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

Mining evolving data streams for concept drifts has gained importance in applications like customer behavior analysis, network intrusion detection, credit card fraud detection. Several approaches have been proposed for detection of concept drifts in the context of supervised learning in data streams. Recently, researchers have been looking into the problem of identifying concept drifts in unlabeled data streams. Prevalent approaches study the evolution of streaming clusters using intrinsic and extrinsic characteristics of the discovered clusters, where each cluster is considered a concept. In this paper we model an unlabeled, uniform data stream as a stochastic poisson process and study the arrival pattern of data points to analyse the nature of an evolving concept (cluster). Each concept is modeled as stochastic poisson process and is individually observed for arrival rates of the incoming data points. A random sample of arrival rates is collected for each concept and appropriate non-parametric tests are applied to infer the nature of evolution for the concept. Concept drift in the stream can be inferred by the overall behavior of the concepts. We also propose a taxonomy of various types of concept behaviors and inter-relation among them. Experiments have been performed to demonstrate feasibility, validity and scalability of the proposed method.

https://www.cse.iitb.ac.in/~comad/2009/proceedings/R2_1.pdf

Categorizing Concepts for Detecting Drifts in Stream

Data today fuels both the economy and advances in machine learning and AI. All aspects of decision making, at the personal and enterprise level and in governments are increasingly data-driven. In this context, however, there are still some fundamental questions that remain unanswered with respect to data. \textit{What is meant by data value? How can it be quantified, in a general sense?}. The "value" of data is not understood quantitatively until it is used in an application and output is evaluated, and hence currently it is not possible to assess the value of large amounts of data that companies hold, categorically. Further, there is overall consensus that good data is important for any analysis but there is no independent definition of what constitutes good data. 
In our paper we try to address these gaps in the valuation of data and present a framework for users who wish to assess the value of data in a categorical manner. Our approach is to view the data as composed of various attributes or characteristics, which we refer to as facets, and which in turn comprise many sub-facets. We define the notion of values that each sub-facet may take, and provide a seed scoring mechanism for the different values. The person assessing the data is required to fill in the values of the various sub-facets that are relevant for the data set under consideration, through a questionnaire that attempts to list them exhaustively. Based on the scores assigned for each set of values, the data set can now be quantified in terms of its properties. This provides a basis for the comparison of the relative merits of two or more data sets in a structured manner, independent of context. The presence of context adds additional information that improves the quantification of the data value.

What is my data worth? From data properties to data value.

Vast availability of text data has enabled widespread training and use of AI systems that not only learn and predict attributes from the text but also generate text automatically. However, these AI models also learn gender, racial and ethnic biases present in the training data. In this paper, we present the first system that discovers the possibility that a given text portrays a gender stereotype associated with an occupation. If the possibility exists, the system offers counter-evidences of opposite gender also being associated with the same occupation in the context of user-provided geography and timespan. The system thus enables text de-biasing by assisting a human-in-the-loop. The system can not only act as a text pre-processor before training any AI model but also help human story writers write stories free of occupation-level gender bias in the geographical and temporal context of their choice.

Generating Clues for Gender based Occupation De-biasing in Text.

In this paper, we look at how we can leverage Spark platform for efficiently processing fine-grained provenance queries on large volumes of workflow provenance data. Simple recursive querying based Spark solutions involve large data scanning cost and hence do not work well. We propose a novel provenance framework which is engineered to quickly determine a small volume of data containing the entire lineage of the queried data-item. This small volume of data is then recursively processed to figure out the provenance of the queried data-item. We study the effectiveness of the proposed framework on a provenance trace obtained from a financial domain text curation workflow and report our observations. We show that the proposed framework easily outperforms the naive approaches.

On Efficiently Processing Workflow Provenance Queries in Spark

A method, system and computer program product are disclosed for candidate screening. In one embodiment, the method comprises identifying a multitude of dimensions of a specified job, assigning a weight to each of the dimensions, and determining whether each of a group of candidate satisfies the weights assigned to the dimensions. In an embodiment, a score is assigned to each candidate based on the weights assigned to the dimensions, and the number of the candidates score above a threshold is determined. In an embodiment, if the number of the candidates that have a score above the threshold is less than a defined number, one or more of the weights is adjusted, and the candidates are rescored based on these adjusted weights. In embodiments of the invention, the weights are adjusted based on the number of the candidates that score above the threshold, or based on the other weights.

Sameep Mehta

Papers

Categorizing Concepts for Detecting Drifts in Stream

What is my data worth? From data properties to data value.

Generating Clues for Gender based Occupation De-biasing in Text.

On Efficiently Processing Workflow Provenance Queries in Spark

Multifaceted candidate screening