Social bias in machine learning has drawn significant attention, with work ranging from demonstrations of bias in a multitude of applications, curating definitions of fairness for different contexts, to developing algorithms to mitigate bias. In natural language processing, gender bias has been shown to exist in context-free word embeddings. Recently, contextual word representations have outperformed word embeddings in several downstream NLP tasks. These word representations are conditioned on their context within a sentence, and can also be used to encode the entire sentence. In this paper, we analyze the extent to which state-of-the-art models for contextual word representations, such as BERT and GPT-2, encode biases with respect to gender, race, and intersectional identities. Towards this, we propose assessing bias at the contextual word level. This novel approach captures the contextual effects of bias missing in context-free word embeddings, yet avoids confounding effects that underestimate bias at the sentence encoding level. We demonstrate evidence of bias at the corpus level, find varying evidence of bias in embedding association tests, show in particular that racial bias is strongly encoded in contextual word models, and observe that bias effects for intersectional minorities are exacerbated beyond their constituent minority identities. Further, evaluating bias effects at the contextual word level captures biases that are not captured at the sentence level, confirming the need for our novel approach.

Proceedings of the AAAI Fall Symposium on Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA, November 7-8, 2019

November 21, 2019 Artificial intelligence (AI) is emerging as the prize in an increasingly competitive geopolitical environment, with American rivals investing in AI to shift the military, political, and economic balance of power against the U.S. Leveraging AI can help make the government more efficient, spurring economic growth, and transforming education for future generations. Yet agency leaders still search for where to begin within their own departments. What projects will be the most impactful? What can be done to train the existing workforce to leverage the power of AI? What to expect: You will leave this course able to identify top AI opportunities, with an understanding of key AI success factors, and with a clearly defined project ready for your team to execute. Who should attend: Agency leaders seeking to initiate or improve the performance of machine learning initiatives and AI projects for their agency or department.

Arguably, one of the biggest debates happening in data science in 2019 is the need for AI explainability. The ability to interpret machine learning models is turning out to be a defining factor for the acceptance of statistical models for driving business decisions. Enterprise stakeholders are demanding transparency in how and why these algorithms are making specific predictions. A firm understanding of any inherent bias in machine learning keeps boiling up to the top of requirements for data science teams. As a result, many top vendors in the big data ecosystem are launching new tools to take a stab at resolving the challenge of opening the AI "black box." Some organizations have taken the plunge into AI even with the realization that their algorithm's decisions can't be explained.

Artificial intelligence and robotic process automation are being used to help federal agencies in their digital transformation strategies. As part of my detail with the IBM Center for The Business of Government, I met with several thought leaders across government about this topic, as well as practitioners across IBM. This helped me glean key insights on how leaders may incorporate robotic process automation (RPA) as a precursor to artificial intelligence (AI) in their workforce and workplace. For example, I had the opportunity to hear directly from Margie Graves, the Deputy Chief Information Officer for the US, at a meeting of the National Academy of Public Administration this past July as she touched on ways to tackle implementation of AI. She also described that the best way to view AI is in three different phases: assisted AI, augmented AI, and autonomous AI.

Additional documents from former special counsel Robert Mueller's report provide a layer of texture to the Russiagate scandal. YOU THOUGHT THE MUELLER REPORT WAS OVER, didn't you? Well, yesterday, BuzzFeed's Jason Leopold -- a level 19 FOIA ninja -- and his colleagues got their hands on detailed summaries of the interviews three Trump aides gave to the FBI, known as "302 reports," along with other documents. And while they don't appreciably change our understanding of the Russiagate scandal, they do add a layer of texture to what we already knew. And even after his firing, he was still in touch with top campaign officials up to Election Day, though campaign'CEO' Steve Bannon warned in an email to Jared Kushner: "We need to avoid this guy like the plague."

Artificial Intelligence has been gaining ground as an essential field of research that could revolutionize the world since the first computer revolution in the 1980s. The technology has seen several roller-coasters in its journey, and today, it has come out with many promises to society, accelerated by vast advances in computing capacity. Besides the private sector, AI has now entered into federal agencies, even in a few places, it is creating massive impacts. Still, many things have to be done right now as the technology in its initial experimental phase. At ATARC's Federal Artificial Intelligence And Data Analytics Summit, Director of artificial intelligence at the Department of Veterans Affairs (VA), Gil Alterovitz said, federal data analytics and automation efforts are still getting organized around a larger new paradigm.

The artificial intelligence industry in India is booming. While private companies, colleges, universities, and even talented individuals are driving this change, one cannot forget the role that the Government of India has played in recent years. The sheer push from the Government to come up with policies, initiatives and even partnerships with other nations has helped India to have a stronghold in AI. In this article, we are going to see some of India's major partnerships with other nations to strengthen the country's artificial intelligence industry. Since 2000, German companies have invested nearly $12 billion in India.

One of Britain's best-known economists, Roger Bootle runs Capital Economics, Europe's largest macroeconomics consultancy, which he founded. Roger appears frequently on television and radio and is also a regular columnist for The Daily Telegraph. In the Comment Awards 2012 he was named Economics Commentator of the year. He is the author of widely acclaimed books including - The Trouble with Markets, Money for Nothing and The Death of Inflation. Roger is also a Specialist Adviser to the House of Commons Treasury Committee.

Artificial intelligence is a concept that seems tailor-made for the intelligence community. The ability to sort through massive amounts of data, seeking out patterns large and small, anomalies that warrant further investigation, that's what intelligence analysts do already. Imagine what they could achieve when augmented by AI? Dean Souleles, chief technology advisor for the Office of the Director of National Intelligence, said on Agency in Focus – Intelligence Community that the IC is working now to lay the foundation for adopting AI. "You cannot build a house without a solid foundation. The foundation of AI is data and computational technology," Souleles said. "The intelligence community has spent much of the last decade on a program we call ICITE, the information technology enterprise of the IC. And that's been about modernizing the technology infrastructure. And that is about getting cloud technology throughout the community, making basic computational capability available to our technologists just as it is in the private sector. But that's not good enough, because the new era of computation requires sophisticated kinds of computing. We talk about GPUs, graphical processing units, or tensor processing units (TPUs), or neuromorphic chips or field programmable gate arrays, or any of the wide variety of things that are the specialized computation that enable AI computation. And we need to make the investments in those things."