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

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

Cross Institutional Collaboration: HBCU\u27s Join Together to Further Their Information Literacy Goals

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A Power-threat View of The Role of Neighborhood Demographics on Airbnb Review Sentiments

Reviews in online sharing platforms hold value and drive sales. They also can include bias based on the review author\u27s racio-ethnicity. We set out to understand how racio-ethnicity bias impacts review sentiment. To do this, we draw on the power-threat view and propose two hypotheses. We find support for these hypotheses using archival data from 2,473 guest/host dyads and their associated Airbnb reviews. We find reviews with a more positive sentiment are left by guests from the national majority racio-ethnicity and hosted by a host from the same racio-ethnicity. This pattern is stronger in neighborhoods primarily inhabited by members of minority racio-ethnicities. This research contributes to the literature on online reviews and holds practical implications for digital platforms

Measuring Surface Chemical Properties of Soil Using Flow Calorimetry

Flow calorimetry, which is ideally suited for measuring reactions occurring at the liquid/solid interface, has been used to study the surface chemistry of many types of solids, but little use of it has been made in the study of surface reactions of soils. The purpose of this study was to demonstrate the application of flow calorimetry to the study of two fundamental soil chemical processes, namely cation exchange and phosphate sorption. Surface horizon samples of a Typic Acrorthox and a Typic Tropohumult from Puerto Rico, a strong acid cation exchange resin (Dowex 50W-8), and an amorphous Al(OH)3 were used. Heats for K/Ca exchange on the Dowex resin and the Oxisol, and K/Na exchange on the Ultisol, were consistent with literature values that were obtained using conventional batch calorimetry or derived from the temperature dependence of the exchange constant. Although peak areas associated with a given pair of exchange reactions were equal, peak shapes were generally not equivalent, indicating differences in the rate at which the two reactions occurred. For example, Ca displacing exchangeable K occurred more rapidly than the reverse reaction on the Dowex resin. The reaction of phosphate with the Ultisol and amorphous Al(OH)3 was exothermic. Exposure of the soil to several cycles of phosphate was sufficient to saturate the sorption sites, as evidenced by the loss of a detectable heat signal. However, phosphate reactive sites were regenerated by flushing the column with a salt solution at pH 10. Precipitation of Al-phosphate was shown to be endothermic, confirming that precipitation was not the primary mechanism for phosphate sorption in this study. The results of this study show that flow calorimetry can provide valuable information about surface chemical reactions in soils that cannot be obtained readily by other methods

Heats of K/Ca And K/Pb Exchange in Two Tropical Soils as Measured By Flow Calorimetry

Flow calorimetry can provide useful information about surface chemical reactions in soils that cannot be obtained readily by other methods. When flow calorimetry is conducted over a range of surface coverages, different sorption heats can be calculated to yield information about how binding energies vary with coverage, i.e., surface heterogeneity. The purpose of this study was to determine heats of exchange for K/Ca and K/Pb systems using flow calorimetry and to evaluate the degree of surface heterogeneity with respect to cation exchange. Surface horizon samples from a Typic Acrorthox and Typic Tropohumult from Puerto Rico were used. Lead was adsorbed specifically in both soils, but no adsorption heat was detected for this reaction in either soil. However, heats associated with reversible cation exchange between K and Pb were observed. Heats for K/Ca exchange were greater than those generated for K/Pb exchange in both soils. Heats of exchange were greater in the Ultisol than in the Oxisol. The differential heats of exchange were independent of exchange composition for both K/Pb and K/Ca exchange in the Oxisol, indicating that all cation exchange sites were similar energetically. In the Ultisol, the differential heats of exchange increased as exchangeable K decreased, indicating that the exchange sites were not similar energetically. These differences were attributed to the presence of smectite in the Ultisol, which was able, in part, to collapse when saturated with K

Uncoupling inequality: Reflections on the ethics of benchmarks for digital media

Our collaboration seeks to demonstrate shared interrogation by exploring the ethics of machine learning benchmarks from a socio-technical management perspective with insight from public health and ethnic studies. Benchmarks, such as ImageNet, are annotated open data sets for training algorithms. The COVID-19 pandemic reinforced the practical need for ethical information infrastructures to analyze digital and social media, especially related to medicine and race. Social media analysis that obscures Black teen mental health and ignores anti-Asian hate fails as information infrastructure. Despite inadequately handling non-dominant voices, machine learning benchmarks are the basis for analysis in operational systems. Turning to the management literature, we interrogate cross-cutting problems of benchmarks through the lens of coupling, or mutual interdependence between people, technologies, and environments. Uncoupling inequality from machine learning benchmarks may require conceptualizing the social dependencies that build structural barriers to inclusion

“SEE IT, NAME IT, DO IT” INSTRUCTIONAL COACHING MODEL’S INFLUENCE ON STUDENT ACHIEVEMENT AND TEACHER JOB SATISFACTION

Instructional coaching is a popular form of professional development for improving teacher instruction and student achievement. While there are numerous studies that have examined the impact of instructional coaching on student academic outcomes, very few studies have explored the influence of specific coaching models on student achievement. Another area of research that warrants investigation is the influence of instructional coaching on teacher job satisfaction. Work satisfaction for teachers is important for a variety of reasons. It is linked to improved instructional quality, better student academic outcomes, and higher teacher retention. While there are many research studies on factors that influence teacher job satisfaction (e.g., self-efficacy, work conditions, autonomy, principal support, etc.), there is currently only one study that has investigated the influence of instructional coaching on teacher job satisfaction. The purpose of this study was two-fold. First, the researcher examined the student achievement results for five North Carolina charter schools that implemented the “See it, Name it, Do it” (SND) instructional coaching model in order to evaluate the relationship between the SND coaching model and student achievement. Secondly, the researcher used a questionnaire and conducted interviews at one North Carolina charter school to explore the relationship between the SND coaching model and teacher job satisfaction. Results of the study suggest that the SND coaching model positively influences student academic outcomes and teacher job satisfaction. Four of the five schools analyzed had student achievement gains in math, reading, and science. Additionally, the study’s data indicate that a positive relationship exists between the SND coaching model and teacher job satisfaction, specifically teachers’ sense of self-efficacy, their feelings of being supported by their coach, and their perceptions of an improved work experience.Doctor of Educatio