hosted it and not necessarily the fact that we did crowd funding because we
are interested in doing crowd funding again and giving that option to
individuals, to invest in the company without being an accredited investor.
INTERVIEWER: Why do you think that’s important that part about not having to
be an accredited investor? PARTICIPANT: Well, because there’s a lot of people
who do not meet that classification and we are very much in a market where
people are more educated about investing options. And even though you may not
have the assets or capital to do what an accredit investor can, you can still
take a portion of your income and make small investments in a lot of
companies. And it gives people more options to invest in companies that are
not publicly listed yet. So I think it’s more of a play towards what the wider
audience is interested in and then also in the spirit of transparency and just
saying, ”Hey, if there’s people out there that don’t like this, we have a
public page that you can express your concerns on that we will address that
are on the same page where people make investments.” So that way you can get a
good picture of where we stand on happening events or things that we’ve
addressed in the past.

  * •

[The reason for the chosen funding path] is that because we want to continue
bringing on advisors that will help guide us and that’s what good VCs do.
There’s lots of good VCs out there that we could partner with but we’re an
unproven entity and just moving forward with [a specific sum of money] isn’t
going to get us there quickly and that means that my co-signer and I would be
constantly having to raise another [a specific sum of money], another [a
specific sum of money], and constantly fundraising rather than getting out
there and running the business and building it and helping people and that’s
one reason why the philanthropists have started [this funding entity] because
they noticed that women and people of color in particular, they want to help
their communities and that’s one of the reasons why they don’t grow as quickly
and scale as quickly and they don’t get the funding that they need in those
series of rounds because they’re so focused on their business and giving back
to the community rather than just growing and scaling so they can get to IPO.

  * •

So it’s self funded. We’re on our own, we haven’t borrowed any money from
anybody. Nor are we funded in any form. So we are on our own, entirely.[…] So
the fundamental belief has been to expand organically, to grow organically. So
that’s the power of the organization. Extremely hard working bunch of people
who believe in each other’s abilities. We believe the CEO’s vision. I don’t
think that we have needed all these years. I think our biggest goal is to
become self sufficient. To make sure that we recover the costs that we
invested in the product over the last several years. We try to recover that
over the next year or so and make sure we grow much bigger so that we redeploy
the funds back in the product and come up with more [AI] solutions.

  * •

What our CEO is anticipating [from a crowdfunding round] is it’s likely going
to come from [Company Name] super users; people that are really happy with our
platform, see the direction that we’re going, and wanting to be a part of
that. Honestly, we feel like the features that we’re building out are going to
be so useful to people, that they’ll have no problem investing via a
crowdsourcing way, at least some percentage of our users. Just because it’d be
like, ”Yeah. This thing is great. This is free money. I invest now and I get…
Because these guys are obviously going places.” That’s what we hope our users
feel, and that’s what we think we can offer.

  * •

We have investments from private individuals, and while we were trying to
raise funding, there were some investors that they really cared about the fact
or that we are trying to take an ethical approach in what we are doing. They
really, really like that and supported that. There were some other investors
that they were turned off by it. You see both. I heard by at least one person
saying it out loud and more than one implying it, saying that give it for free
and just get all the data and sell all the data, which is not what we want to
do, but this exists. For the most part, we are trying to raise capital from
investors that have the same kind of values and mindset with us and people who
are not afraid to lose certain revenue or sales … We had clients asking us to
do things that we said, you know what? No. No, this is not something we feel
comfortable with doing.

  * •

I don’t know if you’re aware, we had done an equity crowd funding round that
we were allowing anybody to go and buy shares online. Part of the reason we
did that was because we wanted everybody to know that you have equity, the
crowd, all people, we have [specific number] investors, an average investment
size of [specific dollar value, less than $1,000]. Everybody could go and
invest. We didn’t just do it behind closed doors to give the opportunity to
very few people.

  * •

So we formed a huge network of venture capitalists and scientists who are now
convinced or investors and so forth. And a lot of CEOs who ended up mentoring
us during the process, we are still in touch with. And so that was a really
good experience. And although the feedback used to be really hostile, oh, this
is not going to happen. I mean, the early days, I think we had a prototype and
some people felt it looked like a high school project, until you could really
convince them that we had data and we were doing all these things. And so, it
sort of took us and people can judge you really quickly, because they get like
a five minute pitch to judge you. So it also tells you, one thing we also
learned was that you might get judged very quickly, but the onus is on you to
prove otherwise, that you have to really battle hard and show them that you
are doing that. And maybe it exposes certain things that you’re taking for
granted in terms of how you’re pitching your company.

  * •

So, we are bootstrapping and we are going to go with a debt arrangement and
the reason we’re going to go with debt arrangement is because we know, based
upon our research, that in order to capture market, in order to get the
resources we need, in order to maintain a competitive advantage, we’re going
to need to grow and scale quickly and that’s going to require more capital
than angel investors could put up and we’re going to need to do that faster
and have more control over that process than if we go through the traditional
white male VC process.

  * •

We’re actually trying to do a lot of our seed round crowdfunded, which is a
bit different obviously than how it usually goes. I would anticipate that, do
we take money from VCs? That’s something that I would need to address and
prepare presentations for. But for as long as possible, we want to avoid
taking money from VCs. We don’t want large individual stakeholders in our
business. Because oftentimes they’re very focused on profit, whereas we’re
just trying to build the best platform for people. […] Our CEO, he’s run a few
businesses/startups in the past, and not having the best experiences with VCs
to my knowledge. For instance, a lot of the times VCs will want a board seat
after giving a certain amount of money, or they will set profit goals for you.
And we’ve all agreed that, those things can inhibit progress toward our
collective goal. Everyone that works at [Company Name], like I said, the pay
isn’t why people work here. Everyone that works here is really passionate
about the product, we think we can build something really cool and useful to
people. And that part of the vision is taken away, or dampened, if I get asked
for hard quarterly profit goals, or someone from the VC saying like, ”I think
this is the direction you should take the app.”

  * •

I would say, we looked at the VC funding route in the beginning because that’s
what you’re told to do, right? That’s how you get funded. This is the path.
You go pre-seed it’s angel investors, after that it’s VCs and then you go
through the Series process. Well, if we want to not dilute our company, which
our employees have shares and will continue to have shares and so if we wanted
to do the right thing for them and not dilute their capital ownership, if we
want to be able to have control and run our company rather than having to go
through constant funding rounds, then this was one of the best alternatives
we’ve seen in comparison to using like a small business loan since we’re pre-
revenue and since we would need more capital than what one of those small
business loans could offer since we’re a tech company and we’re going to be
national or an app, right? So you can download the app at any point in time
today. So we need to get that go-to-market strategy out the door quickly. We
need to have our brand presence out and ready to rock and roll, go-to-market
strategy messaging consistent across the board and we’re building a
marketplace. That’s really double the marketing costs.

