Thank you, Alexander [Campbell] for the introduction. Thanks also to Genevieve Furtado and the other conference organizers for the invitation to speak here today, at the 19th Annual Operational Risk North America Conference. I understand that this is the Champagne Keynote address. Given that title, I feel obligated as an economist to share with you the reported last words of John Maynard Keynes – the father of modern macroeconomics: "I should have drunk more champagne." I hope my words here today do not inspire a similar sentiment. And finally, I must remind you that the views that I express today are my own and do not necessarily reflect the views of the Commission or its staff.[1] My remarks this afternoon will center on a technology topic that is encroaching on many aspects of our lives and increasingly so within financial markets: Artificial Intelligence. Perhaps better known by its two-letter acronym "AI," artificial intelligence has been the fodder of science fiction writing for decades. But the technology underlying AI research has recently found applications in the financial sector – in a movement that falls under the banner of "Fintech."

If you applied for a mortgage, would you be comfortable with a computer using a collection of data about you to assess how likely you are to default on the loan? If you applied for a job, would you be comfortable with the company's human-resources department running your information through software that will determine how likely it is that you will, say, steal from the company, or leave the job within two years? If you were arrested for a crime, would you be comfortable with the court plugging your personal data into an algorithm-based tool, which will then advise your judge on whether you should await trial in jail or at home? If you were convicted, would you be comfortable with the same tool weighing in on your sentencing? Much of the hand-wringing about advances in artificial intelligence has been concerned with AI's effects on the labor market.

More and more entities are deploying machine learning and artificial intelligence to automate tasks previously performed by humans. Such efforts carry with them real benefits, such as the enhancement of operational efficiency and the reduction of costs, but they also raise a number of concerns regarding their potential impacts on human society, particularly as computer algorithms are increasingly used to determine important outcomes like individuals' treatment within the criminal justice system. This mixture of benefits and concerns is starting to attract the interest of regulators. Efforts in the European Union, Canada, and the United States have initiated an ongoing discussion around how to regulate "automated decision-making" and what principles should guide it. And while not all of these regulatory efforts will directly implicate private companies, they may nonetheless provide insight for companies seeking to build consumer trust in their artificial intelligence systems or better prepare themselves for the overall direction that regulation is taking.

The First International Workshop on AI and Intelligent Assistance for Legal Professionals in the Digital Workplace (LegalAIIA) will be held at the Cyberjustice Laboratory at the University of Montreal on June 17th. This workshop is part of the 17th International Conference on AI and Law (ICAIL), a biennial conference which has served as an important forum at the intersection the AI and the law since its founding in 1987. The LegalAIIA workshop itself is an offshoot of the successful decade-long DESI (Discovery for Electronically Stored Informed) workshop series, which was pivotal in helping forge an interdisciplinary community of legal and technical practitioners working on advancing the state-of-the-art in electronic discovery practice. The first edition of Legal AIIA, driven by an impressive set of electronic discovery veterans including Jack G. Conrad (Thomson Reuters), Jeremy Pickens (Catalyst Repository Systems), Amanda Jones (H5), Hans Henseler (Magnet Forensics), and Jason R. Baron (Drinker, Biddle & Reath), aims to tackle head on the issue of human-AI collaboration. Accepted papers will focus on evaluating when and how to best leverage a "human-in-the-loop" approach to AI.

Iron Maiden are suing video game company 3D Realms over the game Ion Maiden, which they describe as an "incredibly blatant" infringement on their trademark. The lawsuit, which demands $2m (£1.58m) in damages, argues that the game's title will cause "confusion among consumers", "is nearly identical to the Iron Maiden trademark in appearance, sound and overall commercial impression", and "is attempting to trade off on Iron Maiden's notoriety". It argues that the name of the game's protagonist Shelley Harrison is a play on Iron Maiden bassist Steve Harris, and that a skull icon is similar to the band's mascot Eddie. It also argues the game is similar to Iron Maiden's own game Legacy of the Beast and gives examples of fans writing online that they presumed the game was a band tie-in. The game is still in development, and 3D Realms adds that "everyone continues to work diligently on Ion Maiden to deliver the best possible experience later this year".

