So the video was created (yes, Yoast, I’m intentionally using passive), it was incorporated into the post, and the post was scheduled for 9 am Pacific. Since I had a few minutes to kill, I uploaded the video to a bunch of social platforms.
Only then did I notice a teeny tiny problem with the video. You’ll spot it instantly…but I didn’t.
Take 1.
As a responsible and highly respected content creator, I should have delayed the blog post until we fixed the typo, and I should have deleted all the social posts.
Admission
But I didn’t.
I left my mistake out there for all the world to see, and even posted an image admitting the mistake.
Corrosion transparency.
Oh, and I edited the caption on my 9 am post to acknowledge the typo.
I realize that this entire episode makes me appear extremely unprofessional. I should have covered up my mistake so few if any saw it. (Actually I should have re-read the original video before I posted it, but that’s another story.)
But there are two reasons why I did what I did. One is honorable. One is less so.
The honorable reason is to highlight Bredemarket’s transparency. I don’t hide things from my clients. If something goes bad, I admit it. And if I can’t fix the original goof, I take steps to ensure it doesn’t happen again.
The less honorable reason? To take advantage of human nature. The guaranteed way to maximize engagement is to get something wrong. Then everyone will pile on to correct you.
And to make sure everyone knows about it. Look at what Bredebot and I have shared over the last hour and a half:
The original mistake.
Admission of the mistake.
The corrected version.
This post explaining everything.
My views may go through the roof.
And some firms will never hire Bredemarket because of its lack of professionalism. But these companies weee turned off years ago when the first iguana appeared here.
On the other hand, some forms will hire Bredemarket because of the transparency.
Considering the right consultant for your biometric firm often comes down to a single, make-or-break dynamic: biometric domain fluency.
But that’s just a fancy phrase for knowing what you’re talking about.
“The Longest Hour.” Google Lyria. Public Domain.
Consider what happens when a product marketing consultant at a biometric firm does NOT possess biometric domain fluency.
Off-target content
Imagine that you’re writing your first blog post for a biometric company. So you figure you’ll get educational by telling your prospects how “Gender Shades” identified huge accuracy problems, but your firm’s facial recognition algorithm has overcome them.
Google Gemini.
Congratulations, novice. You just mixed two distinctly different things up.
I’ve addressed this before, but I’ll address it again. Do you know why that old study was called “Gender Shades”? Because it tested algorithmic ability to recognize gender and race. That is facial ANALYSIS (or categorization), not facial RECOGNITION (or individualization). The National Institute of Standards and Technology (NIST) draws a clear distinction between the two. And if you use a high-quality facial recognition application, the gallery of possible matching candidates may have a white woman next to a black man…because the facial recognition algorithm DOESNOTCARE about gender or race.
And that’s just one example of how someone who doesn’t know biometrics can make a grievous mistake that repels a firm’s prospects.
Losing proposals
Before I launch into this, let me explain that there are a number of reasons why a firm may lose a proposal.
Not submitting your proposal in Times New Roman 12 font with one-inch margins.
Submitting a 16-page proposal when the page limit is 15 pages.
Submitting your proposal Friday at 5:03 pm when it was due at 5 pm.
Here I want to talk about something different: getting the answers wrong.
Let’s say you’re tasked to answer a question about submitting your fingerprints at 500 pixels per inch (ppi). But you want to get some extra evaluation points, so you write this:
“The Super Duper Ridger fingerprint reader EXCEEDS THE REQUIREMENT by capturing fingerprints at 1000 ppi.”
Granted, latent fingerprint examiners LOVE higher resolution tenprints and latent prints where they can see the finer detail. But for everyone else, high resolution prints can potentially cause huge problems. Longer time to display images on the screen. Longer network transmission time and higher network bandwidth. Oh, and four times as much storage space. If the agency specifies 500 ppi, they mean what they say.
You have to know your biometrics to answer the questions properly.
