As night falls in London, Georgina Rowlands and Anna Hart start applying makeup. Rowlands has long narrow blue triangles and thin white rectangles criss-crossing her face. Hart has a collection of red, orange and white angular shapes on hers. They're two of the four founders of the Dazzle Club, a group of artists set up last year to provoke discussion about the growing using of facial recognition technology. The group holds monthly silent walks through different parts of London to raise awareness about the technology, which they say is being used for "rampant surveillance."

Rediet Abebe uses algorithms and AI to improve access to opportunity for historically marginalized communities. When Abebe moved from her native Ethiopia to the United States to attend Harvard College, she was struck by how vital resources often fail to reach the most vulnerable people, even in the world's wealthiest nation. She now uses computational techniques to mitigate socioeconomic inequalities. While she was an intern at Microsoft, Abebe formulated an AI project that analyzes search queries to shed light on the unmet health information needs of people in Africa. Her study revealed such information as which demographic groups are likely to show interest in natural cures for HIV and which countries' residents are especially concerned about HIV/AIDS stigma and discrimination.

Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/ her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.

Bias in Word Embeddings has been a subject of recent interest, along with efforts for its reduction. Current approaches show promising progress towards debiasing single bias dimensions such as gender or race. In this paper, we present a joint multiclass debiasing approach that is capable of debiasing multiple bias dimensions simultaneously. In that direction, we present two approaches, HardWEAT and SoftWEAT, that aim to reduce biases by minimizing the scores of the Word Embeddings Association Test (WEAT). We demonstrate the viability of our methods by debiasing Word Embeddings on three classes of biases (religion, gender and race) in three different publicly available word embeddings and show that our concepts can both reduce or even completely eliminate bias, while maintaining meaningful relationships between vectors in word embeddings. Our work strengthens the foundation for more unbiased neural representations of textual data.

Planning is an essential topic in the realm of automated driving. Besides planning algorithms that are widely covered in the literature, planning requires different software tools for its development, validation, and execution. This paper presents a survey of such tools including map representations, communication, traffic rules, open-source planning stacks and middleware, simulation, and visualization tools as well as benchmarks. We start by defining the planning task and different supporting tools. Next, we provide a comprehensive review of state-of-the-art developments and analysis of relations among them. Finally, we discuss the current gaps and suggest future research directions.

A few years ago, Amazon employed a new automated hiring tool to review the resumes of job applicants. Shortly after launch, the company realized that resumes for technical posts that included the word "women's" (such as "women's chess club captain"), or contained reference to women's colleges, were downgraded. The answer to why this was the case was down to the data used to teach Amazon's system. Based on 10 years of predominantly male resumes submitted to the company, the "new" automated system in fact perpetuated "old" situations, giving preferential scores to those applicants it was more "familiar" with. Defined by AI4ALL as the branch of computer science that allows computers to make predictions and decisions to solve problems, artificial intelligence (AI) has already made an impact on the world, from advances in medicine, to language translation apps.

The soccer bot lines up to take a shot at the goal. But instead of getting ready to block it, the goalkeeper drops to ground and wiggles its legs. Confused, the striker does a weird little sideways dance, stamping its feet and waving one arm, and then falls over. It's not a tactic you'll see used by the pros, but it shows that an artificial intelligence trained via deep reinforcement learning--the technique behind cutting-edge game-playing AIs like AlphaZero and the OpenAI Five--is more vulnerable to attack than previously thought. And that could have serious consequences.

Women in Big Data is spotlighting 8 amazing women on March 8th, International Women's Day. Naomi Molefe is WiBD South African Chapter Lead. I recently joined the group talent team at Discovery Holdings, an insurance business headquarted in Johannesburg with 7000 employees, operating in 19 countries with a revenue of just under $700 million (F18). My role is to assist the business to build, source and attract talent pipelines in support of business strategy and talent requirements. I work with the head of Talent Acquisition in executing strategic recruitment for senior and scarce skills.

Women in Big Data is spotlighting 8 amazing women on March 8th, International Women's Day. Naomi Molefe is WiBD South African Chapter Lead. I recently joined the group talent team at Discovery Holdings, an insurance business headquarted in Johannesburg with 7000 employees, operating in 19 countries with a revenue of just under $700 million (F18). My role is to assist the business to build, source and attract talent pipelines in support of business strategy and talent requirements. I work with the head of Talent Acquisition in executing strategic recruitment for senior and scarce skills.

DURBAN - Across the globe, trends in technology, economics and socioeconomics are culminating to disrupt the way entire industries operate and deliver products and services to consumers. When it comes to the impact of technology, there is no industry riper for disruption than the financial services sector, including insurance, which for the longest time remained trapped in outdated product development and delivery models. That has changed, and today we're seeing the pace of change and meaningful innovation in the insurance sector escalating. Not only is the existing insurance model from advice, underwriting, onboarding, risk management, servicing, and claims processing being turned on its head, but new product solutions are now possible for market sectors that have been entirely underserved and marginalised by the formal economy. Until now, most innovation in the insurance sector has been internally focused with little direct value to the customer.