In this special guest feature, Gary M. Shiffman, PhD, Co-founder and CEO, Consilient, takes a look at Federated Machine Learning, the branch of machine learning that's sure to be a revolution for FCC professionals by enabling collaboration while preserving privacy. Gary is an applied micro-economist and business executive working to combat organized violence, corruption, and coercion. Past experiences include senior positions at the Pentagon, U.S. Senate, and the Department of Homeland Security. He is the Founder and CEO of Giant Oak, Inc. and the Co-Founder and CEO of Consilient, Inc., machine learning and artificial intelligence companies building solutions to support professionals promoting national security and combating financial crime. Dr. Shiffman is the author of The Economics of Violence: How Behavioral Science Can Transform Our View of Crime, Insurgency, and Terrorism with Cambridge University Press in 2020.

Did you miss a session from the Future of Work Summit? There is little doubt that AI is changing the business landscape and providing competitive advantages to those that embrace it. It is time, however, to move beyond the simple implementation of AI and to ensure that AI is being done in a safe and ethical manner. This is called responsible AI and will serve not only as a protection against negative consequences, but also as a competitive advantage in and of itself. Responsible AI is a governance framework that covers ethical, legal, safety, privacy, and accountability concerns.

Parity is among a growing crop of startups promising organizations ways to develop, monitor, and fix their AI models. They offer a range of products and services from bias-mitigation tools to explainability platforms. Initially most of their clients came from heavily regulated industries like finance and health care. But increased research and media attention on issues of bias, privacy, and transparency have shifted the focus of the conversation. New clients are often simply worried about being responsible, while others want to "future proof" themselves in anticipation of regulation.

One of the challenges in developing AI applications is obtaining the vast amount of data that's required. Making matters worse, regulations and privacy issues pose obstacles to firms' sharing their data. A possible solution is for firms to form a "data trust." Willis Towers Watson recently piloted a data trust together with several of its clients. This article shared what they learned about how to create such a trust.

This year, despite the challenges from the Covid-19 pandemic, large corporations in the financial industry are operationalizing their AI initiatives. Many mature organizations already have established processes. In the last few years, they've been implementing process workflows, software tools, and frameworks to quickly operationalize their models to capitalize on the changing business landscape. However, as the business environment changed during the Covid-19 pandemic, organizations observed changes in their models' underlying assumptions. The urgency to rapidly deploy new models in a controlled environment to account for the market risks and take advantage of new opportunities proved to be challenging.

One of the greatest barriers to adopting and scaling AI applications is the scarcity of varied, high-quality raw data. To overcome it, firms need to share their data. But the many regulatory restrictions and ethical issues surrounding data privacy pose a major obstacle to doing this. A novel solution that my firm is piloting that could solve this problem is a data trust: an independent organization that serves as a fiduciary for the data providers and governs their data's proper use. Research shows that companies are becoming increasingly aware of the value of sharing data and are exploring ways to do so with other players in their industry or across industries.

One of the essential phrases necessary to understand AI in 2019 has to be "ethics washing." Put simply, ethics washing -- also called "ethics theater" -- is the practice of fabricating or exaggerating a company's interest in equitable AI systems that work for everyone. A textbook example for tech giants is when a company promotes "AI for good" initiatives with one hand while selling surveillance capitalism tech to governments and corporate customers with the other. Accusations of ethics washing have been lobbed at the biggest AI companies in the world, as well as startups. The most high-profile example this year may have been Google's external AI ethics panel, which devolved into a PR nightmare and was disbanded after about a week.

Whether we like it or not, technological advances are reshaping the way companies do business. Artificial intelligence, which involves the processing of huge amounts of data by machine, is becoming more and more commonplace, creating both opportunity on a grand scale and a daunting level of risk. Because boardrooms are the place where opportunity and risk are assessed and judgements made, technology is now on the governance agenda. Directors who have shied away from tackling the subject in the past have less and less excuse for ignoring it, but the question is: where and how to begin? At the Institute of Business Ethics we have been trying to work out some of the answers to the complex questions posed by the increasing use of AI.

Ratul has more than 11 years of experience in technology and advanced analytics, machine learning and AI systems, primarily focused on consumer and SME lending. Ratul worked in leading credit bureau Transunion Cibil in India, where he worked more on automation and machine learning using the power of Big data. Earlier Ratul also worked in Global data science leader SAS in its Research and Development office in Pune, India. Ratul holds a B.Tech from National Institute of Technology, Calicut (India). Analytics India Magazine: How important is Data science & AI within Banking Systems in Malaysia?

The idea of artificial tends to strike fear in the hearts of workers who suspect they'll be replaced by robots. The reality is more nuanced. There is no question some jobs will be lost. But others will be created, and still others will morph into something different -- bot designer, bot supervisor, soother of the most irate customers. In some cases, AI will just take on extra work nobody wants to do.