As lawmakers, citizens, and company's debate the use of facial recognition software in the U.S., tech giants in America and China have been busy hawking products to eager surveillance states abroad. Among the burgeoning markets, according to a report by Buzzfeed News, are monarchies in the United Arab Emirates (UAE), particularly in Dubai, where political leaders have often jailed citizens and journalists that they deem to be political dissidents. Critics of the UAE include Human Rights Watch (HRW) who has frequently derided the country for its authoritarian tendencies. Private companies like IBM are looking to governments accused of violating human rights as a market for facial recognition software. 'UAE authorities have launched a sustained assault on freedom of expression and association since 2011,' says HRW in its analysis.

Although "black box" models such as Artificial Neural Networks, Support Vector Machines, and Ensemble Approaches continue to show superior performance in many disciplines, their adoption in the sensitive disciplines (e.g., finance, healthcare) is questionable due to the lack of interpretability and explainability of the model. In fact, future adoption of "black box" models is difficult because of the recent rule of "right of explanation" by the European Union where a user can ask for an explanation behind an algorithmic decision, and the newly proposed bill by the US government, the "Algorithmic Accountability Act", which would require companies to assess their machine learning systems for bias and discrimination and take corrective measures. Top Bankruptcy Prediction Models are A.I.-based and are in need of better explainability -the extent to which the internal working mechanisms of an AI system can be explained in human terms. Although explainable artificial intelligence is an emerging field of research, infusing domain knowledge for better explainability might be a possible solution. In this work, we demonstrate a way to collect and infuse domain knowledge into a "black box" model for bankruptcy prediction. Our understanding from the experiments reveals that infused domain knowledge makes the output from the black box model more interpretable and explainable.

Technology is changing at rapid speed. But if there's one area outside of IT where workers are feeling breathless from trying to keep pace, it's compliance -- specifically compliance regulation and the myriad technologies that address it. Think of the 2014 movie "The Imitation Game," in which a team of mathematicians spent an entire day trying to crack the Russian code, only to start from square one the next morning. That should give you a sense of the pains faced by present-day compliance officers. Compliance doesn't refresh each day as the example suggests, but the changes in regulations are copious and labor-intensive.

Imagine a world without environmental regulations or traffic laws, where unlicensed motorists drive as they please and factories pollute with impunity. Those were the facts of life in cities around the world as the industrial revolution took hold. And a few decades from now, we may look back on the emergence of AI as a similarly lawless era. With that in mind, governments in Canada and the European Union, among others, have been active in proposing regulations to protect consumers while the U.S. has largely remained silent -- until now. Computers are increasingly involved in the most important decisions affecting Americans' lives – whether or not someone can buy a home, get a job or even go to jail." This spring, Democratic senators Cory Booker and Ron Wyden proposed the first national AI ethics bill in the form of the Algorithmic Accountability Act. The bill aims to give regulators, and the public, greater insights into how AI systems make the decisions they do -- and what data is ...

Last week, another notable AI milestone occurred. Fresh off the heels of the U.S. Treasury's Financial Crimes Enforcement Network and Federal Banking Agencies' recent initiative that urged financial services organizations to implement "innovative approaches" like AI to better combat money laundering, terrorist financing and other illicit financial threats, U.S. Senators Martin Heinrich (D-N.M.) and Rob Portman (R-Ohio) announced the Artificial Intelligence Initiative Act. The bipartisan legislation represents a coordinated, national strategy for developing AI and will provide a $2.2 billion federal investment over five years to "build an AI-ready workforce, accelerating the responsible delivery of AI applications for government agencies, academia and the private sector over the next 10 years." Many businesses are spending exorbitant amounts of time and money on what they think is AI, yet in reality failing to reap any of the benefits of true unsupervised learning technology. This legislation is desperately needed.