Flawed analysis
Sometimes, product marketing consultants need to strategically analyze the market. Or the products in the market. Or the competitors who sell the products in the market. Analysis is a minefield in which an inexperienced person can get blown up.
Let’s say you’re asked to identify the leading facial recognition algorithm providers for U.S. intelligence community agencies. Now you, being a smart person who knows the difference between face recognition and face analysis, hone in on NIST’s Face Recognition Vendor Test (FRTE) collection. You go to FRTE 1:N, identify the specific test that best meets the needs of the intelligence community, and sort the list with the most accurate algorithms at the top. You then report results for all the leading vendors…including CloudWalk, Megvii, and SenseTime.
Google Gemini or Imagen; I can’t remember which.
Congratulations, novice. The firm that contracted with you will NEVER get intelligence business.
All three of the firms I mentioned are Chinese firms. Just because NIST or the FBI tests an algorithm doesn’t mean that U.S. agencies will buy it. For legal and practical reasons, the CIA is not going to install a Chinese algorithm on one of its systems.
No point in performing a competitive analysis on firms that will never compete in your markets.
Avoid these problems by contracting biometric domain fluency
You can avoid these problems by contracting with a product marketing consultant who offers biometric domain fluency.
Such as Bredemarket.
My wife passed by me while I was drafting this post, and when I told her I was writing about biometric domain fluency, she was not impressed.
Then again, she is not an identity marketing leader.
But if YOU are an identity marketing leader, consider Bredemarket for your consulting needs.
Watch and book.
Learn about what I can do for your firm and schedule a free meeting.
Rather than analyzing an accuracy rate in a vacuum, ensure that you also analyze:
The complexity of the problem
The amount of data
The consequences of the accuracy determination
If you’re comparing 20 contestants at a small beauty pageant where the winner gets a McDonald’s gift card, a 95% accuracy rate may be acceptable.
But if you’re using the 1 billion+ Aadhaar database to confirm an identity and access the money in your bank account, 95% accuracy will be a national scandal.
I swear I’ve written about “MasterPrints” before, but I can’t find any such article. Maybe I just discussed it internally at IDEMIA when I worked there in 2018.
“Researchers at NYU and U Michigan have published a paper explaining how they used a pair of machine-learning systems to develop a “universal fingerprint” that can fool the lowest-security fingerprint sensors 76% of the time (it is less effective against higher-security sensors).
“The researchers used “generative adversarial networks” (GAN) to develop their attack: this technique uses a pair of machine learning systems, a “generator” which tries to fool a “discriminator,” to produce a kind of dialectical back-and-forth in that creates fakes that are harder and harder to detect.”
While this happened over seven years ago and is probably harder to implement with today’s technology, I was reminded of this when I ran across this Biometric Update article.
“A new research paper explores a signal-level approach to voice morphing attacks that exposes vulnerabilities in biometric voice recognition systems.
“The abstract describes Time-domain Voice Identity Morphing (TD-VIM) as “a novel approach for voice-based biometric morphing” which “enables the blending of voice characteristics from two distinct identities at the signal level.” TD-VIM allows for seamless voice morphing directly in the time domain, allowing “identity blending without any embeddings from the backbone, or reference text.””
So it, um, sounds like we not only have MasterPrints, but also MasterVoices.
“I think too much knowledge is actually bad in tech: you’re biased.”
Why does this quote affect me so deeply? Because with my 30-plus years of identity/biometric experience, I obviously have too much knowledge of the industry, which is obviously bad. After all, all a biometric company needs is a salesperson, an engineer, an African data labeler, and someone to run the generative AI for everything else. The company doesn’t need someone who knows that Printrak isn’t spelled with a C.
Google Gemini.
In this post I will share three of the “biases” I have developed in my 30-plus years in identity and biometrics, and how to correct these biases by stripping away that 20th century experience and applying novel thinking.
And if that last paragraph made you throw up in your mouth…read to the end of the post.
