With the recent rise of #MeToo, an increasing number of personal stories about sexual harassment and sexual abuse have been shared online. In order to push forward the fight against such harassment and abuse, we present the task of automatically categorizing and analyzing various forms of sexual harassment, based on stories shared on the online forum SafeCity. For the labels of groping, ogling, and commenting, our single-label CNN-RNN model achieves an accuracy of 86.5%, and our multi-label model achieves a Hamming score of 82.5%. Furthermore, we present analysis using LIME, first-derivative saliency heatmaps, activation clustering, and embedding visualization to interpret neural model predictions and demonstrate how this extracts features that can help automatically fill out incident reports, identify unsafe areas, avoid unsafe practices, and 'pin the creeps'.

Law firms tasked with analyzing mounds of data and interpreting dense legal texts can vastly improve their efficiency by training artificial intelligence (AI) tools to complete this processing for them. While AI is making headlines in a wide range of industries, legal AI may not come to mind for many. But the technology, which is already prevalent in the manufacturing, cybersecurity, retail and healthcare sectors, is quickly becoming a must-have tool in the legal industry. Due to the sheer volume of sensitive data belonging to both clients and firms themselves, legal organizations are in a prickly position when it comes to their responsibility to uphold data privacy. Legal professionals are still learning what the privacy threats are and how they intersect with data security regulations.

Algorithmic predictions are increasingly used to aid, or in some cases supplant, human decision-making, and this development has placed new demands on the outputs of machine learning procedures. To facilitate human interaction, we desire that they output prediction functions that are in some fashion simple or interpretable. And because they influence consequential decisions, we also desire equitable prediction functions, ones whose allocations benefit (or at the least do not harm) disadvantaged groups. We develop a formal model to explore the relationship between simplicity and equity. Although the two concepts appear to be motivated by qualitatively distinct goals, our main result shows a fundamental inconsistency between them. Specifically, we formalize a general framework for producing simple prediction functions, and in this framework we show that every simple prediction function is strictly improvable: there exists a more complex prediction function that is both strictly more efficient and also strictly more equitable. Put another way, using a simple prediction function both reduces utility for disadvantaged groups and reduces overall welfare. Our result is not only about algorithms but about any process that produces simple models, and as such connects to the psychology of stereotypes and to an earlier economics literature on statistical discrimination.

Machine Learning community is recently exploring the implications of bias and fairness with respect to the AI applications. The definition of fairness for such applications varies based on their domain of application. The policies governing the use of such machine learning system in a given context are defined by the constitutional laws of nations and regulatory policies enforced by the organizations that are involved in the usage. Fairness related laws and policies are often spread across the large documents like constitution, agreements, and organizational regulations. These legal documents have long complex sentences in order to achieve rigorousness and robustness. Automatic extraction of fairness policies, or in general, any specific kind of policies from large legal corpus can be very useful for the study of bias and fairness in the context of AI applications. We attempted to automatically extract fairness policies from publicly available law documents using two approaches based on semantic relatedness. The experiments reveal how classical Wordnet-based similarity and vector-based similarity differ in addressing this task. We have shown that similarity based on word vectors beats the classical approach with a large margin, whereas other vector representations of senses and sentences fail to even match the classical baseline. Further, we have presented thorough error analysis and reasoning to explain the results with appropriate examples from the dataset for deeper insights.

The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.

Today marks the start of the fall Strata Data Conference in New York City, which has traditionally been the big data community's biggest show of the year. It's been a wild ride for the big data crowd in 2018, one that's brought its share of highs and lows. Now it's worth taking some time to consider where big data has come, and where it's possibly headed in the future. Here are five things to keep in mind as the Strata Data Conference kicks off. We've said this before, but it bears repeating: Hadoop is just one of many technologies angling for relevance in today's increasingly heterogeneous at-scale computing environment.

Google is fighting in Europe's top court today to tighten the scope of an EU privacy law that grants citizens the'right to be forgotten'. The rule allows people to demand Google remove search results that mention outdated or embarrassing information about them. This includes links to websites mentioning serious incidents - such as bankruptcy or criminal convictions - that may cause that person to be stigmatised. Google is battling with France's data privacy regulator over an order to extend the rule to remove search results worldwide upon request. The dispute pits data privacy concerns against the public's right to know, while also raising thorny questions about how to enforce differing legal jurisdictions when it comes to the borderless internet.

Justin Kan launched a new company, with a fresh $65M round led by Andreessen Horowitz, General Catalyst, and YC's Continuity Fund. This isn't Kan's first startup: he launched and sold live-streaming and gaming startup Twitch to Amazon for a cool billion dollars in 2014. Kan's been building for years as part of the first class of famed startup accelerator Y Combinator with Reddit's Alexis Ohanian, late internet-activist Aaron Swartz, and current YC President Sam Altman. Kan's new startup, Atrium, wants to automate lawyers using machine learning. Since going through YC last winter and raising $10M last year, they've acquired 220 customers, including bird-themed companies, MessageBird, SendBird, and Bird.

Kent Walker, vice president and general counsel with Google Inc., from right, Colin Stretch, general counsel with Facebook Inc., and Sean Edgett, acting general counsel with Twitter Inc., swear in to a House Intelligence Committee hearing in Washington, D.C., U.S., on Wednesday, Nov. 1, 2017. Technology has advanced rapidly along several related fronts. In just the last few years, there have been dramatic improvements in robotics, sensors, and machine vision, and Artificial Intelligence (AI) can now perform better, per Stanford's AI Index, than humans on multiple dimensions, including image recognition, speech recognition, translation, and strategy games such as Go, Poker and chess. In pursuit of profits from AI-enabled business models, firms are now investing lots of money in these technologies. Worldwide industrial robotics shipments have increased from an annual average of about 100,000 units prior to 2010 to almost 300,000 annual shipments by 2016.

Finance, education, medicine, programming, the arts -- artificial intelligence is set to disrupt nearly every sector of our society. Governments and policy experts have started to realize that, in order to prepare for this future, in order to minimize the risks and ensure that AI benefits humanity, we need to start planning for the arrival of advanced AI systems today. Although we are still in the early moments of this movement, the landscape looks promising. Several nations and independent firms have already started to strategize and develop polices for the governance of AI. Last year, the UAE appointed the world's first Minister of Artificial Intelligence, and Germany took smaller, but similar, steps in 2017, when the Ethics Commission at the German Ministry of Transport and Digital Infrastructure developed the world's first set of regulatory guidelines for automated and connected driving.