The taxation of multi-national companies is a complex field, since it is influenced by the legislation of several states. Laws in different states may have unforeseen interaction effects, which can be exploited by allowing multinational companies to minimize taxes, a concept known as tax planning. In this paper, we present a knowledge graph of multinational companies and their relationships, comprising almost 1.5M business entities. We show that commonly known tax planning strategies can be formulated as subgraph queries to that graph, which allows for identifying companies using certain strategies. Moreover, we demonstrate that we can identify anomalies in the graph which hint at potential tax planning strategies, and we show how to enhance those analyses by incorporating information from Wikidata using federated queries.

Bike sharing systems (BSS) have been a popular traveling service for years and are used worldwide. It is attractive for cities and users who wants to promote healthier lifestyles; to reduce air pollution and greenhouse gas emission as well as improve traffic. One major challenge to docked bike sharing system is redistributing bikes and balancing dock stations. Some studies propose models that can help forecasting bike usage; strategies for rebalancing bike distribution; establish patterns or how to identify patterns. Other studies propose to extend the approach by including weather data. This study aims to extend upon these proposals and opportunities to explore how and in what magnitude weather impacts bike usage. Bike usage data and weather data are gathered for the city of Washington D.C. and are analyzed using k-means clustering algorithm. K-means managed to identify three clusters that correspond to bike usage depending on weather conditions. The results show that the weather impact on bike usage was noticeable between clusters. It showed that temperature followed by precipitation weighted the most, out of five weather variables.

The Human Rights Commission has warned that the federal government's growing reliance on artificial intelligence and automated decisions is dangerous and will increasingly put vulnerable Australians at risk. Releasing new research on Australians' attitudes on the use of artificial intelligence, Human Rights Commissioner Edward Santow has urged the government to overhaul its approach to the emerging technology to avoid another scandal like the "robo-debt" scheme that unlawfully calculated and pursued debts from welfare recipients. Government agencies are increasingly relying on automated decisions rather than humans. Mr Santow said the people that tended to be least aware of the rise of automated decisions by government agencies like the Australian Taxation Office and Centrelink were the most disenfranchised in society. "That is really, really concerning to me. And that's because there's a real tendency, when new technology is being trialled, essentially to beta test it on some of the most vulnerable people," he said.

A recent study of 2,360 data science students, academics, and professionals by software firm Anaconda states that only 15% of institute and college professors said that they're teaching AI ethics, and just 18% of students specified that they're learning about the subject. We live in a world that is engulfed in data. This data is that which gives sustenance to Artificial Intelligence (AI). In the 21st century, AI has become an essential part of the technology industry. AI techniques have gone through a revival after simultaneous advances in computer power, data that were flooded in and its theoretical understanding.

Just two weeks ago Facebook settled a lawsuit alleging violations of privacy laws in Illinois (for the considerable sum of $550 million). Now controversial startup Clearview AI, which has gleefully admitted to scraping and analyzing the data of millions, is the target of a new lawsuit citing similar violations. Clearview made waves earlier this year with a business model seemingly predicated on wholesale abuse of public-facing data on Twitter, Facebook, Instagram and so on. If your face is visible to a web scraper or public API, Clearview either has it or wants it and will be submitting it for analysis by facial recognition systems. Just one problem: That's illegal in Illinois, and you ignore this to your peril, as Facebook found.

We all know that cryptocurrencies are the only way to do anonymous transaction. Either it is FBI or NIA, they find it impossible to track the record of transaction made through cryptocurrency. Every technology geek in today's time have tried to understand the concept of cryptocurrency due to its growing popularity. In many countries, the cryptocurrency is banned but still it a trending currency in the cyber criminal underground world. But do you wonder what makes it trending in the underground?

Get an overview of the ever-changing face of Data privacy laws, and the prevalent loopholes that exist. Aren Carpenter casts a light on the importance of adaptability within data privacy regulation as he showcases notorious misses of the wide net governing bodies throw over the handling of personal and private data. Aren's article delves into the evolution of data privacy laws from HIPAA (1996) Omnibus Final Rule (2013) to the more current GDPR. Aren makes statements that allude to the ambiguity caused by the blurred lines and vague privacy laws that make it difficult for an organisation to navigate patient privacy boundaries. Nonetheless, the first section of this article illustrates an effort by government and regulating bodies to update data privacy laws in synchronicity with the advancement of technology and information gathering.

By Kashmir Hill Floyd Abrams, one of the most prominent First Amendment lawyers in the country, has a new client: the facial recognition company Clearview AI. Litigation against the startup "has the potential of leading to a major decision about the interrelationship between privacy claims and First Amendment defenses in the 21st century," Abrams said in a phone interview. He said the underlying legal questions could one day reach the Supreme Court. Clearview AI has scraped billions of photos from the internet, including from platforms like LinkedIn and Instagram, and sells access to the resulting database to law enforcement agencies. When an officer uploads a photo or a video image containing a person's face, the app tries to match the likeness and provides other photos of that person that can be found online.

We've been hearing about the potential impacts Artificial Intelligence (AI) could have on the legal profession for several years now. Naysayers warn of waves of legal administration job losses and that IBM's Watson could supplant lawyers. Proponents laud AI's ability to transform the legal industry to a model of effectiveness and profitability never seen before. Neither side provides clear and direct evidence of how these things might actually come to pass. The AI discussion outside of legal operates on the same basic premises, but at a macro level.

AI (Artificial Intelligence) is the branch of Information Technology that develops self-adapting algorithms. Usually a program evaluates a result based on the input in a fixed way, like a mathematical formula. A Machine Learning algorithm can "adapt" itself and change its behaviour based on the result. A fine example of "data driven decisions" is suggesting you the best ads on your Facebook timeline based on what sites you visited in past days or something you might say near your phone (just saying). Allow me to make a few examples of the AI applications that could potentially mine the creativity industry.