Prof. Dr.
Austin Saragih

Municipal Solid Waste (MSW) recycling is a pivotal pillar in waste reduction and sustainable resource management. However, the United States (U.S.) is behind its schedule for the national recycling rate goal of 50% by 2030, which California is almost achieving. To this end, we review and compare the national and California recycling policies. Our analysis reveals that while there have been some positive changes toward sustainable waste management practices, significant gaps remain, particularly in the lack of a comprehensive national recycling law. The current federal allocation of funds has also been inadequate for achieving the national goal. Therefore, we identify policy options to grow toward a national policy and funding for recycling infrastructure and education. Using California as a model, we provide federal law options to balance recyclable supply and demand. These options are paired with future U.S. MSW policy and research directions.

As algorithms increasingly aid public sector decision making in the United States, it becomes important to understand how to effectively tackle algorithmic bias in systems that local, state, and federal government entities use and procure, including what kinds of policies are currently in place or proposed. There is a prevalent belief that algorithmic bias arises primarily from statistical biases present in the data used to train or develop the algorithm, but bias may also arise during data collection, problem specification, how and where algorithms are deployed, and within the broader societal contextualization of algorithms. So far, enacted policies in in the US center on temporary bans of particular types of algorithms, transparency, and post-hoc bias audits, as well as more wide-ranging (but non-binding) policy frameworks; all largely focus on quantitative notions of fairness when they assess bias, leaving room for more comprehensive legislation to meaningfully address this issue going forward.