Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals

Recent studies have shown that information disclosed on social network sites (such as Facebook) can be used to predict personal characteristics with surprisingly high accuracy. In this paper we examine a method to give online users transparency into why certain inferences are made about them by statistical models, and control to inhibit those inferences by hiding ("cloaking") certain personal information from inference. We use this method to examine whether such transparency and control would be a reasonable goal by assessing how difficult it would be for users to actually inhibit inferences. Applying the method to data from a large collection of real users on Facebook, we show that a user must cloak only a small portion of her Facebook Likes in order to inhibit inferences about their personal characteristics. However, we also show that in response a firm could change its modeling of users to make cloaking more difficult.Comment: presented at 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), New York, N

Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals

Recent studies show the remarkable power of information disclosed by users on social network sites to infer the users' personal characteristics via predictive modeling. In response, attention is turning increasingly to the transparency that sites provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. We draw on the evidence counterfactual as a means for providing transparency into why particular inferences are drawn about them. We then introduce the idea of a \cloaking device" as a vehicle to provide (and to study) control. Specifically, the cloaking device provides a mechanism for users to inhibit the use of particular pieces of information in inference; combined with the transparency provided by the evidence counterfactual a user can control model-driven inferences, while minimizing the amount of disruption to her normal activity. Using these analytical tools we ask two main questions: (1) How much information must users cloak in order to significantly affect inferences about their personal traits? We find that usually a user must cloak only a small portion of her actions in order to inhibit inference. We also find that, encouragingly, false positive inferences are significantly easier to cloak than true positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. In our main results we replicate the methodology of Kosinski et al. (2013) for modeling personal traits; then we demonstrate a simple modeling change that still gives accurate inferences of personal traits, but requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make modeling choices to make control easier or harder for their users.Columbia University, New York University, NYU Stern School of Business, NYU Center for Data Scienc

Crash Risk in Currency Markets

How much of carry trade excess returns can be explained by the presence of disaster risk? To answer this question, we propose a simple structural model which includes both Gaussian and disaster risk premia and can be estimated even in samples that do not contain disasters. The model points to a novel estimation procedure based on currency options with potentially different strikes. We implement this procedure on a large set of countries over the 1996-2008 period, forming portfolios of hedged and unhedged carry trade excess returns by sorting currencies on their forward discounts. We find that disaster risk premia account for about 25% of carry trade excess returns in advanced countries

Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals

Abstract Recent studies show the remarkable power of information disclosed by users on social network sites to infer the users' personal characteristics via predictive modeling. In response, attention is turning increasingly to the transparency that sites provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. We draw on the evidence counterfactual as a means for providing transparency into why particular inferences are drawn about them. We then introduce the idea of a "cloaking device" as a vehicle to provide (and to study) control. Specifically, the cloaking device provides a mechanism for users to inhibit the use of particular pieces of information in inference; combined with the transparency provided by the evidence counterfactual a user can control model-driven inferences, while minimizing the amount of disruption to her normal activity. Using these analytical tools we ask two main questions: (1) How much information must users cloak in order to significantly affect inferences about their personal traits? We find that usually a user must cloak only a small portion of her actions in order to inhibit inference. We also find that, encouragingly, false positive inferences are significantly easier to cloak than true positive inferences. gives accurate inferences of personal traits, but requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make modeling choices to make control easier or harder for their users

Crash Risk in Currency Markets

How much of carry trade excess returns can be explained by the presence of disaster risk? To answer this question, we propose a simple structural model which includes both Gaussian and disaster risk premia and can be estimated even in samples that do not contain disasters. The model points to a novel estimation procedure based on currency options with potentially different strikes. We implement this procedure on a large set of countries over the 1996-2008 period, forming portfolios of hedged and unhedged carry trade excess returns by sorting currencies on their forward discounts. We find that disaster risk premia account for about 25% of carry trade excess returns in advanced countries

Crash risk in currency markets

Abstract Since the Fall of 2008, out-of-the money puts on high interest rate currencies have become significantly more expensive than out-of-the-money calls, suggesting a large crash risk of those currencies. To evaluate crash risk precisely, we propose a parsimonious structural model that includes both Gaussian and disaster risks and can be estimated even in samples that do not contain disasters. Estimating the model for the 1996 to 2014 sample period using monthly exchange rate spot, forward, and option data, we obtain a real-time index of the compensation for global disaster risk exposure. We find that disaster risk accounts for more than a third of the carry trade risk premium in advanced countries over the period examined. The measure of disaster risk that we uncover in currencies proves to be an important factor in the cross-sectional and time-series variation of exchange rates, interest rates, and equity tail risk

Quantifying and predicting success in show business

Recent studies in the science of success have shown that the highest-impact works of scientists or artists happen randomly and uniformly over the individual's career. Yet in certain artistic endeavours, such as acting in films and TV, having a job is perhaps the most important achievement: success is simply making a living. By analysing a large online database of information related to films and television we are able to study the success of those working in the entertainment industry. We first support our initial claim, finding that two in three actors are "one-hit wonders". In addition we find that, in agreement with previous works, activity is clustered in hot streaks, and the percentage of careers where individuals are active is unpredictable. However, we also discover that productivity in show business has a range of distinctive features, which are predictable. We unveil the presence of a rich-get-richer mechanism underlying the assignment of jobs, with a Zipf law emerging for total productivity. We find that productivity tends to be highest at the beginning of a career and that the location of the "annus mirabilis" -- the most productive year of an actor -- can indeed be predicted. Based on these stylized signatures we then develop a machine learning method which predicts, with up to 85% accuracy, whether the annus mirabilis of an actor has yet passed or if better days are still to come. Finally, our analysis is performed on both actors and actresses separately, and we reveal measurable and statistically significant differences between these two groups across different metrics, thereby providing compelling evidence of gender bias in show business.Comment: 6 Figure