  * •

Yeah, we had a number of pitches that were made to tons of investors, and it
all depends on who the investor is. Some investors are very curious about
every slide that you put together, and some investors, they want to just know,
”Well, tell me how you’re going to generate the traction? What your revenue is
going to be between now and three years? And how you want to return my funds
back with what profit?” So there are very shrewd investors, there are very
curious investors, there are very deep minded investors. So you run into all
kinds of investors in the process, but ultimately, everybody is looking for,
”when is your first pay day? So, let’s say I invest in your firm today, when
are you going to pay me back? And with how much return?” So that would be the
bottom line with most investors, but some in the process will be very slow in
asking that question. But some will be very fast. They’ll probably ask you in
the second minute, ”well, I’ve seen tons of these and I’ve gone through tons
of these, tell me the bottom line, explain the bottom line to me.”

  * •

Because maybe a lot of companies that you speak to could be B2C or B2B could
be ready products, where they will have different challenges. Our challenge is
not sales, or customer interest, but the time taken to implement it, which in
turn impacts our revenue and stuff.

  * •

Most recently [the questions investors ask are] really around, ”how do we
think about making sure the models are valid? How do we think about improving
the models, building new models as our platform grows? So, what is the roadmap
for all of it?”

  * •

What I’m speaking about, these are my impressions. By vision alignment, I mean
somebody who understands the larger problem that we are trying to solve and
the impact that we will be able create in the coming years, and the value that
we provide as a platform. And strategic alignment also means, investors who
better understand this business or have been in this space, have invested in
the ecosystem, not necessarily AI companies, but companies that could be our
consumers, like in [specific, relevant industries.

  * •

[Investors were] like, ”Well, these are just science projects.” But the reason
it rubbed me wrong was that, that was the fundamental key to why we thought
that our product would ultimately succeed on the market, because of the
techniques and rigor that we used. […] it was almost like an anti-science
trivialization of what scientists do.

  * •

The difficulty we have is that [our underlying mathematical field] in itself
is really not looked at … People look at NLP. People look at computer vision.
Those are the kinds of sectors of AI that have gotten a lot of attention and a
lot of brainpower, and so a lot of people have created many different packages
to make those applications really quick. Now you really don’t even have to
build it. A lot of platforms are just click to play. You can load a data set,
and you can say, ”Give me some image recognition,” and you’ve got it. With
[our underlying mathematical field] and the way the technology is right now,
the pipelines don’t exist. The algorithms don’t exist. They’re just in papers,
and so that’s the toughest part is just going through the equations and
ensuring that your pipeline is right so that your output processes through the
equations correctly.

  * •

So we’re trying to solve a specific problem to publish a whitepaper. So what
we want to do to prove the efficacy of our model is to apply our data set that
already has [ground truth labels], to impute it. We’ve built our ML models,
and we have [ground truth labels], but we want to compare it to a published
industry data set to see how much better, quantifiably, our algorithms will be
over what they’re doing currently.

  * •

The only value that they understand is the delta between my [predictions] and
their [predictions], and so if I can show that my [prediction] is more
accurate, they don’t care how the sausage is made. […] But just like the
iPhone, you just want to know that it works, not necessarily all of the
technology that goes into making it. So I always emphasize the accuracy of the
[the predictions] based on what they’re currently doing […] but we don’t go
into details.

  * •

The choice of the relevant literature is a human intervention. So, my co-
founder along with our team of scientists and clinical advisors, work together
to determine the relevancy of the research related to what we’re doing. And
then only then does it get modeled against the data.

  * •

I’m a co-founder for this company, still very actively involved in the company
and helping that development of the algorithms. But I’m also an assistant
professor now here in [the academic institution] and I have my own lab, where
I’m looking at these questions on the side as well. So, I’m hoping that one
day these insights will further help refine my algorithms from the [scientific
domain] side, more so than from the [algorithm] side.

  * •

So, we have an AI and data science advisor on our board. We have one data
scientist on our team, that was our first hire because we knew how integral
this was going to be to ensure the accuracy of our algorithm going forward and
to also leverage the data that we’re collecting for academic research going
forward and for other product lines.

  * •

I’m finding that articulating these kind of nuanced data sciencey things is
very challenging to the normal user. Somebody who does it for a living
probably gets it very quickly. People who are end users are like, ”I don’t
understand this.” So it’s one of our bigger challenges to try to make the user
experience something that is palatable and easily understood. In the context
of the first feature I mentioned called [feature name], a lot of people just
don’t understand. It just doesn’t make sense to them. It makes sense to us but
it doesn’t make sense to them so it’s not very useful. […] You can say fairly
easily the way we do it now is kind of like … Think of it as like Z scores.
Like you’re either negative or positive to the norm and how negative or how
positive are you and we’re also thinking of like a ”what if” tool to where you
could say, ”If I index this up, what happens to the target variable?” That I
think will end up being a much more palatable experience for people than just
seeing a Z-score and they go, ”Thank you, I’m +1 or I’m +2.”

  * •

At some point, once we get the funding, our goal would be to recruit a Ph.D.
level, I’m not a PhD, I have a computer science background from one of the top
institutes but I’m not anywhere close to a Ph.D., we probably need to develop
the data science.

  * •

The thing I can think of is when we were more actively fundraising and I had a
pitch deck together… I came from academia, so I had an initial pitch deck that
had this almost like a spreadsheet and it had a bunch of numbers on it and it
carefully calculated out the return on investment and how it related to our
algorithm performance and things like that. And then I presented to a bunch of
my friends who were also academics and they said, ”Well, this slide needs more
numbers.” And so, then I added more numbers and then I got more feedback from
fellow like startup CEOs, and they were like, ”You just need to get rid of
this slide altogether.” And so, there were a couple of iterations there. And
so, then when I went out and actually pitched it, I pitched it to just tech
investors and the no numbers thing was working just fine.

