Immuta and the Future of Privacy Forum (FPF) today released a working white paper, Data Protection by Process: How to Operationalise Data Protection by Design for Machine Learning, that provides guidance on embedding data protection principles within the life cycle of a machine learning model. Data Protection by Design (DPbD) is a core data protection requirement introduced in Article 25 of The General Data Protection Regulation (GDPR). In the machine learning context, this obligation requires engineers to integrate data protection and privacy measures from the very beginning of a new ML model's life cycle and then take them into account at every stage throughout the process. The requirement has frequently been criticized for being vague and difficult to implement in practice. The paper, co-authored by Sophie Stalla-Bourdillion of Immuta, Alfred Rossi of Immuta, and Gabriela Zanfir-Fortuna of FPF, provides clear instructions on how to fulfill the DPbD obligation and how to build a DPbD strategy in line with data protection principles.

Immuta, the leading provider of enterprise data management solutions for artificial intelligence (AI), today announced a new customer relationship with Cognoa, which provides an AI-based solution for pediatric behavioral health diagnostics and digital therapies. Cognoa utilizes Immuta's platform to ensure data access policies are consistently and accurately enforced across a wide variety of data sources and users driving their machine learning programs. Palo Alto, CA-based Cognoa trains algorithms to aid in the diagnosis of behavioral health conditions, including autism and ADHD, with highly sensitive data from a production database which lives in a HIPAA environment. Data privacy and security concerns are paramount for the company, and Cognoa needed a platform that would enforce data access roles, permissions, and policies beyond the standard resource or table-based control levels. Halim Abbas, Chief AI Officer, Cognoa "We have a group of very talented data scientists who build our run-time engine for diagnostics software. Our legacy practice of providing them with all of the data they required to build models, while removing the ePHI and HIPAA sensitive information, was extremely time and labor intensive. It was essential to expedite this process, and to also continue to anonymize sensitive information for reporting."

In it, we will spotlight the leaders in each sector, which include enterprise software, hardware, security, on-premises-based systems and cloud services. We also will add promising new companies as they come into the market. Company Description: Immuta is a fast and efficient way for algorithm-driven enterprises to accelerate the development and control of machine learning and advanced analytics. The company's data management platform provides data scientists with rapid, personalized data access to improve the creation, deployment and auditability of machine learning and AI. Founded in 2014, Immuta is a privately-held company led by co-founder and CEO Matt Carroll and headquartered in College Park, Md.

Immuta today announced that it has been named in Gartners report, Cool Vendors in Data Science and Machine Learning. Immuta provides the industrys only enterprise data management platform for artificial intelligence (AI), enabling organizations to easily operationalize data for increased access, control and visibility to drive their machine learning and artificial intelligence (AI) programs. In the report, Gartner finds that attention and emphasis on data management and governance around machine learning have intensified. Mature data science teams are looking for functionality around data lineage, privacy, risk management and access control. Gartner recommends that data management and governance becomes a point of emphasis within data science teams, and provide support through specialized technologies.

Almost no one knows how to utilize the technology at scale. More precisely, only a very small handful of organizations truly understand how to manage the risks of machine learning (ML) when implemented widely. Those risks include navigating the legal, reputational, and ethical issues ML can create – from wildly offensive chatbots and image classifiers to furthering racial disparities amongst zip codes, and much, much more. And that's not even taking into account the deceptively complex requirement of being able to predict how ML models will behave over long periods of time, or new laws like the EU's GDPR and their impact on ML. That's why we're thrilled to partner with the Future of Privacy Forum to release the first-ever guide to managing risk in machine learning, written specifically for practitioners.

Always an issue with complex models, especially ones that are not very transparent. We always did risk models in parallel. Managing risk in machine learning models The O'Reilly Data Show Podcast: Andrew Burt and Steven Touw on how companies can manage models they cannot fully explain. Hurry--early price ends July 27. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI.

"We see a really deep and pressing need for guidelines and for an actual framework to measure risk for machine learning," says Andrew Burt, chief privacy officer at Immuta and one of the paper's authors. In the paper, released Tuesday, they offer some guidance to companies thinking about these issues. Among their suggestions, inspired in part by a 2011 Federal Reserve document on handling financial model risk, is that companies set up three "lines of defense" in handling artificial intelligence risk. Those should include data scientists and other experts defining exact assumptions and goals around a project; a second team of data and legal experts who work as "validators" and review assumptions, methods, documentation, and information on underlying data quality; and a regular third line of defense involving reviews of the overall assumptions around the model and how they're working out. FPF & Immuta – How can we govern a technology its creators can't fully explain?

Check out Andrew Burt's talk "Beyond Explainability: Regulating Machine Learning In Practice" at the Strata Data Conference in New York, September 11-13, 2018. Hurry--early price ends July 27. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer at Immuta, and Steven Touw, co-founder and CTO of Immuta.

There's no doubt about it: The future will be machine driven, and central to this future are the advanced algorithms, which are fueled by the data they're trained on. Every ad you see, every car driving itself, every medical diagnosis provided by a machine will be based on your data – and lots of it. Without your data, we inherit a world without machine learning, and most would argue that companies without machine learning will fail. At least that's where we're heading; it sounds like a big problem, and it is. Concepts around "big data" are completely incompatible with how people expect their data to be protected and how laws are shaping those protections.

Immuta Inc., the data management for AI vendor, said it will use a $20 million funding round to establish a European beachhead in London as it sharpens its focus on helping enterprises develop "ethical" AI algorithms that comply with new European privacy rules as well as possible U.S. data privacy regulations. The Series B funding round announced Wednesday (June 20) was led by DFJ Growth and joined by new investors Citi Ventures and Dell Technologies Capital. The startup's customers include banks such as Barclays (NASDAQ: DTYS), insurers and U.S. intelligence agencies. Immuta's data management platform is designed to provide greater control of the data fed into algorithms, speeding deployment as well as increasing visibility into how automation tools are functioning. The goals include "controls around data science" Immuta CEO Matthew Carroll said in an interview, along with helping enterprises get a handle on what he called "analytics ethics."