Australian authorities say they are investigating whether Facebook breached the country's privacy law when personal information of more than 300,000 Australian users was obtained by Cambridge Analytica, a Trump-linked political consulting firm, without their authorization.

Police said Thursday they recorded 68 illegal drone flight incidents in 2017, almost double the previous year's 36, at a time when delivery services and other businesses are looking to utilize unmanned aircraft. Authorities took action against 77 people, up from 37 the year before, the National Police Agency said, indicating a need to improve public awareness of regulations. "It seems (the regulations) are not widely known," an NPA official said, suggesting the need to improve public awareness. The aviation law bans drone flights in airspace around airports and above densely populated areas. Drones are restricted to flying in daylight hours and need to be monitored at all times.

When people imagine a future of "robot lawyers," they tend to focus on employment and economic effects -- for example, the implications for the lawyer labor market (e.g., "will the robots take our jobs"). Often overlooked, but no less important, are the ethical implications of artificial intelligence. Last week, at the Global Privacy Summit of the International Association of Privacy Professionals (IAPP), I attended a session tackling this very subject -- "Machines That Can Learn: Can They Also Be Taught Human Values?" -- with the following panelists: Mark MacCarthy of the SIAA opened by noting the significance of the subject. Machine learning has been described as "the most important general-purpose technology of our era." Taken as a whole, it will improve human civilization, promoting justice and prosperity.

Technology used by Nasa for studying faint stars and galaxies could be used to trap poachers in a rare collaboration between astronomers and ecologists. The system combines flying drones, infra-red thermal imaging, and artificial intelligence to monitor animals at night when most poaching occurs. In an early field trial in South Africa, it was used to detect elusive riverine rabbits, one of the most endangered mammals in the world. Researchers hope in future the heat-sensitive drones will make it easier to track animals such as rhinos and spot poachers hunting them under cover of darkness. Technology for studying faint stars and galaxies could be used to trap poachers in a rare collaboration between astronomers and ecologists.

Be careful which words you feed into that machine-learning software you're building, and how. A study of news articles and books written during the 20th and 21st century has shown that not only are gender and ethnic stereotypes woven into our language, but that algorithms commonly used to train code can end up unexpectedly baking these biases into AI models. Basically, no one wants to see tomorrow's software picking up yesterday's racism and sexism. A paper published in the Proceedings of the US National Academy of Sciences on Tuesday describes how word embeddings, a common set of techniques used by machine-leaning applications to develop associations between words, can pick up social attitudes towards men and women, and people of different ethnicities, from old articles and novels. In word-embedding models, an algorithm converts each word into a mathematical vector and maps it to a latent space.

This master's thesis discusses an important issue regarding how algorithmic decision making (ADM) is used in crime forecasting. In America forecasting tools are widely used by judiciary systems for making decisions about risk offenders based on criminal justice for risk offenders. By making use of such tools, the judiciary relies on ADM in order to make error free judgement on offenders. For this purpose, one of the quality measures for machine learning techniques which is widly used, the $AUC$ (area under curve), is compared to and contrasted for results with the $PPV_k$ (positive predictive value). Keeping in view the criticality of judgement along with a high dependency on tools offering ADM, it is necessary to evaluate risk tools that aid in decision making based on algorithms. In this methodology, such an evaluation is conducted by implementing a common machine learning approach called binary classifier, as it determines the binary outcome of the underlying juristic question. This thesis showed that the $PPV_k$ (positive predictive value) technique models the decision of judges much better than the $AUC$. Therefore, this research has investigated whether there exists a classifier for which the $PPV_k$ deviates from $AUC$ by a large proportion. It could be shown that the deviation can rise up to 0.75. In order to test this deviation on an already in used Classifier, data from the fourth generation risk assement tool COMPAS was used. The result were were quite alarming as the two measures derivate from each other by 0.48. In this study, the risk assessment evaluation of the forecasting tools was successfully conducted, carefully reviewed and examined. Additionally, it is also discussed whether such systems used for the purpose of making decisions should be socially accepted or not.

Opaque and potentially biased mathematical models are remaking our lives--and neither the companies responsible for developing them nor the government is interested in addressing the problem. This week a group of researchers, together with the American Civil Liberties Union, launched an effort to identify and highlight algorithmic bias. The AI Now initiative was announced at an event held at MIT to discuss what many experts see as a growing challenge. Algorithmic bias is shaping up to be a major societal issue at a critical moment in the evolution of machine learning and AI. If the bias lurking inside the algorithms that make ever-more-important decisions goes unrecognized and unchecked, it could have serious negative consequences, especially for poorer communities and minorities. The eventual outcry might also stymie the progress of an incredibly useful technology (see "Inspecting Algorithms for Bias").

Despite its widespread adoption, Artificial Intelligence still has a long way to go in terms of diversity and inclusion. We try to write a lot about the positive ways in which Artificial Intelligence and other technologies are impacting our world. It's a subject close to our hearts as a company, and quite frankly, something that should be celebrated and shouted about given all the doom and gloom we're so often bombarded with in today's media. From healthcare, and sustainable cities, to climate change and industry, investment in AI is making an impact in many areas. Applications of machine learning and deep learning help shape the trajectories of our daily lives, so much so that we are barely even aware of it.

Residents of Shenzhen don't dare jaywalk. Since April 2017, this city in China's Guangdong province has deployed a rather intense technique to deter jaywalking. Anyone who crosses against the light will find their face, name, and part of their government ID number displayed on a large LED screen above the intersection, thanks to facial recognition devices all over the city. If that feels invasive, you don't even know the half of it. Now, Motherboard reports that a Chinese artificial intelligence company is partnering the system with mobile carriers, so that offenders receive a text message with a fine as soon as they are caught.

Authorities in New Jersey arrested a man accused of kidnapping and murdering a 20-year-old trying to buy a PlayStation video game console advertised online, reports said Saturday. Rufus Thompson, 29, was arrested Saturday morning in Trenton and was charged with murder, felony murder, robbery, kidnapping and weapons offenses in the death of Danny Diaz-Delgado. The victim's body was discovered March 24 near the banks of Assunpink Creek in Hamilton Township, the Trentonian reported. Investigators said Diaz-Delgado had been shot at least seven times, with wounds in his head, torso and one leg. His hands were tied behind his back and tape was wrapped around his face.