Questions on whether governments have acted promptly enough, and whether lockdown measures can be lifted soon, have since been central in public discourse. Data-driven models that predict COVID-19 fatalities under different lockdown policy scenarios are essential for addressing these questions and informing governments on future policy directions.

Fairness-aware learning studies the development of algorithms that avoid discriminatory decision outcomes despite biased training data. While most studies have concentrated on immediate bias in static contexts, this paper highlights the importance of investigating long-term fairness in dynamic decision-making systems while simultaneously considering instantaneous fairness requirements. In the context of reinforcement learning, we propose a general framework where long-term fairness is measured by the difference in the average expected qualification gain that individuals from different groups could obtain.Then, through a causal lens, we decompose this metric into three components that represent the direct impact, the delayed impact, as well as the spurious effect the policy has on the qualification gain. We analyze the intrinsic connection between these components and an emerging fairness notion called benefit fairness that aims to control the equity of outcomes in decision-making. Finally, we develop a simple yet effective approach for balancing various fairness notions.

We propose a new unified framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference describes a large literature on treatment spillovers, social interactions, social learning, information diffusion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other agents and connections nearby as measured by path distance. The impact of a policy or treatment assignment is then learned by pooling outcome data across similarly configured agents. In the paper, we propose a new nonparametric modeling approach and consider two applications to causal inference. The first application is to testing policy irrelevance/no treatment effects. The second application is to estimating policy effects/treatment response. We conclude by evaluating the finite-sample properties of our estimation and inference procedures via simulation.

With the recent progress in artificial intelligence (AI) algorithms, dramatic increase in computational capacities, and availability of big data necessary for training deep neural networks, a lot of AI applications became available at the market and automation tendencies started to penetrate all spheres of human activities and all industries. While the topic of AI has been getting a lot of media coverage and public attention, profound research on its socio-economic and policy effects, especially with regard to entrepreneurship, has yet to be developed. Moreover, methodological papers in artificial intelligence field have been mainly published in very technical venues and it is difficult for a broader publics to grasp the most recent developments in this area. Therefore, the purpose of this special issue is to address these shortcomings. This special issue is the first initiative to interact the technical and methodological papers in AI with papers exploring socio-economic, entrepreneurship and policy effects of AI.