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

In past few years, several data-sets have been released for text and images. We present an approach to create the data-set for use in detecting and removing gender bias from text. We also include a set of challenges we have faced while creating this corpora. In this work, we have worked with movie data from Wikipedia plots and movie trailers from YouTube. Our Bollywood Movie corpus contains 4000 movies extracted from Wikipedia and 880 trailers extracted from YouTube which were released from 1970-2017. The corpus contains csv files with the following data about each movie - Wikipedia title of movie, cast, plot text, co-referenced plot text, soundtrack information, link to movie poster, caption of movie poster, number of males in poster, number of females in poster. In addition to that, corresponding to each cast member the following data is available - cast name, cast gender, cast verbs, cast adjectives, cast relations, cast centrality, cast mentions. We present some preliminary results on the task of bias removal which suggest that the data-set is quite useful for performing such tasks.

Bollywood Movie Corpus for Text, Images and Videos

A method includes determining, by a controller, a portion of data that is selected by a user. The portion of data includes source data that is to be transformed by at least one shaping function. The method also includes generating, by the controller, a first output recommendation data that communicates at least one recommended shaping function to apply to the portion of data. The first output recommendation data is generated based on patterns of shaping functions that have been previously chosen. The patterns of shaping functions that have been previously chosen can be chosen by a plurality of system users. The method also includes determining whether to apply the at least one recommended shaping function to the portion of data. The method also includes applying the at least one recommended shaping function based on the determining.

Generating a recommended shaping function to integrate data within a data repository

We propose a method to control the attributes of Language Models (LMs) for the text generation task using Causal Average Treatment Effect (ATE) scores and counterfactual augmentation. We explore this method, in the context of LM detoxification, and propose the Causally Fair Language (CFL) architecture for detoxifying pre-trained LMs in a plug-and-play manner. Our architecture is based on a Structural Causal Model (SCM) that is mathematically transparent and computationally efficient as compared with many existing detoxification techniques. We also propose several new metrics that aim to better understand the behaviour of LMs in the context of toxic text generation. Further, we achieve state of the art performance for toxic degeneration, which are computed using \RTP (RTP) benchmark. Our experiments show that CFL achieves such a detoxification without much impact on the model perplexity. We also show that CFL mitigates the unintended bias problem through experiments on the BOLD dataset.

CFL: Causally Fair Language Models Through Token-level Attribute Controlled Generation

Data discovery is a multi-dimensional field encompassing information extraction, information retrieval, exploratory data analysis, visualization and recommendations among other things. Data Marketplaces are platforms where users discover and shop for data products. These products themselves are produced by modern data stacks governed by frameworks like Data Fabric. Knowledge Graphs and semantic technologies already form a core part of Data Fabric and hence could be leveraged for data discovery. In this tutorial, we’ll present state of the art semantic technologies that enable automation of various tasks in data discovery. In particular, we’ll focus on data enrichment, datasets search and recommendations, and explorations within a dataset.

Tutorial on Semantic Automation for Data Discovery

Social networking sites such as Facebook and Twitter have become favorite portals for users to discuss and express opinions. Research shows that topical discussions around events tend to evolve socially on microblogs. However, sources like Twitter have no explicit discussion thread which will link semantically similar posts. Moreover, the discussion may be evolving in multiple different threads (like Facebook). Researchers have proposed the use of online contemporary documents to act as external corpus to connect pairs of contextually related semantic topics. This motivates the question: given a significant social event, what is a good choice of external corpus to identify evolution of discussion topics around the event’s context? In this work, we compare the effectiveness of contemporary blog posts, online news media and forum discussions in creating adhoc external corpus. Using social propensity of evolution of topical discussions on Twitter to assess the goodness of the creation, we find online news media as most effective. We evaluate on three large reallife Twitter datasets to affirm our findings.

/pdf/an-empirical-assessment-of-contemporary-online-media-in-ad-2a00gc6opy.pdf

Sameep Mehta

Papers

Bollywood Movie Corpus for Text, Images and Videos

Generating a recommended shaping function to integrate data within a data repository

CFL: Causally Fair Language Models Through Token-level Attribute Controlled Generation

Tutorial on Semantic Automation for Data Discovery

An Empirical Assessment of Contemporary Online Media in Ad-Hoc Corpus Creation for Social Events