This event will be highly interactive. Dr Suzanne Little will give a short talk followed by group break-out sessions to discuss the topic in more depth. About the speaker: Dr Suzanne Little is Associate Professor and Senior Lecturer at the School of Computing, Dublin City University and SFI Principal Investigator, Insight Centre for Data Analytics. Before moving to the School of Computing at DCU in 2015, Suzanne was previously a Senior Research Fellow at the Insight Centre for Data Analytics at DCU. Suzanne originally joined the CLARITY research centre at Dublin City University in February 2012 and was principally responsible for the SAVASA project (Standards based Approach to Video Archive Search and Analysis). In 2013, CLARITY evolved to become Insight where Suzanne worked on and managed a number of projects in video analytics, motion analysis and data collection.

Jeff Bezos, Amazon's CEO, announces that his company will draft its own set of laws to regulate the use of facial recognition. The best way to have the law on your side is to create it. Maybe that's what Jeff Bezos thought. As part of the annual event dedicated to its virtual assistant Alexa, Amazon's CEO has just announced that his company will develop a set of laws to regulate facial recognition technology. This draft legislation will then be proposed to U.S. federal legislators.

A video created by Deloitte Consulting some moons ago aptly describes why Regulatory Tech or RegTech as it was affectionately called would go beyond being just a part of FinTech. In other words, it has moved way beyond being just a buzzword half-a-decade ago, thanks to ever-increasing costs of compliance coupled with the growing list of stringent regulations in the entrepreneurial world. Today there are several players who offer these solutions with real-time compliance analytics that support enterprises and regulators while protecting consumer interests. One such name is Bahwan CyberTek, a player that ideates on new regulations and risk management principles and brings innovative solutions to meet the ever increasing demands of compliance within the financial industry. In an exclusive interaction with CXOToday, Jaya Vaidhyanathan, President, Bahwan CyberTek, explains why RegTech is on the rise and why it matters in the age of compliance.

Artificial Intelligence (AI) and Machine Learning are used to power a variety of important modern software technologies. AI also powers the facial recognition software commonly used by law enforcement, landlords, and private citizens. Of all the uses for AI-powered software, facial recognition is a big deal. Security teams from large buildings that rely on video surveillance – like schools and airports – can benefit greatly from this technology. An AI algorithm has the potential to detect a known criminal or an unauthorized person on the property.

Has eDiscovery become too dominated by techno talk? Are we more worried about using our computers than our brains? What happened to the traditional skills of the "old days" of discovery? What happened to analysis, creativity, lateral thinking, interpretation of the facts, and even good old-fashioned common sense? This session will look at some of those skills that are now often overlooked or even ignored in the rush to upload data and find just the right rate of precision and recall.

The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact computational processes are missed if we do not consider the lifecycle stages upstream from model training and deployment. Yet, very little has been done to date to provide system-level support to data scientists who wish to develop and deploy responsible machine learning methods. We aim to fill this gap and present FairPrep, a design and evaluation framework for fairness-enhancing interventions. FairPrep is based on a developer-centered design, and helps data scientists follow best practices in software engineering and machine learning. As part of our contribution, we identify shortcomings in existing empirical studies for analyzing fairness-enhancing interventions. We then show how FairPrep can be used to measure the impact of sound best practices, such as hyperparameter tuning and feature scaling. In particular, our results suggest that the high variability of the outcomes of fairness-enhancing interventions observed in previous studies is often an artifact of a lack of hyperparameter tuning. Further, we show that the choice of a data cleaning method can impact the effectiveness of fairness-enhancing interventions.

Fox News Flash top headlines for Nov. 27 are here. Check out what's clicking on Foxnews.com A Georgia man is accused of scamming a Virginia woman out of more than $6.5 million after wooing her into a romantic relationship through an online dating site. Nnamdi Marcellus MgBodile, 35, from Marietta, Georgia, was charged with 20 counts of bank fraud, money laundering and conspiracy to commit bank fraud, according to a Justice Department press release on Wednesday. MgBodile and others also allegedly tried to scam a company out of $350,000 using an email scheme, according to the DOJ.

In 2018, Streamr announced a partnership with Daisy AI, Japan, an AI platform using blockchain for deep learning, based in Japan. Streamr intended to become Daisy AI's official data provider to exclusively sell data from Streamr's decentralized data Marketplace. Daisy AI planned to purchase data for a wide range of purposes, including forecasting stock and cryptocurrency prices, economy insights, footfall and traffic. Shohei Ohsawa, representative director of Daisy AI and associate professor at the University of Tokyo, recently made a series of racist and offensive statements on Twitter. He wrote that "Daisy does not hire Chinese people."

Data governance recently became a vitally important personal and political topic. Security and privacy are at the heart of new regulatory efforts like GDPR in the EU and CCPA in California. When asked, individuals rank data protections among their top privacy priorities. Companies know the importance of proper data governance, as well. Yet, research suggests their confidence in their own know-how may be overinflated.

If you think smart world-class companies don't face challenges when using machine learning to automate credit decisions, just ask Apple and Goldman Sachs. Based on a flurry of angry tweets and high-profile accusations, the New York Department of Financial Services launched an investigation into potential gender discrimination by algorithms that evaluate Apple Card applicants. Whether real or perceived, gender bias can damage the reputation of tech darlings like Apple, even if their credit decisioning process is wholly managed by someone else -- in this case, Goldman. It doesn't help when one of the accusers is a former Apple co-founder. Enova Decisions understands how to build accurate credit models -- without bias.