As I prepare for the Amplifying Her Voice event on International Women's Day hosted by The State of Women Institute (its a three day event! Register here), I wish I could be more excited about celebrating the progress of women in my field, but given my field is AI, Machine Learning and Data Science, my outlook on gender parity in the world as it is today is disappointingly unsatisfied. I can imagine a world where women's participation in AI fields is so normal that discussing gender parity is an afterthought. A world where there are so many examples of women (ALL women!) in technical and leadership roles that none of us feel like imposters. A world where we are celebrated and included in AI decisions, designs and policy improvements.

This article presents a large-scale automated analysis of gender trends in the authorship of Computer Science literature. We answer these questions by performing an automated study of literature metadata from scientific conferences and journals, using data from the Semantic Scholar academic search engine.a Our study incorporates metadata from 11.8M Computer Science publications. To provide a basis for comparison, we also analyze more than 140M articles from other fields of study. Our results demonstrate that although progress has been made, there is still a significant gap in gender representation among Computer Science authors. Continued delay in addressing the gender gap may perpetuate imbalances for generations to come. Our analysis was performed over the Semantic Scholar literature corpus.2 The corpus contains publications between 1940 and the end of November 2019, and associated metadata such as title, abstract, authors, publication venue, and year of publication.

As roles and tasks shift in tandem with the expansion of new technologies, and the division of work between human and machine is redrawn, it is of critical importance to monitor how those changes will impact the evolution of economic gender gaps. Artificial Intelligence (AI) is a prominent driver of change within the transformations brought about by the Fourth Industrial Revolution (4IR), and can serve as key marker of the trajectory of innovation across industries.19 In partnership with the LinkedIn Economic Graph Team, the World Economic Forum aims to provide fresh evidence of the emerging contours of gender parity in the new world of work through near-term labour market information. The increasing expansion of AI is creating the demand for a range of new skills, among them neural networks, deep learning, machine learning, and "tools" such as Weka and Scikit-Learn. AI skills are among the fastest-growing specializations among professionals represented on the LinkedIn platform.

Out of the many themes in HR, diversity frequently gets the spotlight. Yet seeing advanced analytics or AI solutions applied to diversity is rare, especially when compared to themes like turnover and absenteeism. Of course, putting numbers on gender, ethnicity, etc. is tricky: the very insights to counter discrimination might also be used to discriminate. As a result, diversity calls for less of a head-on approach than we are used to. In this article, I will explore how we may attain diversity-by-design in model development.

Stagnation in the proportion of women in the workplace and women's declining representation in politics, coupled with greater inequality in access to health and education, offset improvements in wage equality and the number of women in professional positions, leaving the global gender gap only slightly reduced in 2018. This is according to the Forum's Global Gender Gap Report 2018, published today. According to the report, the world has closed 68% of its gender gap, as measured across four key pillars: economic opportunity; political empowerment; educational attainment; and health and survival. While only a marginal improvement on 2017, the move is nonetheless welcome as 2017 was the first year since the report was first published in 2006 that the gap between men and women widened. At the current rate of change, the data suggest that it will take 108 years to close the overall gender gap and 202 years to bring about parity in the workplace.

Artificial Intelligence (AI) has been the talk of the town for a while, but it seems it can walk that talk too, with AI being potentially able to influence gender parity in the workplace. What AI can bring to the workplace was one of the topics explored and debated during'The Future of Work - Accelerating Gender Parity' Conference held by Harvard Business School (HBS) Club of India at Taj Mahal Palace Hotel in Mumbai on September 21. HBS faculty, industry leaders and other experts spoke at the conference to encourage the strategic importance of gender diversity in a changing environment. "Technology by itself is neutral, so technology can't favour women. But it can be utilized to mitigate the inherent biases against women. What we have started to see in organisations is that AI is being used to look at patterns, along with algorithms, to see if we are biasing some of our decisions, be it decisions concerning entry, promotions or the jobs that they get, against women," said Rekha Menon, Chairman and Senior Managing Director at Accenture in India, during the conference.

From the largest and most successful tech corporations to the smallest start-ups just finding their footing, most will agree that increasing diversity is in the best interests of customers, employees, and the general public. However, we in the tech world often fail to recognize the impact of our own biases. We sometimes think that, because our products and services are based on 0's and 1's, everything we put out into the world is fair and logical. This International Women's Day (Thursday), let's take a closer look at the biases that inhabit so much of our work, as well as some of the ways we can work toward a culture of inclusive AI. The theme of this year's International Women's Day is #PressforProgress, a call for gender parity across countries, industries, and all kinds of organizations. As we celebrate the achievements of women and push for equality, we should recognize that harmful biases affect a range of communities.

Deep-learning software attempts to mimic human brain activity in the neocortex, where thinking occurs. The software learns to recognize patterns in digital representations of sounds, images, and other data. Ray Kurzweil wrote a definitive book "How to Create a Mind" through software techniques. The goal of deep learning is to recreate human intelligence at a machine level, hence, Artificial Intelligence. However, the outcome of such learning is predicated on how well the software is trained.

Deep-learning software attempts to mimic human brain activity in the neocortex, where thinking occurs. The software learns to recognize patterns in digital representations of sounds, images, and other data. Ray Kurzweil wrote a definitive book "How to Create a Mind" through software techniques. The goal of deep learning is to to recreate human intelligence at a machine level, hence, Artificial Intelligence. However, the outcome of such learning is predicated on how well the software is trained.

There has been much discussion in the last few days about the announcement by the World Economic Forum that it predicts it will take 117 more years until we achieve gender parity in the workplace. It seems crazy that in today's workforce, which is driving developments like self-driving cars, gaming-genius AI, and making hoverboards a reality, we still don't have gender equality. Research published recently by EY makes a compelling case for businesses to do more in terms of tackling existing inequalities: data shows that more diverse company boards command higher share prices and improved financial performance; balanced leadership increases a company's productivity and nationally a country's GDP can be lifted by reducing the gender gap. Another piece of research that looked at start-ups receiving Series A funding in the Bay Area in 2015, showed that only 8% of firms were led by women - that's 16 out of 204 start-ups. And this figure was down by 30% from the previous year.