But first, let’s briefly explore these three biases that I shamefully hold due to my status as a biometric product marketing expert:
Independent algorithmic confirmation is valuable.
Process is valuable.
Artificial intelligence is merely a tool.
Biometric product marketing expert.
Bias 1: Independent Algorithmic Confirmation is Valuable
But how do prospects know that these algorithms work? How accurate are they? How fast are they? How secure are they?
My bias
My brain, embedded with over 30 years of bias, gravitates to the idea that vendors should submit their algorithms for independent testing and confirmation.
From a NIST facial recognition demographic bias text.
This could be an accuracy test such as the ones NIST and DHS administer, or confirmation of presentation attack detection capabilities (as BixeLab, iBeta, and other organizations perform), or confirmation of injection attack detection capabilities.
Novel thinking
But you’re smarter than that and refuse to support the testing-industrial complex. They have their explicit or implicit agendas and want to force the biometric vendors to do well on the tests. For example, the U.S. Federal Bureau of Investigation’s “Appendix F” fingerprint capture quality standard specifically EXCLUDES contactless solutions, forcing everyone down the same contact path.
But you and your novel thinking reject these unnecessary impediments. You’re not going to constrain yourself by the assertions of others. You are going to assert your own benefits. Develop and administer your own tests. Share with your prospects how wonderful you are without going through an intermediary. That will prove your superiority…right?
Bias 2: Process is Valuable
A biometric company has to perform a variety of tasks. Raise funding. Hire people. Develop, market, propose, sell, and implement products. Throw parties.
How will the company do all these things?
My bias
My brain, encumbered by my experience (including a decade at Motorola), persists in a belief that process is the answer. The process can be as simple as scribblings on a cocktail napkin, but you need some process if you want to cash out in a glorious exit—I mean, deliver superior products to your customers.
Perhaps you need a development processs that defines, among other things, how long a sprint should be. A capture and proposal process (Shipley or simpler) that defines, among other things, who has the authority to approve a $10 million proposal A go-to-market process that defines the deliverables for different tiers, and who is responsible, accountable, consulted, and informed. Or maybe just an onboarding process when starting a new project, dictating the questions you need to ask at the beginning.
Bredemarket’s seven questions. I ask, then I act.
Novel thinking
Sure all that process is fine…if you don’t want to do anything. Do you really want to force your people to wait two weeks for the latest product iteration? Impose a multinational bureauracy on your sales process? Go through an onerous checklist before marketing a product?
Google Gemini.
Just code it.
Just sell it.
Just write it.
Bias 3: Artificial Intelligence is Merely a Tool
The problem with experienced people is that they think that there is nothing new under the sun.
You talk about cloud computing, and they yawn, “Sounds like time sharing.” You talk about quantum computing, and they yawn, “Sounds like the Pentium.” You talk about blockchain, and they yawn, “Sounds like a notary public.”
My bias
As I sip my Pepperidge Farm, I can barely conceal my revulsion at those who think “we use AI” is a world-dominating marketing message. Artificial intelligence is not a way of life. It is a tool. A tool that in and of itself does not merit much of a mention.
Google Gemini.
How many automobile manufacturers proclaim “we use tires” as part of their marketing messaging? Tires are essential to an automobile’s performance, but since everyone has them, they’re not a differentiator and not worthy of mention.
In the same way, everyone has AI…so why talk about its mere presence? Talk about the benefits your implementation provides and how these benefits differentiate you from your competitors.
Novel thinking
Yep, the grandpas that declare “AI is only a tool” are missing the significance entirely. AI is not like a Pentium chip. It is a transformational technology that is already changing the way we create, sell, and market.
Therefore it is critically important to highlight your product’s AI use. AI isn’t a “so what” feature, but an indication of revolutionary transformative technology. You suppress mention of AI at your own peril.
How do I overcome my biases of experience?
OK, so I’ve identified the outmoded thinking that results from too much experience. But how do I overcome it?
I don’t.
Because if you haven’t already detected it, I believe that experience IS valuable, and that all three items above are essential and shouldn’t be jettisoned for the new, novel, and kewl.
Are you a identity/biometric marketing leader who needs to tell your prospects that your algorithms are validated by reputable independent bodies?
Or that you have a process (simple or not) that governs how your customers receive your products?
Or that your AI actually does unique things that your competitors don’t, providing true benefits to your customers?
Bredemarket can help with strategy, analysis, content, and/or proposals for your identity/biometric firm. Talk to me (for free).
By the way, here’s MY process (and my services and pricing).
Bredemarket: Services, Process, and Pricing. Revised 10/28/2026.
With the exception of colorblind people, the use of colors in dashboards makes information more accessible, particularly in populations where green means “good” and red means “bad.”
The National Institute of Standards and Technology understands the importance of consistent colors, having worked on traffic light colors since the National Bureau of Standards days (PDF).
For more modern applications such as biometrics, NIST recently incorporated a color coding display change to one of its tabs for the “Face Recognition Technology Evaluation (FRTE) 1:N Identification” results. Specifically, the “Demographics: False Positive Dependence” tab.
The change, announced in an email, is as follows:
“The false positive identification error rate tables now include color-coding to highlight anomalously high values.”
In this context, “anomalously high” is bad, or red. (Actually dark pink, but close enough.)
But let’s explain WHY and HOW NIST made this change.
Why does NIST highlight demographic false positive dependence?
NIST has of course explored the demographic effects of face recognition for years, and the “Demographics: False Positive Dependence” tab provides additional tracking for this.
Why does NIST do this?
“False positives occur when searches return wrong identities. Such outcomes have application-dependent consequences, which can be serious.”
How does NIST highlight demographic false positive dependence?
Anyway, NIST created the “Demographics: False Positive Dependence” tab.
“The table shows false positive identification rates (FPIR), the fraction of searches that should not return gallery entries above a threshold, but do. The threshold is set for each algorithm to give a FPIR of 0.002 (1 in 500) or less on searches of women born in Eastern Europe.”
And for algorithms that have “anomalously high values” in other demographic populations, NIST has added the color coding.
“A cell is shaded by how much larger FPIR is than that: yellow if FPIR is 20 times larger; pink if FPIR is 40 times larger; and dark pink if FPIR is 80 times larger.”
What does the highlighting look like?
Let me illustrate this with the results from the three algorithms Omnigarde submitted.
Data captured April 8, 2026. Omnigarde.
Omnigarde’s first two algorithms, submitted in 2023 and 2024, exhibited high FPIR values for south Asian females, and the second algorithm also exhibited a high FPIR value for east Asian females. See the color coding.
The third algorithm, submitted in 2025, had lower FPIR values for these populations and thus no yellow color coding.
Even the less-stellar algorithms show improvement over time.
Data captured April 8, 2026. Anonymized (but you can figure it out if you’re curious).
Final thoughts
Both vendors and customers/prospects can rightfully question whether this is helpful or hurtful. I lean toward “helpful,” because if the facial recognition algorithm you use provides high false positives for certain popularions, you need to know.
And as always, law enforcement in the United States should NEVER solely rely on facial recognition results as the basis for an arrest…even for Eastern European females. They should ONLY be an investigative lead.
In the meantime, take care of yourself, and each other.
The National Institute of Standards and Technology (NIST) isn’t the only entity that is seeking to combat facial recognition demographic bias. The Center for Identification Technology Research (CITeR) is doing its part.
The Problem
NIST and other entities have documented facial recognition accuracy differences related to skin tone. This is separate from the topic of facial analysis: this relates to facial recognition, or the identification of an individual. (As a note, “Gender Shades” had NOTHING to do with facial recognition.)
It’s fair to summarize that the accuracy of an algorithm depends upon the data used to train the algorithm. For example, if an algorithm is trained entirely on Japanese people, you would expect that it would be very accurate in identifying Japanese, but less accurate in identifying Native Americans or Kenyans.
