The worldwide growth of maritime traffic and the development of the Automatic Identification System (AIS) has led to advances in monitoring systems for preventing vessel accidents and detecting illegal activities. In this work, we describe research gaps and challenges in machine learning for vessel behavior change and event detection, considering several constraints imposed by real-time data streams and the maritime monitoring domain. As a starting point, we investigate how unsupervised and semi-supervised change detection methods may be employed for identifying shifts in vessel behavior, aiming to detect and label unusual events.

Most financial institutions know it's critical to manage the ever-increasing amounts of accessible data, but many miss the potential in using that data in innovative ways. Financial institutions have a plethora of data they can access, either through their own systems or through public sources. However, many can't -- or won't -- exploit the large volumes of data, particularly the "owned" data that an organization holds about customers. This kind of data is typically called customer relationship management data, such as the purchase history tied to app installs, email addresses and postal addresses. Though financial institutions maintain and collect massive volumes of data, many firms are restricted from fully using that data because they are required to comply with stringent regulations around what can and cannot be done with customer data. Such major regulatory changes include the Dodd-Frank Act in the United States, Europe's Markets in Financial Instruments Directive II and the General Data Protection Regulation -- all of which affect banks with a global presence.

Members of the public have said there is no justification for the use of facial recognition technology in CCTV systems operated by a private developer at a 67-acre site in central London. It emerged on Monday that the property developer Argent was using the cameras "in the interests of public safety" in King's Cross, mostly north of the railway station across an area including the Google headquarters and the Central Saint Martins art school, but the precise uses of the technology remained unclear. "For law enforcement purposes, there is some justification, but personally I don't think a private developer has the right to have that in a public place," said Grant Otto, who lives in London. He questioned possible legal issues around the collection of facial data by a private entity and said he was unaware of any protections that would allow people to request their information be removed from a database, with similar rights as those enshrined in GDPR. Jack Ramsey, a tourist from New Zealand, echoed his concerns. He said: "It makes you think: 'What sort of information they are trying to get from us?' Are they trialling a new system for security reasons, are they tracking every person who comes in the area – maybe for information that could be bought by the shops, like'Our customer comes here three times a week, is there a way we can target him more?'"

The UK's privacy regulator said it is studying the use of controversial facial recognition technology by property companies amid concerns that its use in CCTV systems at the King's Cross development in central London may not be legal. The Information Commissioner's Office warned businesses using the surveillance technology that they needed to demonstrate its use was "strictly necessary and proportionate" and had a clear basis in law. The data protection regulator added it was "currently looking at the use of facial recognition technology" by the private sector and warned it would "consider taking action where we find non-compliance with the law". On Monday, the owners of the King's Cross site confirmed that facial recognition software was used around the 67-acre, 50-building site "in the interest of public safety and to ensure that everyone who visits has the best possible experience". It is one of the first landowners or property companies in Britain to acknowledge deploying the software, described by a human rights pressure group as "authoritarian", partly because it captures images of people without their consent.

Despite facial recognition's seal of approval from law enforcement agencies across the U.S., recent experiments show the technology is far from infallible. In a demonstration by the American Civil Liberties Union, about 26 California lawmakers were misidentified by face-matching software built by Amazon, putting the rate of a mismatch at about 1 in 5. The results mimic a similar test done by the advocacy group in 2018 when a test saw Amazon's software, called'Rekognition', mismatch 28 members of congress -- many of whom were people of color. The ACLU says a test of Amazon's facial recognition software misidentified 1 in 5 lawmakers fed into its system Similarly, the software attempted to match their head shots against a database of known criminals -- a process that has become commonplace for the at least 200 departments across the U.S. who use Rekognition software. According to the LA Times, the test is fueling calls from California legislators to limit the technology's application in a law enforcement capacity, including its integration with police body cameras.

The use of deep neural networks to make high risk decisions creates a need for global and local explanations so that users and experts have confidence in the modeling algorithms. We introduce a novel technique to find global and local explanations for time series data used in binary classification machine learning systems. We identify the most salient of the original features used by a black box model to distinguish between classes. The explanation can be made on categorical, continuous, and time series data and can be generalized to any binary classification model. The analysis is conducted on time series data to train a long short-term memory deep neural network and uses the time dependent structure of the underlying features in the explanation. The proposed technique attributes weights to features to explain an observations risk of belonging to a class as a multiplicative factor of a base hazard rate. We use a variation of the Cox Proportional Hazards regression, a Generalized Additive Model, to explain the effect of variables upon the probability of an in-class response for a score output from the black box model. The covariates incorporate time dependence structure in the features so the explanation is inclusive of the underlying time series data structure.

Illinois is attempting to stay at the forefront of legislating the interaction between employment and technology with the Artificial Intelligence Video Interview Act (Act), which the state legislature passed on May 29, 2019. The Act, which is effective Jan. 1, 2020, impacts an employer's ability to use artificial intelligence (AI) when hiring workers in Illinois. Under the Act, an employer using videotaped interviews when filling a position in Illinois may use AI to analyze the interview footage only if 1) the employer notifies the applicant that the videotaped interview may be analyzed using AI for purposes of evaluating the applicant's fitness for the position, 2) the employer provides the applicant with information about how the AI works and what characteristics it uses to evaluate applicants, and 3) the employer obtains consent from the applicant to use AI for an analysis of the video interview. Further, because audio will be recorded, the employer must obtain the consent of the applicant to videotape the interview with or without the use of AI. An employer is not required to consider an applicant who refuses to provide consent for the employer's use of AI to evaluate the candidate.

Regulators have the obligation to monitor all the entities that fall under their jurisdiction to ensure that their regulations are followed and there is no exploitation of customers as well as businesses. They must, as often as they can, verify the compliance of these entities either by checking their documentation or their processes. If they find any discrepancies or deliberate fraud attempts, they must take legal action against the defaulters. However, constantly monitoring an ever-growing number of businesses and individuals under their regulatory purview and effectively ensuring compliance can be too much as it requires the scrutiny of several parameters associated with each entity. This can be especially hard considering the fact that these regulators have limited resources, both in terms of people and funds.

A new patent filing in the U.K. aims to find out. An international team led by AI activist Ryan Abbott, a law professor at the University of Surrey, has filed the first-ever patent applications for two inventions created autonomously by artificial intelligence without a human inventor. The AI inventor, named DABUS by its creator, Stephen Thaler, was previously best known for creating surreal art, but it was designed to come up with new ideas and then assess those ideas for consequences, novelty, and salience. So far the series of neural networks that makes up DABUS has come up with two ideas that may be worth patenting. According to a press release, one patent application is for "a new type of beverage container based on fractal geometry," which sounds pretty sweet, while the other is for a device that can help attract attention that could be useful in search and rescue operations.

As privacy regulations like GDPR and the California Consumer Privacy Act proliferate, more startups are looking to help companies comply. Enter Preclusio, a member of the Y Combinator Summer 2019 class, which has developed a machine learning-fueled solution to help companies adhere to these privacy regulations. "We have a platform that is deployed on-prem in our customer's environment, and helps them identify what data they're collecting, how they're using it, where it's being stored and how it should be protected. We help companies put together this broad view of their data, and then we continuously monitor their data infrastructure to ensure that this data continues to be protected," company co-founder and CEO Heather Wade told TechCrunch. She says that the company made a deliberate decision to keep the solution on-prem.