Neil Raden

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Neil Raden, based in Santa Fe, NM, is an active industry analyst, consultant and widely published author and speaker and also the founder of Hired Brains Research. Hired Brains provides thought leadership, context and advisory consulting and implementation services in Information Management, Analytics/ Data Science, Machine Learning/AI and IoT for clients worldwide. Hired Brains also provides consulting, market research, product marketing and advisory services to the software industry. Neil is the co-author of Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions, Prentice-Hall. He welcomes your comments at nraden@hiredbrains.com.

Neil Raden

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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.

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AI ethics - why teaching ethics and "ethics training" is problematic

We've been trying to teach "ethics" for years. Teaching AI ethics to organizations is proving to be just as problematic. Yet as the urgency of ethical AI increases, we need a way forward. What are the options?

AI ethics robot teaches Asimov's three laws © Andrey Suslov - shutterstock

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Rethinking AI Ethics - Asimov has a lot to answer for

Is the current obsession with AI Ethics doing any good? Maybe Asimov's Three Laws of Robotics wasn't such a great starting point after all

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AI readiness isn’t just a technology issue – ethics matter too

With the possibility of serious negative consequences springing directly from AI projects, there needs to be more focus and discussion around ensuring ethical standards are upheld.

Hand soap in supermarket during COVID-19 by @philww 740px.jpg

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Wrangling complex supply chains with Chaos Theory

Complex supply chains are inherently chaotic, which means demand planners need to understand Chaos Theory and the math behind it to unravel what's going on

Stethoscope and woman's hands using laptop computer - Photo by National Cancer Institute on Unsplash

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AI in healthcare - will it help or just make things worse?

Those who laud the potential benefits of AI in healthcare are too often silent on the risk of exacerbating the healthcare system's current failings

Brain in an open storage jar © ktsdesign - Fotolia.com

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Artificial General Intelligence will not resemble human intelligence

Airplanes don't flap their wings like birds, and artificial general intelligence (AGI) will never think like the human brain, which is more complex than we imagine

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How can we measure fairness beyond bias, discrimination and other undesirable effects in AI?

The question of AI ethics and bias remains a potent one - but are we framing these issues in the right way? A better approach would be centered around AI fairness. But can fairness be monitored?

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AI explainability and interpretability - we have a long way to go

AI explainability remains an important preoccupation - enough so to earn the shiny acronym of XAI. There are notable developments in AI explainability and interpretability to assess. How much progress have we made?

Man leaping across gap in rocks against sunburst on blue sky © GIS - Fotolia.com

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Still rife with ethical issues, data sharing has a bright side too

Data brokers and personal data collection continues to cross ethical lines. But there are bright spots - including supply chain data sharing startup Aperity. I talked with their CEO about how their approach is different, and why AI and machine learning play a crucial data processing role.

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When model drift becomes a deluge - the Coronavirus pandemic wreaks havoc with data science and ML models

All predictive models are wrong - but some are very wrong. How did we wind up in the predicament of flawed Machine Learning models, just when we need them the most?

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The technical realities of functional quantum computers - is Google’s ten-year plan for Quantum Computing viable?

Google's quantum computing exploits have garnered plenty of attention in recent months. But how might they apply this commercially? A contrasting technical view comes by way of Itamar Sivan, CEO of Quantum Machines.