The Trump administration issued a ruling that expands the entities which can exempt themselves from the contraception mandate in the Affordable Care Act. A federal judge in Boston Monday threw out a lawsuit by Massachusetts' attorney general that attempted to block the Trump administration's rules expanding exemptions from ObamaCare's birth control mandate. U.S. District Judge Nathaniel Gorton said Massachusetts lacked standing to sue and noted that "the record is uniquely obscure" regarding whether employers in the state would take advantage of the exemptions. In a statement, Massachusetts Attorney General Maura Healey said that she was disappointed in the decision but remained committed to ensuring "affordable and reliable reproductive health care for women." ObamaCare originally required most companies to cover birth control at no additional cost, though it included exemptions for religious organizations.

A federal judge has tossed the Massachusetts attorney general's lawsuit against President Donald Trump's administration over rules allowing more employers to opt out of providing no-cost birth control to women.

It's that time of year, when like you, we all look to the future and ponder what the next one, two, five or even 10 years will be like? Specifically this blog addresses the following topics and themes: RegTech; Artificial Intelligence (AI); Blockchain (wider distributed ledgers); Crypto-currencies inc. Bitcoin; Machine Learning; Big Data; Smart Data; AML, KYC, MiFIDII, GDPR in addition to some specific companies you may want to keep an eye on and more... We must not forget to mention the former Panama, Bahamas and more recent Paradise paper leaks (see my other LinkedIn pulse to explore that topic in more detail). In this LinkedIn pulse I aim to walk you through the many topics, themes, patterns that are taking place and what I think the future holds in particular for the #RegTech sector & regulatory risk (in addition to others).

BOSTON (Reuters) - A federal judge on Monday rejected a lawsuit by Massachusetts' attorney general challenging new rules by President Donald Trump's administration that make it easier for employers to avoid providing insurance that covers women's birth control.

The glasses are being used to check people and registration plates against a centralized "blacklist" that the government compiles. Along with the facial recognition glasses, the government is also using facial scanners to monitor those entering the venue for the meeting. Many are concerned with the growing use of ever-more sophisticated surveillance technology throughout China and many worry that the blacklist will contain not only criminals, but political dissidents, journalists and human rights activists as well. "(China's) leadership once felt a degree of trepidation over the advancement of the internet and communication technologies," David Bandurski, co-director of the University of Hong Kong's China Media Project, told Reuters. "It now sees them as absolutely indispensable tools of social and political control."

Here's something you can bet on: Your youngest employees are already way smarter than you in lots of areas. As a leader, are you taking the time to learn from them? For bosses and senior leaders, staying relevant and effective means finding a way to keep up to date on what's just coming on the radar--and that's where reverse mentoring comes in. Reverse mentoring--in essence, learning from younger employees--isn't a new approach. But, in my experience, it's sadly underutilized. As a leader, you can always benefit from fresh eyes, new perspectives and direct insights into new technology.

Artificial intelligence is set to play a significantly greater role in society. And that raises the issue of accountability. If we rely on machines to make increasingly important decisions, we will need to have mechanisms of redress should the results turn out to be unacceptable or difficult to understand. But making AI systems explain their decisions is not entirely straightforward. One problem is that explanations are not free; they require considerable resources both in the development of the AI system and in the way it is interrogated in practice.

Drones are a perfect example of how our technology has evolved and will continue to grow in the future. So, what exactly does it offer? For casual users, it is just a fun toy. However, drones have use cases in multiple fields, including safety, health, and industry. Until now, drones have gone on a wild ride.

Our study aims to build a machine learning model for crime prediction using geospatial features for different categories of crime. The reverse geocoding technique is applied to retrieve open street map (OSM) spatial data. This study also proposes finding hotpoints extracted from crime hotspots area found by Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). A spatial distance feature is then computed based on the position of different hotpoints for various types of crime and this value is used as a feature for classifiers. We test the engineered features in crime data from Royal Canadian Mounted Police of Halifax, NS. We observed a significant performance improvement in crime prediction using the new generated spatial features.

Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. We completely characterize the delayed impact of three standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably. Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs.