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

Privacy threat at work - laptop with eyes in screen © Juergen Faelchle - shutterstock

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AI didn't invent privacy abuse - how the history of data privacy informs our future

There is no AI Ethics without data privacy. But how do we account for new privacy legislation like GPDR and CPRA - and also the resistance to it? Here's how the regulation of privacy evolved.

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Statistical bias in context - AI didn't invent quantitative methods of bias

An effective approach to AI Ethics must reckon with bias, algorithmic discrimination, and privacy. These terms have a historical context that should be understood - if we want to deploy AI ethically. This time around, we delve into bias.

Human and robot shaking hands over stacked coins AI finance trust concept © Andrey_Popov - Shutterstock

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Trustworthy AI versus ethical AI - what's the difference, and why does it matter?

We all want "trustworthy AI" - or do we? A closer look at the semantics of trust indicate the dangers of assuming trust is ethical. Fuzzy terminology will not help our pursuit of ethical AI.

Code with brain artificial intelligence AIOps concept © Antonov Serg - shutterstock

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The US National Security Commission issues its "Final Report on AI in Defense and Intelligence" - here are the takeaways

The US National Security Commission has issued a massive, 700 page "final report" on the impact of AI on defense and intelligence. The report's conclusions are concerning, and not without controversy. US tech leaders were directly involved in this report - the proposed plan of action is worth a close look.

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The FTC takes steps to regulate AI abuses - but will it be effective?

A new FTC mandate is intended to crack down on AI abuses. How will it work? Are crucial areas of AI abuse overlooked? Here's my first cut analysis.

Silhouettes of people mental health teamwork and communication concept and connected lines with dots © GrAl - Shutterstock

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Is there good bias in AI, or is AI bias always undesirable? Responses to a LinkedIn debate

When it comes to eradicating AI bias, we assume that all bias is negative. But is that distinction correct? A recent LinkedIn debate brought up issues worth reckoning with.

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Is fairness in AI a practical possibility? A new angle on designing ethical systems

Is fairness in AI an (im)possibility? Not necessarily, but it requires a proper definition of fairness, in the context of systems design. Here's a fresh look into the decision-making constructs of AI fairness.

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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?

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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.