The Innovation Winter Is Coming: How the U.S.-China Trade War Endangers the World

Massive amounts of data, increased computing power, and advances in technology have created the recent AI Spring. Some feel the continuation of this period of innovation in artificial intelligence is inevitable, but its future is in jeopardy due to the recent trade war between the United States and China. Although the United States spearheaded the globalization movement after WWII, it has shifted to a policy of protectionism and rejectionism. China, conversely, has begun to fill the gap that the United States has left in its wake with its withdrawal from multilateral trade agreements, rejection of the World Trade Organization, and retreat from free trade principles. The future of AI, especially the Internet of Things (IoT), rests on the availability of a massive communication infrastructure that 5G can provide. Although the United States was the undisputed leader in 4G technology, China is the primary supplier of 5G networking equipment and, through its Belt and Road Initiative, seeks to spread its 5G technology throughout the world. Additionally, China has created a long-term strategic plan for AI providing billions for tech start-ups—locally and abroad—promoting collaboration and research, investing in educational programs, and designing technical standards, as well as supporting the needed 5G infrastructure. Conversely, the U.S. government relies on private industry to move this field forward. The U.S.-instigated trade war with China appears to be an attempt to thwart China’s progress. This trade war not only threatens the global economy and endangers democracy, it will likely cause an Innovation Winter—hindering future developments in AI. There is a very real danger that should the United States and China continue with this decoupling, the result could be a bifurcated internet, the development of technology on two divergent tracks, and a 5G infrastructure with non-interchangeable components requiring the rest of the world to choose a side

Representation of veterans who have been homeless

Professional project report submitted in partial fulfillment of the requirements for the degree of Masters of Arts in Journalism from the School of Journalism, University of Missouri--Columbia

High Stakes Behavior with Low Payoffs: Inducing Preferences with Holt-Laury Gambles

A continuing goal of experiments is to understand risky decisions when the decisions are important. Often a decision’s importance is related to the magnitude of the associated monetary stake. Khaneman and Tversky (1979) argue that risky decisions in high stakes environments can be informed using questionnaires with hypothetical choices (since subjects have no incentive to answer questions falsely.) However, results reported by Holt and Laury (2002, henceforth HL), as well as replications by Harrison (2005) suggest that decisions in “high” monetary payoff environments are not well-predicted by questionnaire responses. Thus, a potential implication of the HL results is that studying decisions in high stakes environments requires using high stakes. Here we describe and implement a procedure for studying high-stakes behavior in a low-stakes environment. We use the binary-lottery reward technique (introduced by Berg, et al (1986)) to induce preferences in a way that is consistent with the decisions reported by HL under a variety of stake sizes. The resulting decisions, all of which were made in a low-stakes environment, reflect surprisingly well the noisy choice behavior reported by HL’s subjects even in their highstakes environment. This finding is important because inducing preferences evidently requires substantially less cost than paying people to participate in extremely high-stakes games.

The Use of Big Data Analytics by the IRS: Efficient Solutions or the End of Privacy as We Know It?

This Article examines the privacy issues resulting from the IRS\u27s big data analytics program as well as the potential violations of federal law. Although historically, the IRS chose tax returns to audit based on internal mathematical mistakes or mismatches with third party reports (such as W-2s), the IRS is now engaging in data mining of public and commercial data pools (including social media) and creating highly detailed profiles of taxpayers upon which to run data analytics. This Article argues that current IRS practices, mostly unknown to the general public are violating fair information practices. This lack of transparency and accountability not only violates federal law regarding the government\u27s data collection activities and use of predictive algorithms, but may also result in discrimination. While the potential efficiencies that big data analytics provides may appear to be a panacea for the IRS\u27s budget woes, unchecked, these activities are a significant threat to privacy. Other concerns regarding the IRS\u27s entrie into big data are raised including the potential for political targeting, data breaches, and the misuse of such information. This Article intends to bring attention to these privacy concerns and contribute to the academic and policy discussions about the risks presented by the IRS\u27s data collection, mining and analytics activities

The Data Trust Solution to Data Sharing Problems

A small number of large companies hold most of the world’s data. Once in the hands of these companies, data subjects have little control over the use and sharing of their data. Additionally, this data is not generally available to small and medium enterprises or organizations who seek to use it for social good. A number of solutions have been proposed to limit Big Tech “power,” including antitrust actions and stricter privacy laws, but these measures are not likely to address both the oversharing and under-sharing of personal data. Although the data trust concept is being actively explored in the United Kingdom, European Union, and Canada, this is the first Article to take an in-depth look at the viability of data trusts from a US perspective. A data trust is a governance device that places an independent fiduciary intermediary between Big Tech and human data subjects. This Article explores how data trusts might be configured as bundles of contracts in the information supply chain. In addition to their benefits for the social good, data trusts might contribute to relieve some of the tension between EU and US privacy practices

Disrupting Venture Capital: Carrots, Sticks, and Artificial Intelligence

Despite the massive dollars invested each year by Venture Capital (VC) firms, more than two-thirds of the companies they fund will provide zero return. More problematic, less than 3% of VC funds go to female-led startup teams, and less than 1% to racially diverse founders. While many argue that this underrepresentation will work itself out over time, in reality, these numbers have remained stagnant for over 30 years. This is especially perverse given that diverse startups, when funded, appreciably outperform male-only founding teams. The VC industry operates under an antiquated model of investing in founders with demographics reflecting those of VC partners (white men control 93% of VC funds, and only 0.2% of VC partners are Black or Latina women). While antidiscrimination law intended to create a level playing field for all, the VC field operates outside this regulatory scheme. In addition to its lack of diversity, ironically it also has a technology problem. Despite the incredible advances in artificial intelligence (AI), and the industry’s focus on tech startups, many VC firms fail to incorporate data analytics and machine learning to guide their decision- making, relying instead on “gut instinct.” This is the first article to comprehensively explore the current state of the VC industry through the lens of behavioral law and economic theory, revealing the field's intransigence and the heuristics and biases infecting its decision-making. Using insights gained from this analysis, this Article suggests that disruption is possible through a combination of policy and legal initiatives as well as leveraging advances in technology. The Article concludes by offering a novel multipronged solution comprised of a combination of carrots (incentives), sticks (penalties), and AI to motivate behavioral change within the VC industry and stimulate a true meritocracy where gender and racially diverse startups are equitably funded, and innovation flourishes

GDPR: The End of Google and Facebook or a New Paradigm in Data Privacy?

EU Data Protection Agencies have been vigorously enforcing violations of regional and national data protection law in recent years against U.S. tech companies, but few changes have been made to their business model of exchanging free services for personal data. With the Cambridge Analytica debacle revealing how insufficient American privacy law is, we now find ourselves questioning whether the General Data Protection Regulation (GDPR) is not the onerous 99 article regulation to be feared, but rather a creation years ahead of its time. This paper will explain how the differences in U.S. and EU privacy and data protection law and ideology have led to a wide divergence in enforcement actions and what U.S. companies will need to do in order legally process the data of their users in the EU. The failure of U.S. tech companies to fulfill the requirements of the GDPR, which has extraterritorial application and becomes applicable on May 25, 2018, could result in massive fines (up to $4 billion using the example of Google). The GDPR will mandate a completely new business model for these U.S. tech companies that have been operating for well over a decade with very loose restrictions under U.S. law. Will the GDPR be the end of Google and Facebook or will it be embraced as the gold standard of how companies ought to operate