  * •

Essentially the feedback that we’ve gotten from clinicians has been that the
product that we’re building really serves an area of high clinical need. So I
think they’re very interested. I think they’re less curious about the AI
aspect, that really doesn’t mean much to them. Even the [domain relevant to
medicine] aspect I think is not that interesting or exciting to them. They’re
really just kind of interested in ”can you get this answer to me faster than
the typical diagnostic so I can treat my patients in a more precise way?” So
that’s ultimately what they care about. I’ve kind of found that on the
clinician side there’s very little understanding, maybe even very little
appetite for understanding or really delving into scientific [domain relevant
to medicine] explanations or really understanding the AI aspect. In fact, I
don’t think really anybody’s asked any questions on how does the machine
learning really work or which model did you use. The questions are really on
the clinician impact and that’s really ultimately what the clinician… makes
sense that would be what the clinician cares about.

  * •

So that’s in terms of the machine learning that the team does. Upstream of the
machine learning, I guess I should have said, there’s the dataset and actually
the domain experts, the [scientific specialists] are in charge of just
curating that data to begin with. So while there’s some feature extrapolation
that the data scientists is responsible for, just knowing what data to get and
how to represent it and making sure that the data makes sense, the
[biological] data is high quality, the annotated metadata is high quality,
that’s something that the [scientific specialists] do since that’s kind of in
their area.

  * •

Yeah, I mean our advisory board has been excellent and I think while none of
them are in the industry themselves, or I should say some of them are, but
even for the ones who are not in industry and are academic, I think they have
a good sense of… Certainly on the scientific technical side, just thinking
about okay, you can simplify this or you could try this other avenue. They’re
very aware of the literature, so if there’s something new that’s come up in
the last few months that we’re not aware of that could really help solve a
particular problem they have, they usually can point us in the right direction
and they can also give us a gut check if the things that we’re doing make
sense. So yeah, they’ve been very helpful in that regard.

  * •

Sometimes we feel like the most exciting part of our product is in the details
and we always want to talk about the details and also being from scientific
backgrounds, a lot of folks in the company, this is their first job after
academia, and so a lot of people have to wean themselves off the desire to
just go straight way into the technical details. So even though we think
there’s a lot of… really some of the magic is in the technical details, that’s
not really what marketing wants to hear, and having something that’s a little
bit more simple and easier to grasp is really the thing that can have an
impact.

  * •

Because in our field, I think where a lot of companies have failed is as soon
as they go try to do that, their algorithms flop. And we had high enough
confidence in the diversity of our data set that if we were to just hand it
over to someone that it would just prove itself. And we didn’t see that quite
in the field as much, so we thought it would be a good competitive advantage.

  * •

INTERVIEWER: So you said that they were disinterested in the technical
details. Were there instances, specific times where it showed to you that they
were disinterested in those details? PARTICIPANT: They said it right away.

  * •

I think because ultimately the folks that we work with are trained and at the
end of the day they’re trying to care for patients and there’s, I’m sure, a
lot of noise that comes along with all sorts of external diagnostics and
services and are really laser focused on how do I make sure that I’m giving
the best care to patients. So I think there’s a bit of that.

  * •

The other challenge is that ultimately, our results aren’t going to be as
stellar as a lot of others because now we have to account for the fact that
it’s for all the variability within the data set whereas, if we’re just
focused on one homogeneous data set, our accuracy stats will be higher. So,
that has been one sticky, like difficult point in terms of head-to-head
comparisons, for example, between us and our competitors. But what it does buy
us is that when we do deploy to a new site where we have less of a drop-off
[…]

  * •

So a very educated potential customer would say something like, ”Where’s your
data from?” And most doctors will be like, ”Okay. AI, what’s the error rate?
How do you know if it’s wrong? Who’s liable for…?” But it’s more about how it
impacts their practice rather than explaining how the model works. […] And
then sometimes they’re curious, so a particularly curious techie [doctor], we
have materials I can just copy and paste into an email, and they can learn
about a three-minute video on how CNNs work. And ”Here’s the Google paper,”
and different materials if they want to consume that. Because some do it, it’s
a rare bird.

  * •

[…] and the second, I think is a fundamental understanding of statistics and
how those statistics apply to large data sets and the different pitfalls that
you can run into when you run these statistics on big data sets and how the
data can in a sense, lie to you, or you can lie to yourself. It’s very easy to
kind of get in trouble with that.

  * •

A lot of it in our field has to do with overfitting and out-of-sample
validation. And so, I think a lot of people, whether it’s on the research side
or in the academic side or the industry side, go about building machine
learning algorithms that demonstrate very high accuracy, and then they’re able
to publish it and make pretty big news with it. And it’s not their fault
either that ultimately, these models don’t end up generalizing well, because
they don’t have access to the data sets to even validate that, but there was a
big wave of these initial studies just showing numbers that are through the
roof AUCs of 0.99, and things like that, almost too good to be true, but then
time and time again, when you take these algorithms and apply them in the real
world, or have slight perturbations in the patient population or whatever
other features it is, then it kind of falls apart. And so, that was probably
one of the prime things on my mind when trying to develop our algorithms
because yeah, I felt like we wanted to take that next step and make sure that
once we did deploy in the real world, that it wouldn’t kind of fall apart. And
so, we do a lot of testing in terms of trying to do completely out-of-sample
testing, but also sourcing our data from as diverse sources as possible in
order to make sure that we have the highest chance of generalizing.

  * •

It’s still something we’re trying to deal with because I think it’s a hard
conversation to have when you have that conversation with people who know
statistics, and then it’s an almost impossible conversation to have with
people who don’t know statistics well at all. So, what I mean by that is even
within stats, what numbers do you report? There’s a lot of wide range of
different values that you could report and they have different meanings in
different contexts. And so, the challenge really is trying to boil it down to
what it ultimately means for the performance of a certain product. And
honestly, it’s still a challenge we’re facing now, because there are certain
anchor points that people in the field have, whether or not they have a good
understanding of statistics.

  * •

I’ve been talking about this for four or five years, so it’s hard for me to
recall a specific thing. But then, when we’ve talked to even our current
investors, it’s just like, ”If you can get above 90, that’s…” Or even doctors
will say, ”90 or above. That’s really great. That’s really amazing.” But I
think it’s just like humans are comparative creatures, and they’re not good at
absolutes at all. Nothing is an absolute in how we talk about things.

  * •

The 90% bar comes from the fact that well, it is the psychological effect that
[…] So at a fundamental level, the 90% is like that your Macy’s sale, when an
item is 9.99, you tend to look at it and buy it more. So it is from a
marketing publicity standpoint, that is just one aspect of it.

  * •

Nobody cares about the exact percentage. When we’re talking to most of the
customers, they say is that at least 80% accurate. That’s what most people put
us there. 80%, if it is at least 80%, I have heard it from so many people,
then they will work well enough for us. INTERVIEWER: Why 80%? Why do you
think? […] PARTICIPANT: I have no idea. I have no idea, but that’s what I have
heard many times. For them, it’s a good confidence level to make them say,
okay, we will at the insights we are looking for. INTERVIEWER: What do you
think their conceptualization of what that even means is? PARTICIPANT: […] I
don’t think most people have a very clear way of defining accuracy. I think
it’s more psychological. Can I trust the numbers?

  * •

That’s the number one requirement as to doing anything less than a deep
learning will not give us the type of results that we’re looking for. So
anything at the linear modeling level will not give us the accuracy that we’re
looking for, at least, about 90%. [This quote also included in Technological
Pressures section of this appendix.]

  * •

So, investors are a heterogeneous bunch too. So, if it’s the typical tech
investor, they often aren’t as familiar with biostats, and so often, we don’t
even need to get into this conversation. Like, they almost take you at your
word that your algorithm works and they never dive into the details. And then
there’s like the kind of biotech kind of academia-related investor, and it’s
almost the opposite extreme where they get completely lost in the numbers and
not potentially just enough to be dangerous sometimes in terms of trying to
interpret what you’re trying to say. And so the messaging, there has to be
much more careful and well-thought-out and airtight because any tiny gap in
the logic will result in disaster there.

  * •

And obviously we’ve got to do that whole thing and do it legitimately. So I am
somebody who is wired that – all the products I’ve brought to market – I want
them to be successful for what they are. So I don’t want to misrepresent the
product. I want to have something that’s actually really a great breakthrough.

  * •

I was trying to make the point about the whole generalizability, but also an
additional point on top of that which was the fact that we had a competitive
advantage because we were from academia. We knew how to do rigorous stats and
we knew how to design rigorous experiments to validate the performance of our
algorithms. And we had ongoing academic partners where I took great lengths to
minimize conflict of interest and things like that, such that I would… Like
our company would hand over the use of our algorithms. I wouldn’t be involved
in any of the data analysis or the data interpretation, I would just provide
support for running the algorithm. I would provide some… Like I initiated
these ideas and some advice on how to design the experiments, but it was
ultimately up to the discretion of the academics on how to proceed with the
project and how to write it up.

  * •

Now, the AI is just one small part of it. The AI is just one small part of it
in order to be able to analyze some of the data, but it requires [subject
matter experts] and other people to figure out, how do we actually take this
extremely large dimensional dataset and don’t just use something random like
PCA, or multidimensional scaling, or some other ad hoc, one of these
techniques to reduce it, but how do we actually reduce it down to smaller,
trainable datasets based on the [a scientific domain]? How do we make
statistical models based on [scientific data] that are more informative, so
that when you feed it to your machine learning algorithm, you’re more likely
to be able to discriminate between, make better predictions, at the end of it?
To me, these challenges are actually real opportunities because of the fact
that we’re not an AI company.

  * •

And in terms of the process itself, I think [these investors] kind of
respected the validations that we’ve done with a lot of different partners and
they themselves also sought to validate our algorithm independently as well,
and we did that. And so, I think in a sense, that played well into our hands
in that we were able to just show the product as it was without having to
compete with others with a lot of marketing wrapping around the product itself
and having to compare on those terms.

  * •

[The product documentation is] very, very detailed. It has references up the
wazoo. Anyone who has even … You can tell a significant amount of time was
made making this product. I don’t know how else to explain that. If you play
around and follow all the references in there, you realize even the writing
itself, this is not like you’re reading a Healthline article that was put
together by some person in Aflac. It’s almost like a review paper that would
get published in the literature, put into laypeople’s language. We typically
look at like hundreds of references for each things we make. This is heavy-
duty stuff. […] It is quite clear a team of scientists worked on this, right?
[…] People don’t read the references but the fact that we put them in there,
they’re like, ”Oh, no. They’re not making it up.” Now, some people do read
some of the references. There are people who are into that kind of stuff, but
the fact that it’s there and it’s explained clearly what is going on is,
people get that.

  * •

[The market doesn’t] focus so much on the scientific rigor or validation, but
more so on what other people are doing and what they see in terms of
marketing.

  * •

You see the flow of the science in there. It reproduces the work of a
healthcare provider because we make sure that what we’re putting is something
a healthcare provider would tell you if they understood the [biological] data.

  * •

We are basically just trying to show that we’re a legitimate scientific
company by having published data. We publish our AI models. We’re going to try
to publish these things, get them in nice journals, have that kind of
validation data. Like, ”Here’s our data. Here’s our datasets. Here’s how we
used it. Here’s how accurate they are.”

###  C.1. Technological Pressures

Quotes are derived from P4, P23, P3, P11, P13, P9, P14, P20, P22

  * •

I’ll make a general statement. Just given that we’re trying to do things
quickly and we don’t have a lot of manpower. When I can, I try to leverage
anything pre-trained. Just because I’m usually the only one working on ML at
[Company Name]. Although I do have a one intern right now. A decent amount of
stuff that we use is pre-trained, but also we’re doing enough things that are
specialized. For the [feature] that I mentioned earlier, I wrote that
completely from scratch, and it took a decent amount of time. They really just
depends on how specialized the use case. But the general answer is, we use
pre-trained whenever it will afford us the accuracy that we feel we need from
that model.

  * •

I’m still using classifiers and I do feature selection, do training, testing
out of sample, that sort of thing, but not really anything, more sophisticated
than that. So, in fact now we’re actively talking with another group that we
know really well from seven years. So this is in another [industry lab in the
scientific domain]. So, they’ve done a very, very good deep learning based
algorithm for [physiological recording] devices. They’ve done really well and
they’ve talked a lot about their work, and we are in talks for a good sort of
collaboration as well in the next year or so.

  * •

We use as much pre-training as possible.

  * •

We are training in-house. That’s the primary way of doing things because to
make everything [work with our hardware requirements], it’s not easy.

  * •

But I haven’t really learned that much after that, since we’re using some of
those tools. I know probably a lot has developed, the other side there’s a lot
of big data, they’re developing deep learning models and other like that, but
[I] haven’t really gone that side, [I’ve] stuck to classifiers and regression.

  * •

I would say more so what [deep learning models are] capable of doing. If you
were able to get the same results with non-deep learning techniques, then in
my mind, you’re not using AI and that’s totally okay. Because you shouldn’t be
using a technology just to use it, you should use whatever best fits the
problem. I guess just more so the capabilities, in general. You choose the
tools for the problem, and if the problem is complex in certain ways and not
complex in others, and the data is there, then deep learning is a really good
choice. There are a lot of caveats and there are a lot of different
methodologies. Actually here’s my counterexample, there’s a lot of
reinforcement learning techniques that aren’t based on deep networks that I
would still consider to be AI. It’s hard to give a blanket definition, at
least without thinking of it a little bit longer.

  * •

So that’s why at this point, so I’m more content if somebody in the lab wants
to learn [deep learning], or if a collaborator is really good at it. So I have
some people are very versed in RL based methods or deep RL based methods and
using some of the other approaches, so I just talk to them and see if there
are ways in which I can collaborate with them on those applications.

  * •

[…] we have our own Python-based models that are … We’re not doing any deep
learning or anything nutty like that, because it’s fairly rudimentary data
science I would say. Stuff that’s accessible to someone like me who’s not like
a Ph.D. I’m sure there’s people who would do it differently.

  * •

The term is ever evolving, because back in the seventies you have these just
logic engines, these rules-based engines that are lots of ”if” statements, and
that was AI. […] The most concise answer I can give you is just deep learning.
That is almost the new cutoff for AI, in my mind at least. Not to say using
random forest or gradient-boosted decision trees, or whatever you want to use,
isn’t data science, because it totally is. I like to say AI is very nebulous
term, but in my mind deep learning. These use of deep learning, is probably
it.

  * •

I’d contrast [current approaches to deep learning for computer vision] with,
I’d told you about some of the computer vision we were doing [in our previous
startup], where it’s like, okay, you’re trying to generate a signal in meeting
some expectations about what the data looks like, about how you’re going to
identify things like doing background subtraction and edge detection and
comparing this frame to this frame, and where you’re designing and
architecting the whole thing based on your deep understanding of the problem.
And those techniques, versus this magic of … I really do view these highly
evolved networks as magical, that they know these things, they’re in there.
And we don’t actually know how they know them. People are working on that, but
they’re more of a black box.

  * •

For instance, some of those pre-trained models are massive, and right now
we’re trying to monitor tech costs because we haven’t raised our seed just
yet. So we want to keep everything as reasonable as possible. And if I were to
just say like, ”I need real-time online predictions from BERT.” That’s not
really the most reasonable cost metrics.

  * •

That’s the number one requirement as to doing anything less than a deep
learning will not give us the type of results that we’re looking for. So
anything at the linear modeling level will not give us the accuracy that we’re
looking for, at least, about 90%.

  * •

So, there’s still tons of work to be done. But this is what, in a nutshell, we
plan to provide. But in the process, we’re also employing a number of
technologies like AI, artificial intelligence, the machine learning and deep
learning aspects of mining the data.

  * •

INTERVIEWER: […] does your solution, is it a custom solution or is it a
combination of pre-trained existing models like GPT-3 or something like that?
PARTICIPANT: This is pre-trained models, no training required. Absolutely zero
training, but you can have some impact [for customers].

  * •

But in terms of why we decided to include a bunch of real AI, deep learning,
all that kind of stuff, it’s the only solution that will enable our grander
vision.

  * •

So when I did [Previous Startup Name], the computer vision was super hard. And
it was all classical computer vision, background subtraction, feature
identification. And then I came to this, and the state of the art had advanced
quite a lot between [the previous startup date and the current startup date].
And I was blown away by how easy it was with transfer learning to make [a
classifier relevant to the problem]. It literally took a hundred labeled
images, to get something that was greater than 90% accurate. And that got us
off the ground. So we’ve deliberately shied away from hard problems, to look
at how we can build upon the state of the art, all the advancements that are
being driven by the Googles of the world.

  * •

I would say, providing meaningful inference from data with really high
dimensionality is a characteristic of deep learning that is pretty unique to
it. What else? Unique to deep learning… I can give you examples of a lot more
specific use cases. For instance, if you want to do sentiment analysis on
text, if you want to [task we employ pre-trained models for], all those kinds
of things, those are sufficiently complex problems that deep learning is the
only reasonable answer, if you are hoping to get near state-of-the-art
accuracy.

  * •

But we are hoping in the next one to five years, we will be able to provide a
solution that will be almost 100% accuracy [at the ML task]. So that’s where
we’re going to use the AI and deep learning methods.

  * •

At the back end, we have a mix of deep learning, machine learning models that
work together, and there’s a lot of NLP and NLU [components], that kind of
stuff that happens in the background.

  * •

But overall we believe pre-trained models are good enough, and custom training
is a lot of pain that we don’t want our customers to go through.

  * •

It’s basically feature identification, doing transfer learning on top of
established things like YOLO and Mask R-CNN and stuff like that. And then
occasionally doing regressions.

  * •

So the people I’m working with are not CV researchers, who build up networks
from scratch. They’re expert users who know how to do transfer learning, know
how to train something, maybe with a little oversight from an academic, that’s
a model that has worked really well. But what I’ve seen in that is there is a
huge difference in productivity and capability. Even among people who have
similar credentials and you’d think would do the same. And it boils down to I
think, making a few right choices, like choosing the right net to build off
of, knowing how to clean training data to get it to a good place, and being
hands on in the whole process.

  * •

I think a convolutional neural net is central to [what AI is] or some learning
network. There’s a training process where you learn from data. I’d contrast
that with an analytical process where you build up a model of how you believe
the world works, and then fit the parameters to it. And I think of, these
terms are all over the place these days. But I think of it as hooking on to
the advancements we’ve seen, like building off the ImageNet databases,
building off the work in Transformers on natural language processing in that
whole area. These areas where there’s like a scale component to it, the more
data you get, the more CPU you can throw at it, the better your result gets,
provided you run the controls the right way.

  * •

INTERVIEWER: So [deep learning is] accomplishing many of the same goals as
something that deep subject matter expertise might previously accomplish and
now it can do it in an automated way? PARTICIPANT: In an automated way and in
a better way. Yeah, I think for me, there is this aspect to AI of opaqueness,
where people work on those. They try and understand what features is it
picking up and so forth? But if anything, it’s like archaeology and going back
and try and understand that after the fact. Those aren’t necessarily designed
in.

