@article {2018,
	title = {Computational power and the social impact of artificial intelligence},
	journal = {SSRN Electronic Journal},
	year = {2018},
	abstract = {Machine learning is computational process. To that end, it is inextricably tied to computational power - the tangible material of chips and semiconductors that the algorithms of machine intelligence operate on. Most obviously, computational power and computing architectures shape the speed of training and inference in machine learning, and therefore influence the rate of progress in the technology. But, these relationships are more nuanced than that: hardware shapes the methods used by researchers and engineers in the design and development of machine learning models. Characteristics such as the power consumption of chips also define where and how machine learning can be used in the real world.
In a broader perspective, computational power is also important because of its specific geographies. Semiconductors are designed, fabricated, and deployed through a complex international supply chain. Market structure and competition among companies in this space influence the progress of machine learning. Moreover, since these supply chains are also considered significant from a national security perspective, hardware becomes an arena in which government industrial and trade policy has a direct impact on the fundamental machinery necessary for artificial intelligence (AI).
This paper aims to dig more deeply into the relationship between computational power and the development of machine learning. Specifically, it examines how changes in computing architectures, machine learning methodologies, and supply chains might influence the future of AI. In doing so, it seeks to trace a set of specific relationship between this underlying hardware layer and the broader social impacts and risks around AI. On the hand, this examination shines a spotlight on how hardware works to exacerbate a range of concerns around ubiquitous surveillance, technological unemployment, and geopolitical conflict. On the other, it also highlights the potentially significant role that shaping the development of computing power might play in addressing these concerns.},
	doi = {10.2139/ssrn.3147971},
	author = {Tim Hwang}
}
@article {2016,
	title = {Regional Diversity in Autonomy and Work: A Case Study from Uber and Lyft Drivers},
	year = {2016},
	chapter = {Data And Society},
	url = {https://datasociety.net/pubs/ia/Rosenblat-Hwang_Regional_Diversity-10-13.pdf},
	author = {Alex Rosenblat and Tim Hwang}
}
