continue to evolve and shape our world, researchers are presented with a unique opportunity to study these changes as a natural experiment. It's a call to action for researchers to seize this moment, collaborate across sectors, agree on baselines, and promote data sharing.

This is not merely an academic exercise; the insights gained could inform future policy development, shaping the trajectory of AI's role in society for years to come.