###  C.2. Normative Pressures

Quotes are from P1, P2, P4, P5, P6, P7, P8, P9, P10, P11, P12, P13, P15, P16,
P17, P20, P21, P22

  * •

[…] if you had this type of thing, like for instance, I personally don’t want
to do any work in China because you get this type of thing in China, it would
be great for surveillance. INTERVIEWER: So if you don’t want it to be in
China, how do you prevent that from happening? PARTICIPANT: Well, we can’t. I
mean, I can do it by sales and by where we develop the product itself.
Eventually it’ll be out there. I mean, they’re working on it already. And I
think it’s just showing the different ways that we as a company aren’t going
to do that. And the other thing, too, is making sure that everything we do and
all the data we collect is private and it’s kept private. So for instance, our
data platform is going to be HIPAA compliant. We are not going to use data for
any other reasons other than just improving the health outcomes and the
accuracy of the product. So there are lots of ways that we can do that.

  * •

So we’ve noticed [the idea of AI] certainly resonates with international
[clients]. It increases our response rates by saying, ”Hey, we’re building an
equitable AI,” so there’s a partially marketing component, but also just there
is a lot of news about, ”Well, what if AI actually perpetuates healthcare
disparities?” And we think you can actually do the opposite. I mean, and most
people, I think most people who know AI think you can actually do the
opposite. You can help doctors who would have been biased become less biased
if you train your algorithms right on representative data.

  * •

I think one of the serious things that we think about a lot, and this is a
huge can of worms, but as with any industry, there’s also this negative. AI
can be viewed in a negative way too. And in medicine, it’s often viewed as a
threat to the livelihood of physicians and their ability to practice medicine
in the future because they might get replaced by algorithms. And so, that’s
something that in the grand scheme weighs heavily on [us]. I don’t know how
much it affects our day-to-day operations, but it will. There are ways that I
guess it affects our day-to-day operations in terms of how we market certain
things to certain groups and things like that. But it does play a huge role in
just the fundamental philosophical question of how much do we aim to do, how
far do we aim to take it, and what our ultimate goal is. […] So, it’s a
nuanced game because when you talk to investors, you want to talk about the
upside, the optimism, the hope and the goal of where things will end up. And
sometimes, that’s obviously way more advanced than where we are now, but that
level of advancement is potentially threatening to another group of people who
aren’t investors, who are potentially our customers or people who to me
personally, to other physicians who work with us. It’s easy to talk about what
we do today. It’s harder to talk about what we do in five to 10 years to
different groups, without being a little bit more cautious about how we convey
certain things, especially since they’re very speculative in nature to begin
with.

  * •

All this Black Lives Matter […] I really find it hard as an immigrant to this
country after half a year of forming opinion and voicing opinion about things
that going on here, because I don’t know the history, I don’t know what
unspoken things are going on in different groups in society. And I don’t want
to say that we don’t have problems in Europe or in [European Country] with
racism or stuff like that, but it was never racism based on the color of your
skin, at least in the last 80 years.

  * •

So we have discussed how we can offer our platform to nonprofit organizations.
And then maybe startups who don’t have the funds yet, but definitely need our
solution. So we actually plan to offer that via accelerator programs. So for
smaller cohorts, startups could apply and get our solution for free to use if
they’re approved.

  * •

We had privacy built in by design from the start. And I think that gives us
perhaps a little head start about the competition, especially here in the US.

  * •

So I personally think that race is nothing that determines anything important
that is in our [product]. So of course, on the individual level, if you grow
up in an impoverished part of town and all that, and I know that [people are]
ethnically distributed oftentimes in the US, so of course, that depends on
that. But if I hear what the Black Lives Matter movement is citing, what has
been happening in the US, I think in the 21st century, that should not be an
issue in any society if it’s here or anywhere else on the world. So if that is
still a thing that is happening, then of course a society should discuss it
and be better than that, just be better. So don’t be assholes. And on the
other hand, if I read something extreme, radical left-wing, it’s the same, so
don’t be an asshole. And that is our guidance. So do we think that there are
A-holes that want to use our technology or not?

  * •

INTERVIEWER: Does your team ever talk about the ethical or social implications
of your machine learning or technology? PARTICIPANT: Not that much. Not that
much. We talk about it a little bit, but not that much. Mainly, for a couple
reasons. […] One, in order to get a health product, I’m going to have to make
sure it’s not biased by some other AI. Because it’s not the same thing as one
of these AI models that predicts people that have done crime and it turns out
it’s looking at Black people […] The beauty of our industry is we can’t get
away with that kind of stuff. […] We definitely spend a little less time
worried about that because of the fact that we have to address those things
before we ever get big anyway.

  * •

INTERVIEWER: Have you gotten push back from [industry experts] that are
saying, ”Hey, there is this guy that would have paid for me but then he got
[Company Name Product] for free”? Is that something that you hear from
[industry experts]? PARTICIPANT: We are waiting for that. That day will
indicate the fact that we did a fabulous, fantastic job, although we don’t
want that to happen. I’m not trying to be cheeky. I’m not trying to be give
you the wrong impression, but would really mean huge to us because we broke a
hegemony. We broke a tradition and we bent rules there. Let me put this on
record. [Company Name] is not out to drop [industry experts] off their jobs.
[Company Name] is out to help people [perform the task of industry experts].
There are people living in [some parts of the world] who live on less had 30
dollars a month […] there are people who spend an entire month in that budget.
But then all they need is an internet connection and maybe they borrow a
friend’s laptop or a computer, access to it and they can [perform the task of
industry experts] on [Company Name Product]. […] So the day [an industry
expert] says that ”these guys have robbed our jobs,” we’ll definitely want to
invite them and have a cup of coffee.

  * •

So technology wise, I have the utmost respect and admire what Google is doing
when it comes to artificial intelligence, or what Facebook is doing,
especially Facebook in the last year [with their open source technology]. When
it comes to ethics in AI, I think we all witnessed in the last couple of
months, how Google failed. Especially for tech, I’m really a left-leaning
liberal, but I oftentimes think back to university… there were some [people],
a little bit older, that were just males in computer science and math. And of
course, if you’re strictly with other males confined for a couple of years in
some cellar with computers and all that stuff, you have a distinct way of
perhaps talking, making jokes and fun. And I know that these jokes are not an
appropriate representation of our society. And I think that is oftentimes a
problem with tech companies, these ”tech bros”, here in the US. I don’t want
to defend anything like what is going on with Blizzard at EA the computer game
company, who’s now all the rage in the media because of this anti-woman
movement. We don’t need to talk about that, that’s really over our border. And
I don’t want to defend that. And I don’t want to defend people that are not
aware that if you work with women, that you probably can’t bring the same
jokes you can do just on the men. […] Of course, some organization like Google
or Facebook with all their resources, they should be better. People should be
better, but people aren’t, I don’t have a high opinion of humanity in general,
or people in general. I think we should strive to be better. I sound like a
Silicon Valley advertising. I hate that, but that’s not how I’m made. I think
these things will happen, and these things will keep on happening, but
hopefully less frequently. This ethical AI group, that Google had, they had it
as a PR gag. That’s my personal opinion. INTERVIEWER: As a PR gag. What does
that mean? PARTICIPANT: Even in the US, privacy gets discussed more and more.
And so [Google] can divert some attraction and say, ”Hey, we have an ethical
group, an ethical AI group, an ethical privacy group, an ethical I have no
idea what group, and they use that to look like they are still a nice company
out of a garage in Palo Alto. And they don’t mean it. They don’t have it built
into their DNA.

  * •

It’s something that’s important to me, that we are very concerned about data
privacy. We are vehemently against just giving people access to our users’
data in a Facebook-esque way, just selling it for profit, et cetera. That’s
one AI morality line that we’ve drawn in the sand. […] As the person
responsible for data governance at the company, I wouldn’t really accept any
other way of doing it. However, I am lucky that all of the other people at the
company share my opinion.

  * •

In my opinion, all AI should be done very carefully. Because what you know as
the machine learning practitioner, as the data scientists, you know the data
that goes into the model, but beyond that, it’s not really explainable what
the model is doing. It’s gotten better over the years, but in a way, most of
these giant models are still black boxes. So all you really have control over
in terms of trying to eliminate bias or in the modeling process, is the data
that you can see as input. […] Certain features shouldn’t be used just in
general. And then in terms of privacy, […] when I started to learn what all
these big companies were doing with their data, particularly [data sharing]
practices, making it opt out rather than opt in, in terms of what they were
allowed to do with your data, that has always frustrated me. Considering all
those things and thinking about fairness and how I would want to be treated as
a person, I think I should extend the same courtesy to other people.
INTERVIEWER: […] So how do you deal with that tension about your aspirations
in terms of things that you might want to do from an ethical perspective in
the long-term, versus the things that you need to get done right now?
PARTICIPANT: I think it really does come down to the severity of what it would
mean to make a mistake in context. The dialects of English example that I gave
earlier, what it would mean to make a mistake there, the negative impacts of
that are more so on us because the user who our app doesn’t work as well as it
could for, they’ll just be like, ”I don’t really like this app. Let me go
download another app, or let me just not use it.” That’s totally fine. That’s
everyone’s decision. Obviously I think our app is cool. So they lose out on
that. They’re not really losing anything in the grander scheme of things,
whereas we’re incurring the risk on that side because if a user didn’t have a
good experience on our platform, they didn’t think it worked very well, we
lost a user. Maybe a more extreme counterexample, there’s a cancer diagnosis
AI looking at X-rays or something. If you make either a false positives or
false negatives in that space, are super negatively impactful to that person.
Because if it’s a false positive, they get all this chemo that they don’t
need, and that just makes their life hell. Or if it’s a false negative, then
something goes unchecked that might potentially doctors could have helped
with. In that sense, the user or the person the AI is acting upon, is
incurring the risk. And I think that’s a distinction.

  * •

We have [talked about ethics]. I would say that it’s something we’ve talked
about needing to be a focus of our conversations around growth, but we haven’t
dug into it deeply at this stage because I think a part of what we’ll have to
figure out and what we’d like to put on our radar, but haven’t gotten too deep
on any of it is, what is our role [because we provide a service to other
businesses]? I imagine that we’ll want a pretty thoughtful framework around
that as we grow because an entire industry that we hadn’t even thought of,
that we may want to have particular considerations for, could come to us and
want to start using us. And I think we’ll want to be ready with some
principles around, oh, well, we hadn’t conceived of your use case, but we have
some underlying principles that guide how we’ll interact with this use case.
But no, we haven’t actually gotten to those principles just yet.

  * •

[Ethics is] not something we have talked about. I mean I talk about that a lot
at [my main employer], but not in [my startup]. Just because what we’re doing
isn’t approving loans for people, you know what I mean? It’s telling people
largely insights about their data and not a bunch of other people’s data. So
it’s largely very personal, it’s one of the reasons why I have elected to do
things in the way we’ve done. I’m sure there’s someone who could probably find
some societal impact to what we do, but I don’t think it’s too great. We’re
not clustering based on race, ethnicity. We do cluster based on where you live
and who else is there, who [is similar to you] and things like that. But I
don’t really perceive that as something that is messing with society, right?

  * •

[…] the biases, if we don’t have the right mechanisms in place as we start
this journey and we are not aware of the biases that come into play when we’re
building out and testing these algorithms, then we’re not going to be able to
design for all. We’ll be designing for white women, for example. And that’s
not the purpose of what we’re trying to accomplish. We’re also trying to make
sure that, in particular, people of color, women of color who already have
biases in healthcare against them, that this avenue, using [Company Name], is
a way to help overcome those biases so they can get proper healthcare for
themselves.

  * •

But I wanted a diverse team. I want people from different backgrounds because
I think that improves not only innovation but it improves those checks and
balances from the AI ethics standpoint. [Participant describes specifics of
the team members’ demographic characteristics and nationalities]. So we all
come from different backgrounds and when we’re thinking about how do we design
this for our target market–women aged 20 to 54 based upon our market
research–we want to make sure that we have different perspectives included in
how we’re designing this for them and for that scale and growth. So I needed
people on the team that not only could connect with the customer and empathize
with the customer but knew the technology, how to structure it and then that
knew how to scale it.

