We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.

The spread of true and false news online

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

The paper “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification” by Joy Buolamwini and Timnit Gebru, that will be presented at the Conference on Fairness, Accountability, and Transparency (FAT*) in February 2018, evaluates three commercial API-based classifiers of gender from facial images, including IBM Watson Visual Recognition. The study finds these services to have recognition capabilities that are not balanced over genders and skin tones [1]. In particular, the authors show that the highest error involves images of dark-skinned women, while the most accurate result is for light-skinned men.

/pdf/gender-shades-intersectional-accuracy-disparities-in-4qgeu0c1i3.pdf

Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems. Such systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that the decisions do not reflect discriminatory behavior toward certain groups or populations. We have recently seen work in machine learning, natural language processing, and deep learning that addresses such challenges in different subdomains. With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them. In this survey we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined in order to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and how they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

A Survey on Bias and Fairness in Machine Learning

Numerical simulations are replacing traditional experiments in gaining insights into complex physical phenomena. Given recent advances in computer hardware and numerical methods, it is now possible to simulate physical phenomena at very fine temporal and spatial resolutions. Analyzing the datasets produced by such simulations is extremely challenging, given the enormous sizes of the datasets involved, and the fact that often the information that is to be gleaned is like searching for a needle in a haystack. In order to make efficient progress, analyzing such data must advance from current techniques that visualize static images of the data, to the automated mining, identification and subsequent visualization of the important features in the data, a challenging task. We report progress on a unified framework that addresses this critical challenge for two science drivers. In both our science drivers – solid and fluid systems – there are hidden hierarchies of features and the components that characterize them (e.g. shapes). Here, we offer a systematic way to detect such features, characterize and track them, and formulate hypotheses about their evolution – an important step in extracting vital information from such complex systems. The search is made more difficult due to the multiscale nature of the phenomenon being studied. Some features persist for awhile and manifest in global macroscopic effects. Other short-lived transients arise from the occurrence of certain precursor events that are important for understanding the evolutionary behavior of features.

Mining of Complex Evolutionary Phenomena

This paper presents a case study in creating a parallel and scalable implementation of a scientific data analysis application. We focus on a defect detection and categorization application which analyzes datasets produced by molecular dynamics (MD) simulations. In parallelizing this application, we had the following three goals. First, we obviously wanted to achieve high parallel efficiency. Second, we wanted to create an implementation that can scale to disk-resident datasets. Third, we wanted to create an easy to maintain and modify implementation, which is possible only through using high-level interfaces. We used a number of techniques for organizing the input data, achieving load balance, and efficiently parallelizing the step for updating and matching with the defect catalog. To meet our third goal, we used a system called FREERIDE (FRamework for Rapid Implementation of Datamining Engines), which was originally developed for parallelizing data mining algorithms. We have carried out a detailed evaluation of our implementation. The main observations from our experiments are as follows: 1) our implementation achieves high parallel efficiency, 2) the execution time remains proportional to the amount of computation even as the dataset becomes disk-resident, and 3) our scheme for load balancing and the method we use for parallelizing updating and matching of the defect catalog are crucial for parallel efficiency of the defect categorization phase.

Parallelizing a defect detection and categorization application

The presence of gender stereotypes in many aspects of society is a well-known phenomenon. In this paper, we focus on studying such stereotypes and bias in Hindi movie industry (Bollywood). We analyze movie plots and posters for all movies released since 1970. The gender bias is detected by semantic modeling of plots at inter-sentence and intra-sentence level. Different features like occupation, introduction of cast in text, associated actions and descriptions are captured to show the pervasiveness of gender bias and stereo- type in movies. We derive a semantic graph and compute centrality of each character and observe similar bias there. We also show that such bias is not applicable for movie posters where females get equal importance even though their character has little or no impact on the movie plot. Furthermore, we explore the movie trailers to estimate on-screen time for males and females and also study the portrayal of emotions by gender in them. The silver lining is that our system was able to identify 30 movies over last 3 years where such stereotypes were broken.

Analyzing Gender Stereotyping in Bollywood Movies

Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have been introduced to facilitate or complement physical contact tracing. However, there are a number of privacy issues in the implementation of contract tracing applications, which make people reluctant to install or update their infection status on these applications. In this concept paper, we present ideas from Graph Neural Networks and explainability, that could improve trust in these applications, and encourage adoption by people.

Explainable Link Prediction for Privacy-Preserving Contact Tracing.

We consider the problem of linking web search queries to entities from a knowledge base such as Wikipedia. Such linking enables converting a user’s web search session to a footprint in the knowledge base that could be used to enrich the user profile. Traditional methods for entity linking have been directed towards finding entity mentions in text documents such as news reports, each of which are possibly linked to multiple entities enabling the usage of measures like entity set coherence. Since web search queries are very small text fragments, such criteria that rely on existence of a multitude of mentions do not work too well on them. We propose a three-phase method for linking web search queries to wikipedia entities. The first phase does IR-style scoring of entities against the search query to narrow down to a subset of entities that are expanded using hyperlink information in the second phase to a larger set. Lastly, we use a graph traversal approach to identify the top entities to link the query to. Through an empirical evaluation on real-world web search queries, we illustrate that our methods significantly enhance the linking accuracy over state-of-the-art methods.

Sameep Mehta

Papers

Mining of Complex Evolutionary Phenomena

Parallelizing a defect detection and categorization application

Analyzing Gender Stereotyping in Bollywood Movies

Explainable Link Prediction for Privacy-Preserving Contact Tracing.

Entity Linking for Web Search Queries