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

To securely leverage the advantages of Cloud Computing, a lot of research has happened in the area of "Secure Query Processing over Encrypted Data". As a concrete use case, many encryption schemes have been proposed for securely processing k Nearest Neighbors (SkNN) over encrypted data in the outsourced setting. Recently Zhu et al.[1] proposed a SkNN solution which claimed to satisfy following four properties: (1)Data Privacy, (2)Key Confidentiality, (3)Query Privacy, and (4)Query Controllability. However, in this paper, we present an attack which breaks the Query Controllability claim of their scheme. Further, we propose a new SkNN solution which satisfies all the four existing properties along with an additional essential property of Query Check Verification. We analyze the security of our proposed scheme and present the detailed experimental results to showcase the efficiency in real world scenario.

Secure k-NN as a Service over Encrypted Data in Multi-User Setting

Methods, systems and computer program products for association rule mining of an encrypted database are provided herein. A computer-implemented method includes receiving, at a first cloud computing environment, encrypted transaction data that are encrypted using an encryption scheme which provides additive homomorphism, wherein the transaction data comprise a plurality of combinations of two or more elements of a set of elements, receiving, at the first cloud computing environment, encrypted query data that are encrypted using the encryption scheme, wherein the query data comprise at least one of an element and a combination of two or more elements of the set of elements which are the subject of a query seeking a determination of whether at least one of the element and the combination of two or more elements is frequent, and computing addition of the encrypted query data with the encrypted transaction data.

System and method for association rule mining from encrypted databases

Methods, systems, and computer program products for schema-free in-graph indexing are provided herein. A computer-implemented method includes creating multiple indexes directed to data within a knowledge graph; correlating two or more of the created indexes, thereby generating one or more multi-dimensional indexes; determining, based on a received query, one or more traversal paths within the data of the knowledge graph and the generated multi-dimensional indexes, wherein the traversal paths facilitate processing of the query; and outputting a response to the query based on the determined traversal paths.

Schema-free in-graph indexing

Methods, systems, and computer program products for updating annotator collections using run traces are provided herein. A computer-implemented method includes generating one or more alternate versions of one or more document annotators selected from a set of multiple document annotators; executing, on one or more document data sets, (i) one or more document annotators from the set of multiple document annotators and (ii) the one or more alternate versions to generate log information for each document annotator in the set and each alternate version of the one or more alternate versions; and outputting an instruction to modify, based on the generated log information for each document annotator in the set and each alternate version, at least one document annotator from the set with at least one alternate version from the one or more alternate versions.

Updating Annotator Collections Using Run Traces

Customer satisfaction (CSAT) is the key driver for retention and growth in retail banking and several techniques have been applied by banks to achieve this. For instance, banks in emerging markets with high footfall in branches have gone beyond the traditional approach of segmenting customers and services to optimizing the wait time for customers visiting the bank's branch. While this approach has significantly improved service quality, it has also added a new dimension in the service quality metric : pro-actively identify and address customer needs for (i) efficient banking experience and (ii) enhancing profit by selling additional services to existing customer. In this paper we present a system that addresses the challenge involved in providing better service to retail banking customer while ensuring that a larger share of customer's wallet comes to the branch. We do this by combining predictive analytics, scheduling and process optimization techniques.

Sameep Mehta

Papers

Secure k-NN as a Service over Encrypted Data in Multi-User Setting

System and method for association rule mining from encrypted databases

Schema-free in-graph indexing

Updating Annotator Collections Using Run Traces

Simultaneously improving CSAT and profit in a retail banking organization