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 nraden@hiredbrains.com.

Neil Raden

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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.

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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.

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Can fairness be automated with AI? A deeper look at an essential debate

I've addressed whether fairness can be measured - but can it be automated? These are central questions as we contend with the real world consequences of algorithmic bias.

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Can we measure fairness? A fresh look at a critical AI debate

By now, most AI practitioners acknowledge the universal prevalence of bias, and the problem of bias in AI modeling. But what about fairness? Can fairness be measured via quantifiable metrics? Some say no - but this is where the debate gets interesting.

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Musings on China's 'Global Initiative on Data Security' and the problem of security "back doors"

A review of 'Global Initiative on Data Security' led me to an exchange with a company doing business in China. With new 5G security issues on the horizon, it's a good time to reflect on the implications of "back doors," ethical AI, and where the responsibility lies.

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The ups and downs of cognitive computing, from Watson to Amelia

IBM Watson's health initiative underscored the limitations of applied cognitive computing. But can a fresh wave of cognitive, conversational AI solutions like Amelia from IPsoft succeed with a different type of offering?

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Revisiting ethical AI, part two - on data management, privacy, and the misunderstood topic of bias

No, you can't program your AI for empathy or ethics. But you can certainly confront the problem of bias. In part two of revisiting AI ethics, we examine how bias, data management, and privacy should be addressed.

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Revisiting ethical AI - where do organizations need to go next?

AI ethics are having a hard time keeping up with AI. Academic debates may be interesting, but organizations need a practical AI ethics framework. Where do we go from here?

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Does small data provide sharper insights than big data? Keeping an eye open, and an open mind

Big data gets all the hype. Small data is perceived as inadequate for today's in vogue algorithms. But by overlooking small data, are enterprises missing a superior source of insight?

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The fragility of privacy - can differential privacy help with a probabilistic approach?

Enterprises crave personalized data, but protecting privacy is non-negotiable. Anonymizing the data brings limitations. Can differential privacy help?

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AI inevitability - can we separate bias from AI innovation?

AI evangelists pay lip service to solving AI bias - perhaps through better algorithms or other computational means. But is this viable? Is bias in AI inevitable?

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The explainability problem - can new approaches pry open the AI black box?

Explainability has moved from an academic debate to a significant barrier to AI adoption. A slew of new tools and approaches are intended to address this problem - but will they close the explainability gap?

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Unethical AI unfairly impacts protected classes - and everybody else as well

We've established that unethical AI hurts protected classes - but it doesn't stop there. Across industries and regions, unethical AI can impact the entire population. Here's some questions to consider.