Digital Access, Political Networks and the Diffusion of Democracy

We examine the effects of digital access on the prevalence of democracy and its diffusion via geographical and trade networks across 152 countries between 2000 and 2008. Although civil liberties and media freedom show a consistently positive relationship with different forms of digital access, our dynamic models that allow co-evolution of digital access, democracy and trade tie formation suggest that high mobile penetration has a more significant impact on civil liberties than Internet access does, and may also increase a country's "susceptibility" to democratic changes in neighboring nations. We explore possible drivers of these empirical findings, discussing some social and political implications.NYU Stern School of Busines

The Trade-Offs in Promoting Equity-Focused Initiatives in Crowdfunding

Organizations interested in supporting matters of diversity and equity need guidance on whether to be transparent about their equity-focused initiatives and appeal to users’ sense of social justice. This paper explores how information transparency affects backer responses to equity-related initiatives. The educational crowdfunding platform DonorsChoose launched an “equity-focused” initiative to highlight the platform’s commitment to equity in education and encourage donations towards that cause. Using an observational analysis and an experimental study, I found evidence that information transparency, i.e., explicitly promoting equity-focused projects, dampens overall donations because any change in donations associated with the equity-focused initiatives is insufficient to offset the decrease in other donations. However, I found evidence that distant donors are more responsive to equity-focused recommendations, consistent with the hypothesis that a holistic mindset moderates the effectiveness of promoting equity-focused initiatives. This study provides insight into the trade-offs associated with information transparency for equity-related initiatives and the complicated relationship between organizations and equity-related initiatives

Beauty’s in the AI of the Beholder: How AI Anchors Subjective and Objective Predictions

Researchers increasingly acknowledge that algorithms can exhibit bias, but artificial intelligence (AI) is increasingly integrated into the organizational decision-making process. How does biased AI shape human choices? We consider a sequential AI-human decision that mirrors organizational decisions; an automated system provides a score and then a human decides a score using their discretion. We conduct an AMT survey and ask participants to assign one of two types of scores: a subjective, context-dependent measure (Beauty) and objective, observer-independent measure (Age). Participants are either shown the AI score, shown the AI score and its error, or not shown the AI score. We find that participants without knowledge of the AI score do not exhibit bias; however, knowing the AI scores for the subjective measure induces bias in the participants’ scores due to the anchoring effect. Although participants’ scores do not display bias, participants who receive information about the AI error rates devalue the AI score and reduce their error. This study makes several contributions to the information systems literature. First, this paper provides a novel way to discuss artificial intelligence bias by distinguishing between subjective and objective measures. Second, this paper highlights the potential spillover effects from algorithmic bias into human decisions. If biased artificial intelligence anchors human decisions, then it can induce bias into previously unbiased scores. Third, we examine a method to encourage participants to reduce their reliance on the artificial intelligence, reporting the error rate, and find evidence that it is effective for the objective measure

THE INFORMATION CONTENT OF ECONOMIC NETWORKS: EVIDENCE FROM ONLINE CHARITABLE GIVING

We measure the “information content” of online economic networks – sets of connected entities where links are created by realizations of shared prior outcomes. We conjecture that such electronic networks contain information about similarity in latent preferences across actors that are not captured by observable product or consumer features. We provide a methodology for measuring this information content in a rigorous and outcome-driven manner using matchedsample estimation techniques to mimic the optimal use of all observable non-network data. Using detailed transaction-level data about 257,851 contributions to 95,684 charitable projects by 99,720 donors on an leading online giving web site, we show that co-donors in an economic network have an 80-fold higher overlap in future choice than a random benchmark, the network outperforms even matched sample alternatives based on sophisticated feature-based predictive models 5-fold to 23-fold, and this inferred overlap in latent preferences persists with local network traversal

Tracing the Invisible: Information Fiduciaries and the Pandemic

Predictive data technology designed to contain the COVID-19 pandemic was not as successful as promised. Data-centric solutions to providing testing and tracing did little to limit the virus’s spread in part because they served only the most visible parts of society. This Article argues for more robust solutions to protect individuals’ privacy—whether those individuals are currently visible or invisible to pandemic technology—if pandemic technology is to provide the universal coverage necessary for a public health emergency, such as the COVID- 19 pandemic. First, we contend that current pandemic data technology operates under rigid technical and social assumptions that thwart participation from all population groups. Second, we demonstrate that the organizations associated with pandemic data technology have financial incentives that could be in opposition to protecting anyone susceptible to the virus. Third, we consider how the need for someone to protect data to allow for medically necessary access to data could be an onramp for a pilot implementation of legal theory on information fiduciaries. Finally, we offer two tangible policy suggestions: conflict-of-interest notices released as open data and a public health fiduciary that has legal responsibility to protect data relevant to epidemiological outbreaks. A public health fiduciary working in the public interest would be more likely to gather sufficiently accurate data than would a fiduciary working within the organizations collecting data themselves. Technology has a vital role to play in managing the pandemic, but in the hands of some organizations, it may encourage behavior that counters public health goals. Trusted data technology solutions in conjunction with predictive epidemiology models could contribute to reducing the spread of the virus more holistically and with fewer privacy-related consequences