Loomis appealed the sentence, arguing that neither he nor the judge could examine the formula for the risk assessment as it was a trade secret. The state of Wisconsin countered that Northpointe required it to keep the algorithms confidential in order to protect the firm's intellectual property. Wisconsin's attorney general, Brad D. Schimel, even used the same argument that Loomis did, that judges do not have access to the algorithm either, although he seems to have spun it as a positive somehow. This is a bit like saying a game of chess is fairer if neither player knows the rules. Which is true, in a way, but it's unlikely to produce a game of chess, just two people throwing pieces round a board, which will result in no winners in the traditional sense.

By now you probably know that the people who make our clothes, chocolate, and diamond rings often suffer in the process. With growing consumer consciousness and stricter regulations putting pressure on companies to clean up their act, one company has devised a machine learning system it says will sift through data and locate forced labor in the manufacturing process. SAP Ariba, a for-profit software and IT company based in California, works with millions of large and small-scale companies to streamline their supply chain, the system through which their products are sourced, made or delivered. It works with companies that deal with fashion and food, but also technology. "The fact that every country in the world has made forced labor illegal, the acknowledgement alone has made companies realize they cannot take this lightly," said Padmini Ranganathan, the vice president of products and innovation at Ariba.

To start, it's pouring more energy into machine learning research that could improve its ability to automatically flag and remove terrorist videos while keeping innocently-posted clips (say, news reports) online. It's also expanding its counter-radicalization system, which shows anti-extremist ads to would-be terrorist recruits. Google plans to "greatly increase" the number of humans in its YouTube Trusted Flagger program, improving the chances that it'll catch terrorist material. Google wants to tackle those YouTube videos that are borderline, too -- if it spots videos with "inflammatory" religious or supremacist material, it'll put those clips behind a warning and prevent them from getting ad revenue, comments or viewing recommendations.

To start, it's pouring more energy into machine learning research that could improve its ability to automatically flag and remove terrorist videos while keeping innocently-posted clips (say, news reports) online. It's also expanding its counter-radicalization system, which shows anti-extremist ads to would-be terrorist recruits. Google plans to "greatly increase" the number of humans in its YouTube Trusted Flagger program, improving the chances that it'll catch terrorist material. Google wants to tackle those YouTube videos that are borderline, too -- if it spots videos with "inflammatory" religious or supremacist material, it'll put those clips behind a warning and prevent them from getting ad revenue, comments or viewing recommendations.

As artificial intelligence proliferates, companies and governments are aggregating enormous data sets to feed their AI initiatives. Although privacy is not a new concept in computing, the growth of aggregated data magnifies privacy challenges and leads to extreme ethical risks such as unintentionally building biased AI systems, among many others. Privacy and artificial intelligence are both complex topics. There are no easy or simple answers because solutions lie at the shifting and conflicted intersection of technology, commercial profit, public policy, and even individual and cultural attitudes. Given this complexity, I invited two brilliant people to share their thoughts in a CXOTALK conversation on privacy and AI.

It's safe to say that most sectors will undergo significant disruption as a result of artificial intelligence (AI) technology. AI will not only disrupt our business models but it will also disrupt our legal framework for the creation and exploitation of intellectual property (IP) rights, giving rise to new IP challenges for those seeking to develop and deploy new AI systems. With AI systems already being used to generate content capable of attracting IP protection, working out exactly who owns the IP rights in this content will become increasingly important, especially when it comes to licensing or enforcing those rights. When an AI system is used by a human solely as a tool for creating a work, the same human using the system will generally be considered "the author". But what happens when the AI system is more involved in creating the work, e.g. it is given a few simple inputs and the system goes on to create something which is much more than the sum of the inputs?

We present a model for random simple graphs with a degree distribution that obeys a power law (i.e., is heavy-tailed). To attain this behavior, the edge probabilities in the graph are constructed from Bertoin-Fujita-Roynette-Yor (BFRY) random variables, which have been recently utilized in Bayesian statistics for the construction of power law models in several applications. Our construction readily extends to capture the structure of latent factors, similarly to stochastic blockmodels, while maintaining its power law degree distribution. The BFRY random variables are well approximated by gamma random variables in a variational Bayesian inference routine, which we apply to several network datasets for which power law degree distributions are a natural assumption. By learning the parameters of the BFRY distribution via probabilistic inference, we are able to automatically select the appropriate power law behavior from the data. In order to further scale our inference procedure, we adopt stochastic gradient ascent routines where the gradients are computed on minibatches (i.e., subsets) of the edges in the graph.

Recently, I had the honor of speaking with a number of the world's most influential thought-leaders in the fields of data science, data analytics, machine learning and digital transformation. This group of prominent data technologists was more than happy to answer a wide variety of question on topics ranging from the fast-evolving area of unified governance and preparing for General Data Protection Regulations (GDPR) to transformative hybrid data management technologies, and of course, data science and machine learning. You'll learn more about all of the topics discussed here during the main event, breakout sessions and demos, plus have the opportunity to join in the conversation and connect with these renowned data pioneers who can help you understand how to build a data-driven strategy to outsmart your competition. There will be an additional opportunity to chat with a number of these thought-leaders, as well as fellow data enthusiasts, at the Fast Track Your Data CrowdChat on Tuesday, June 20th, 2017 at 1:00 PM (EDT). Now let's meet our panel of experts: His latest book is Leading Innovation: Building a Scalable, Innovative Organization. She is also on the faculty of the Data Science Graduate Program at UC Berkeley, the Data Analytics MS Advisory Board at CUNY SPS, and the Data Science Committee for the Grace Hopper Conference. Ronald Van Loon, Director Adversitement, where he is helping data-driven companies generate business value as a globally recognized Top 10 Big Data, Data Science, IoT, and BI Influencer. Aylee Nielsen: Thank you all so much for joining me today, I'd like to start off with questions on a subject matter that I know you are all very familiar with – Data Science and Machine Learning.

When doing legal research, most lawyers first must figure out what cases are relevant and which precedents judges still enforce. Case-law website Judicata on Wednesday unveiled a new color-coding system to help researchers find which cases to cite and which to avoid. Using machine learning to analyze cases, as well as human editors to check the work, Judicata utilizes a system reminiscent of a law student's highlighted textbook. Background colors indicate the strength of a particular case, its relevance to a user's case, and whether its holding is still good law or has been disputed or even overturned. The color-coding process was quite time consuming.

Exercising good judgement in difficult situations is a much tougher standard. Above all, ethics must be realistic, and in our real world, bad things happen. Abe is very specific: he means "biased" in a technical, statistical sense. Cathy O'Neil has frequently argued that secret algorithms and secret data models are the real danger.