Many of the most-used facial recognition algorithms are authored by North American/European or Asian companies, and while the good ones seek to employ a broad data set for algorithm training, NIST and other results document clear demographic differences in accuracy.
The Research
The Center for Identification Technology Research (CITeR) is a consortium of universities, government agencies, and private entities. The lead entity in CITeR, Clarkson University, has initiated research on “improving equity in face recognition systems.” Clarkson is using the following methods:
Establish a continuous skin color metric that retains accuracy across different image acquisition environments.
Develop a statistical approach to measure equity, ensuring FR results fall within a precise margin of error.
Employ new FR systems in combination with or instead of existing measures to minimize bias of results.
In this work, Clarkson is cooperating with other entities, such as the International Organization for Standardization (ISO) and the FIDO Alliance.
The final goal is to make facial recognition usable for everyone.
Your problem
Is your identity company and its product marketers also working to reduce demographic bias? How are you telling your story? Bredemarket (the biometric product marketing expert) can help with strategic and tactical solutions for your marketing and writing needs.
Bredemarket services, process, and pricing. Revised 10/28/2026.
If I can help your firm with analysis, content, or even proposals in this area, talk to me.
As I’ve noted before, healthcare is a pioneering user of artificial intelligence, although (hopefully) under robust controls to maintain accuracy and preserve HIPAA-level privacy.
“We are living through a generational shift, one where AI doesn’t just augment how organizations work but fundamentally transforms them from the inside out,” said Mohamad Makhzoumi, Co-CEO of NEA, who will join Qualified Health’s Board of Directors in conjunction with the financing. “From NEA’s nearly five decades of company-building experience, we believe the organizations shaping the next era of healthcare innovation will be those helping health systems reimagine every administrative and clinical workflow from the ground up, and Qualified Health is exactly that company. We are thrilled to lead this financing and to partner with Justin and team to accelerate healthcare’s AI transformation and shape the future of healthcare enterprises across the country.”
“Health systems today are operating under extraordinary pressure, from rising labor costs to tightening reimbursement, while managing increasing complexity in patient care,” said Jared Kesselheim, MD, Managing Partner at Transformation Capital. “What stood out to us about Qualified Health is that the team approaches this work as medical care specialists, with a deep understanding of the realities health systems face every day. That perspective allows them to identify where AI can create meaningful clinical and operational impact. We’re excited to partner with Justin and the Qualified Health team as they help leading health systems navigate this next phase of healthcare.”
Another topic raised by Nadaa Taiyab during today’s SoCal Tech Forum meeting was ambient clinical intelligence. See her comments on how AI benefits diametrically opposing healthcare entities here.
There are three ways that a health professional can create records during, and/or after, a patient visit.
Typing. The professional has their hands on the keyboard during the meeting, which doesn’t make a good impression on the patient.
Structured dictation. The professional can actually look at the patient, but the dictation is unnatural. As Bredebot characterizes it: “where you have to speak specific commands like ‘Period’ or ‘New Paragraph.’”
“Ambient clinical intelligence, or ACI, is advanced, AI-powered voice recognizing technology that quietly listens in on clinical encounters and aids the medical documentation process by automating medical transcription and note taking. This all-encompassing technology has the ability to totally transform the lives of clinicians, and thus healthcare on every level.”
Like any generative AI model, ambient clinical intelligence has to provide my four standard benefits: accuracy, ease of use, security, and speed.
Accuracy is critically important in any health application, since inaccurate coding could literally affect life or death.
Ease of use is of course the whole point of ambient clinical intelligence, since it replaces harder-to-use methods.
Security and privacy are necessary when dealing with personal health information (PHI).
Speed is essential also. As Taiyab noted elsewhere in her talk, the work is increasing and the workforce not increasing as rapidly.
But if the medical professional and patient benefit from the accuracy, ease of use, security, and speed of ambient clinical intelligence, we all win.
