FPF and Immuta released the first-ever framework for practitioners to manage risk in artificial intelligence and machine learning models. The joint whitepaper, Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models, provides business executives, data scientists, and compliance professionals with a strategic guide for governing the legal, privacy, and ethical risks associated with this technology. Lately a lot of thought, work and advocacy has been going into looking at personal data as a fungible commodity: one that can be made scarce and bought, sold, traded and so on. Good though this might be, it also steers attention away from a far more important issue it would be best to solve first: personal agency. The U.S. has generally approached privacy rules on a sector-by-sector basis, meaning the health care industry has different privacy standards than the financial industry.

By now, Machine Learning is soundly in the public domain as its wide impact is being felt across many industries around the world as they go through digital transformations. Although the spearheading ML applications have come from the usual suspects such as Internet companies and software firms, the waves of automation and data-driven decision making have been recently crushing on the shores of the Legal Services industry (article in Spanish). A typical law firm in the Western world employs tens or even hundreds of attorneys specializing in different practice areas e.g., intellectual property, corporate, civil, criminal, constitutional law. The business of legal services remains perhaps the very definition of a human-driven industry essentially relying on increasing the employee count to be able to scale to higher revenues. Such growth no doubt may present some efficiencies, but there's no evidence of strong network effects letting few players dominate the market.

Unless you've been hiding under a rock, you've probably heard of the Cambridge Analytica Scandal and Mark Zuckerberg's statements about the worldwide changes Facebook is making in response to European Union's General Data Protection Regulation (GDPR). If your business is not yet in Europe, you may be taken aback by the statement from U.S. Senator Brian Schatz that "all tech platforms ought to adopt the EU approach to (data protection)." This, despite the fact that 45% of U.S. citizens think that there is already "too much" government regulation of business and industry. So yes, GDPR is a big deal indeed. When it became the law in European Union on May 25, 2018, it improved data protection for EU citizens dealing with companies not only in Europe but all around the world.

Rubio noted that in 2009, the U.S. Embassy in Moscow complained to the Russian Foreign Ministry about a fake sex video it said was made to damage the reputation of a U.S. diplomat. The video showed the married diplomat, who was a liaison to Russian religious and human rights groups, making telephone calls on a dark street. The video then showed the diplomat in his hotel room, scenes that apparently were shot with a hidden camera. Later, the video appeared to show a man and a woman having sex in the same room with the lights off, although it was not at all clear that the man was the diplomat.

Artificial Intelligence (AI), defined as the capability of machines to imitate intelligent human behavior and learn from data, is considered by many to be the final frontier of computing. And environmentalists and tech companies are now harnessing the power of AI to service to the environment. To wit, Microsoft announced in December 2017 that it is expanding its "AI for Earth" program and committing $50 million over the next five years to put AI technologies in the hands of individuals and organizations working to solve global environmental challenges, including climate change as well as water, agriculture and biodiversity issues. Lucas Joppa, Microsoft's first Chief Environmental Scientist, is convinced that AI is now mature enough and the global environmental crisis acute enough to justify the creation of an AI platform for the planet. "I believe that for every environmental problem, governments, non-profits, academia and the technology industry need to ask two questions: 'How can AI help solve this?' and'How can we facilitate the application of AI?'," Joppa said.

Discrimination via algorithmic decision making has received considerable attention. Prior work largely focuses on defining conditions for fairness, but does not define satisfactory measures of algorithmic unfairness. In this paper, we focus on the following question: Given two unfair algorithms, how should we determine which of the two is more unfair? Our core idea is to use existing inequality indices from economics to measure how unequally the outcomes of an algorithm benefit different individuals or groups in a population. Our work offers a justified and general framework to compare and contrast the (un)fairness of algorithmic predictors. This unifying approach enables us to quantify unfairness both at the individual and the group level. Further, our work reveals overlooked tradeoffs between different fairness notions: using our proposed measures, the overall individual-level unfairness of an algorithm can be decomposed into a between-group and a within-group component. Earlier methods are typically designed to tackle only between-group unfairness, which may be justified for legal or other reasons. However, we demonstrate that minimizing exclusively the between-group component may, in fact, increase the within-group, and hence the overall unfairness. We characterize and illustrate the tradeoffs between our measures of (un)fairness and the prediction accuracy.

As concerns about unfairness and discrimination in "black box" machine learning systems rise, a legal "right to an explanation" has emerged as a compellingly attractive approach for challenge and redress. We outline recent debates on the limited provisions in European data protection law, and introduce and analyze newer explanation rights in French administrative law and the draft modernized Council of Europe Convention 108. While individual rights can be useful, in privacy law they have historically unreasonably burdened the average data subject. "Meaningful information" about algorithmic logics is more technically possible than commonly thought, but this exacerbates a new "transparency fallacy"---an illusion of remedy rather than anything substantively helpful. While rights-based approaches deserve a firm place in the toolbox, other forms of governance, such as impact assessments, "soft law," judicial review, and model repositories deserve more attention, alongside catalyzing agencies acting for users to control algorithmic system design.

The new digital arms race has begun and revolves around artificial intelligence (AI) and machine learning (ML). Companies are seeking to get an advantage on their competitors by gaining insights or automating outcomes using AI and ML. The General Data Protection Regulation (GDPR) from the European Union will force companies to re-examine their use of AI and ML when determining outcomes for European citizens and residents. GDPR does not explicitly reference AI and ML technologies. Article 22 within GDPR is titled "Automated individual decision-making, including profiling".

Elena Donio has been Chief Executive Officer of Axiom Global, Inc. since November 2016. Prior to this role, Donio served as President of Concur Technologies, Inc., from 2014 to 2016. She has also served as a Senior Manager at Deloitte & Touche and as a Senior Consultant at Andersen Consulting (Accenture). She holds BA in Economics from University of California, San Diego. Axiom is the global leading alternative legal services provider.

Artificial intelligence is changing the world of retirement planning. By using improved datasets and algorithms to efficiently deliver solutions tailored to people's needs, AI can help them save, invest and retire better. One of the hottest trends to emerge in this area in recent years is the use of robo-advisors. These are software programs that use the data supplied by clients to create and automatically manage their investment portfolios. They're gaining in popularity, but are they better than human advisors?