Neil Raden

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Neil Raden, based in Santa Fe, NM, is an industry analyst, consultant and widely published author and speaker with a background in algebraic topology and P&C actuarial, he founded Hired Brains Research in 1985 to provide thought leadership, context and advisory consulting and implementation services in Data Architecture, AI, Analytics/Data Science and organizational change for analytics for clients worldwide across many industries. Neil is a recognized authority on AI Ethics, the co-author of the first book on Decision Man agent, “Smart (Enough) Systems,” and the foundational report for the Society of Actuaries, “Ethical Use of Artificial Intelligence for Actuaries.” He welcomes your comments at [email protected]

Neil Raden

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How can Bayesien Inference support complex decisions? A practical guide to an overlooked approach

For decision makers grappling with data, Bayesian Networks are an overlooked asset. Affordable? Yes. Performance and applicability to edge devices? Yes again. Here's a practical guide to how Bayes Nets can solve enterprise problems.

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Can Bayesian Networks provide answers when Machine Learning comes up short? It's a question of probabilities

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and causality.

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In search of trustworthy AI - is the insurance industry using AI to fairwash FICO scores?

Does applying AI to insurance ratings and FICO scores improve fairness? The insurance industry says yes, but the data points to AI fairwashing. It's time for a change.

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Musing on Chief Revenue Officers - do we need them at all?

As a job title, Chief Revenue Officers are trending. But is CRO a needed role? And if so, how is it defined? Are there other ways to oversee revenue growth and accountability?

Health practitioner hold laptop on drawn chalkboard

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For John Snow Labs, doing good with NLP is in their DNA (and yours)

Too often, the AI startup field comes off as a VC-funded money grab. But commercial NLP healthcare player John Snow Labs is doing things differently. Their Spark NLP open source library includes support for 375 languages. Here's why their approach stands out.

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The problem with 'Ethics by Design' - why this WEF report gets AI Ethics wrong, and 25 techniques for producing trustworthy AI

A recent WEF report called "Ethics by Design" reinforces problematic misconceptions about AI ethics. Here's where I think the report went wrong - and my own personal 25 point list for avoiding AI project speed bumps.

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Metadata Shmedadata - today's approaches to metadata are inadequate, and that's a problem.

Struggling with metadata is nothing new, but there's a misconception that advanced data tech and cloud storage solved this problem. That's not the case - so what is the way forward?

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Data lakes, data lakehouses and cloud data warehouses - which is real?

Cloud data warehouses aren't trendy enough - now we evidently need data lakehouses as well. But how should enterprises sort these terms? And has the data lake outlived its usefulness?

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Moral licensing, AI teams, and you - a problem yet to be reckoned with

Most of us have heard of cognitive bias - and the problem it can pose. Less well known is the practice of moral licensing. But it's an issue AI teams need to consider.

Cartoon robots look at young man lined up for job interview © pathdoc - shutterstock

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Robot empowerment - a viable alternative to Asimov's three laws of robotics?

Human-robot interaction is upon us - we're in dire need of a framework that makes sense. Asimov's three laws of robotics are one model, but is it applicable to today's robots? An alternative based on robot "empowerment" is worth a close look.

Mainframe in a futuristic representation of a matrix code © vladimircaribb - Fotolia.com

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How supercomputers found their industry mojo - the evolution of high performance computing

Supercomputers used to be the domain of scientists and the military. Now the enterprise use cases are picking up steam. But in a way, all computers are supercomputers. Here's a look at how the field has evolved - and what's next.

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Friday rant - Facebook's disinformation spreading, ad-server-economy must go

Big Tech has been on the defensive lately, and for good reason. What was once perceived as a way to foster democracy has given way to algorithmic dystopia. But Facebook's algorithmic dangers are tied to an ad-server-based model we must dismantle. Rant time.

Man solving complex problem finds surreal keyhole to success © frankie's - shutterstock

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The problem of algorithmic opacity, or "What the heck is the algorithm doing?"

Opacity in AI used to be an academic problem - now it's everyone's problem. In this piece, I define the issues at stake, and how they tie into the ongoing discussion on AI ethics.