Fair representation learning provides an effective way of enforcing fairness constraints without compromising utility for downstream users.

Fair representation learning provides an effective way of enforcing fairness constraints without compromising utility for downstream users.

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The next article will focus on how to increase the transparency and'explainability' of AI systems in order to effectively remove bias within the data or the computational models – reducing the inherent risk in the process. To learn more, visit HPE.

The potential impact of the ongoing worldwide data explosion continues to excite the imagination. A 2018 report estimated that every second of every day, every person produces 1.7 MB of data on average--and annual data creation has more than doubled since then and is projected to more than double again by 2025. A report from McKinsey Global Institute estimates that skillful uses of big data could generate an additional $3 trillion in economic activity, enabling applications as diverse as self-driving cars, personalized health care, and traceable food supply chains. But adding all this data to the system is also creating confusion about how to find it, use it, manage it, and legally, securely, and efficiently share it. Where did a certain dataset come from?

Agile data governance improves data assets by iteratively capturing knowledge as data producers and consumers work collaboratively. "Our platform was built for a modern workforce where data-empowerment and data stewardship co-exist," said Jon Loyens, Chief Product Officer and co-founder of data.world. "Being able to access the data you need and collaborate remotely on critical datasets is more important than ever. Taking an agile approach to data governance addresses these needs by simplifying data requests, adding transparency to how data is being used internally, and automating workflows so the entire business can get more from its data." These new agile data governance capabilities mean organizations are no longer constrained by data silos, complicated workflows, or opaque data resources.

To effectively enforce fairness constraints one needs to define an appropriate notion of fairness and employ representation learning in order to impose this notion without compromising downstream utility for the data consumer. A desirable notion is individual fairness as it guarantees similar treatment for similar individuals. In this work, we introduce the first method which generalizes individual fairness to rich similarity notions via logical constraints while also enabling data consumers to obtain fairness certificates for their models. The key idea is to learn a representation that provably maps similar individuals to latent representations at most $\epsilon$ apart in $\ell_{\infty}$-distance, enabling data consumers to certify individual fairness by proving $\epsilon$-robustness of their classifier. Our experimental evaluation on six real-world datasets and a wide range of fairness constraints demonstrates that our approach is expressive enough to capture similarity notions beyond existing distance metrics while scaling to realistic use cases.

This ebook, based on the latest ZDNet / TechRepublic special feature, examines how driverless cars, trucks, semis, delivery vehicles, drones, and other UAVs are poised to unleash a new level of automation in the enterprise. We all know and love the cloud. What's not to love about not having to bother with what your own devices can do, and having near-infinite, elastic storage and compute power at your fingertips? In the end, as the aphorism goes, the cloud is just someone else's computer. Okay, it may be millions of computers, thoughtfully arranged in clusters in super efficient data centers -- but all those are someone else's computers.

Which countries are the top data producers? After all, with data-fueled applications of artificial intelligence projected, by McKinsey, to generate $13 trillion in new global economic activity by 2030, this could determine the next world order, much like the role that oil production has played in creating economic power players in the preceding century. While China and the U.S. could emerge as two AI superpowers, data sources can't be limited to concentrations in a few places as we have with an oil-driven economy -- it needs to be drawn from many, diverse sources and future AI applications will emerge from new and unexpected players. The new world order taking shape is likely to be more complex than a simple bi-polar structure, especially since data is being produced at a pace that boggles the mind. Building on our past work mapping the digital evolution and digital competitiveness of different countries around the world, we wanted to try to locate the deepest and widest pools of useful data. This is essential to run the myriad machine learning models critical to AI.