A robust testing program is necessary for contain- ing the spread of COVID-19 infections before a vaccine becomes available. However, due to an acute shortage of testing kits (especially in low- resource developing countries), designing an opti- mal testing program/strategy is a challenging prob- lem to solve. Prior literature on testing strategies suffers from two major limitations: (i) it does not account for the trade-off between testing of symp- tomatic and asymptomatic individuals, and (ii) it primarily focuses on static testing strategies, which leads to significant shortcomings in the testing pro- gram’s effectiveness. In this paper, we introduced a scalable Monte Carlo tree search based algorithm named DOCTOR, and use it to generate the op- timal testing strategies for the COVID-19. In our experiment, DOCTOR’s strategies result in ∼40% fewer COVID-19 infections (over one month) as compared to state-of-the-art static baselines. Our work complements the growing body of research on COVID-19, and serves as a proof-of-concept that illustrates the benefit of having an AI-driven adaptive testing strategy for COVID-19.
Artificial Intelligence (AI), as a collection of tech- nologies, but more so as a growing component of the global mode of production, has a significant im- pact on gender, specifically gendered labour. In this position paper we argue that the dominant aspect of AI industry’s impact on gender is more that the pro- duction and reproduction of epistemic biases which is the focus of contemporary research but is rather a material impact. We draw attention to how as a part of a larger economic structure the AI industry is altering the nature of work, expanding platformi- sation, and thus increasing precarity which is push- ing women out of the labour force. We state that this is a neglected concern and specific challenge worthy of attention for the AI research community.
Many economists argue that a national carbon tax would be the most effective policy for incentivizing the development of low-carbon energy technologies. Yet existing models that measure the effects of a carbon tax only consider carbon taxes with fixed schedules. We propose a simple energy system transition model based on a finite-horizon Markov Decision Process (MDP) and use it to compare the carbon emissions reductions achieved by static versus adaptive carbon taxes. We find that in most cases, adaptive taxes achieve equivalent if not lower emissions trajectories while reducing the cost burden imposed by the carbon tax. However, the MDP optimization in our model adapted optimal policies to take advantage of the expected carbon tax adjustment, which sometimes resulted in the simulation missing its emissions targets.
Back to AI for Social Good event
Fairness, in machine learning research, is often conceived as an exercise in constrained optimization, based on a predefined fairness metric. We argue that this abstract model of algorithmic fairness is a poor match for the real-world, in which applications are likely to be embedded within a larger context involving multiple classes of stakeholders as well as multiple social and technical systems. We may expect multiple, competing claims around fairness coming from various stakeholders, especially in applications oriented towards social good. We propose that computational social choice is a promising framework for the integration of multiple perspectives on system outcomes in fairness- aware systems and provide an example case of personalized recommendation for a non-profit.
Back to AI for Social Good event