  * •

Certainly, something that comes up all the time is disrupting jobs, taking
jobs away. So in [our] industry, there’s actually a labor shortage right now.
It has been going on for a long time, particularly around skilled labor. So we
frame it in terms of allowing your good people to do higher value things,
because you’re not going to get rid of your good people, just because a
computer can do part of their job. […] I certainly do talk to my friends and
staff about how Facebook is destroying our society and stuff like that. But
not so much in the context of these businesses. INTERVIEWER: It sounds like
you’ve actually considered this quite a bit in terms of the business and its
broader implications. Have those conversations translated into anything
concrete yet, in the product itself? PARTICIPANT: Well, you have to think
about workflow for everyone, throughout the whole process from the guy who’s
getting paid 15 bucks an hour, all the way through to the executive. And I saw
this in medicine too, for sure. Oftentimes, you sign a product for the
doctors, but the nurses can kill it, if the nurses don’t like using it. So
that certainly comes in. In the investor discussions, it comes in quite a lot.
You talk to VCs, they’ll bring up, ”What about this?” […] And so you’ve got to
deal with the latest headline they saw on the New York Times and be responsive
to that. Because they want to know that you’re coming from the same place that
they’re coming from.

  * •

I’m a futurist, so I don’t believe in ethics really. Anything that’s possible
will be made. So all technology is inevitable. It just depends on the
timescale you’re evaluating. INTERVIEWER: So how does that perspective then
inform how you think about building something? PARTICIPANT: We always build to
the extent that we can. Whatever is possible, we build for that. We don’t
limit by current ethical constraints because that’s all they are. They’re just
current ethical constraints. They may not exist in the future.

  * •

I’m on this impact high horse. It’s sometimes like I act like I’m still a
college student, and naive and not jaded. [I brought up in my company], ”Have
we thought about going to emerging economies, places where they don’t have
access to [medical specialists]?” Not just from [a regulatory perspective].
But just that’s where the need is the biggest. [But] honestly it’s all about,
can you stay afloat? [Participant describes company rhetoric about ethical
impact.] So that’s the ethical stuff that we talk about, but more so for
internal propaganda, I think. I’m a little skeptical.

  * •

One thing that we really try to do, is ensure that specifically on [aspects of
the underlying science…], that they all have multiple research reports across
different populations. So, we’re looking at a broader population base, versus
the standard genetic reporting is often done on Caucasians in specific areas.
[…] And then yes, in terms of the accessibility and democratizing data access,
that’s one trend that we’re really looking at and continuing to focus on.

  * •

So, we do think about data portability. It is part of our roadmap, but I would
not say we’re anywhere near a hundred percent on that. We more think about it
as interoperability between other products and solutions, that our members
will be using, so that it’s not siloed with us. Which a lot of companies make
it so it is siloed, which makes it more difficult to create the biggest impact
for that person. And that’s something that we’ve built our company around,
making sure that not only can we use a variety of data sources so that we make
it easier for people to get access to our products, but how we can integrate
with others so that it’s creating an even bigger impact.

  * •

But what I think constitutes AI is a system that is not only capable of
performing all the tasks that I described before, doing things consistently
and repeatedly, but doing it in an equitable fashion where it’s being trained
with datasets that are more representative of the world and all of its
communities and not just a subgroup. Which is why facial recognition
artificial intelligence is so controversial, because the people that are
training this AI are being lazy or they’re not looking at these datasets of
people that don’t look like them. If you look at, was it Clearview AI with the
police department and stuff, that was just mislabeling, just recognizing, oh,
we don’t know what a Black person looks like. This person’s Black. I think
[they committed a crime] and then wrongly convict somebody. So I think
artificial intelligence is a wonderful tool, specifically a tool, that needs
to be treated and trained with respect and careful diligence.

  * •

We’re going to follow the GDPR to some extent, though GDPR is not quite
applicable to the U.S. side. We want to use some of the GDPR principles.
There’s nobody going to stop us building the privacy as tightly as possible
into the platform. If somebody has already built a specification around it
through the GDPR, or somebody else, the Chinese, they are building some
privacy document, we could probably use it. By the time the US puts down the
regulations on the privacy aspects, maybe we have built 90% of it into the
platform. So we’re going to do everything possible to build the very tight
privacy and disclosures.

  * •

So those are external constraints, and we have to follow those constraints, so
data protection, privacy, HIPAA compliance. All of those things we need to
build into our product. INTERVIEWER: Do you think about those things as
helping your product development or hurting your product development or
irrelevant? PARTICIPANT: So the product only exists to serve the customer, and
so if the customer is required to follow those regulations, then we must
follow those regulations or else we can’t serve those customers.

  * •

[…] And that [product feature] has been trained on data sets that are [details
of datasets] and more ethically aligned. So that way we make sure that we’re
classifying as best as we can. So that way there’s no discrimination.

  * •

For me, hits close to home when you hear people are being wrongly convicted of
crimes they didn’t commit just because an AI said, it was them that did it. I
think that my personal relationship to how AI can be mistreated, especially
against someone that looks like me, kind of drove me to looking more to the
ethics of artificial intelligence and how these models are being trained.

  * •

There are a lot of people who do not meet that classification [as an
accredited investor]. We are very much in a market where people are more
educated about investing options. And even though you may not have the assets
or capital to do what an accredit investor can, you can still take a portion
of your income and make small investments in a lot of companies.
[Crowdfunding] gives people more options to invest in companies that are not
publicly listed yet. So I think it’s more of a play towards what the wider
audience that is interested in it and then also in the spirit of transparency
and just saying, ”Hey, if there’s people out there that don’t like this, we
have a public page that you can express your concerns on that we will address
that are on the same page where people make investments.” So that way you can
get a good picture of where we stand on happening events or things that we’ve
addressed in the past.

  * •

I would say institutional investors want to invest in AI companies that are
very mindful of how they’re applying AI and doing the thinking for the
investors essentially being like, look we are aware of all of the problems at
large and upcoming regulation and all of the confusion around it. And we want
to stay ahead of it and educate people about it and do it as transparently as
possible to make sure that people are comfortable with the solution before it
is deployed in mass.

  * •

We’re not as big as the giants. Yes it is a David versus Goliath story. Yes
I’m not denying it. Of course we’re David. We’re probably even smaller than
David. We’re probably toddlers when we consider the competition. But at least
we have the gumption and the courage and the guts to think that we could solve
a huge problem in the market by using the AI and putting democratization in
the hands of people to use [the tools of industry experts].

  * •

Also you can look at my age going into an industry that’s very old, very
white, so they kind of discount you, write you off, those typical things. In
fact, one of the CEOs of a large [company in the industry] is also Black. And
we had a call. He was on the call because I was obviously pitching the company
to bring them on as a customer. There are so few women and Black people in the
[the industry], especially in the forms of leadership and power. [This
individual was in] a very unusual position by the standard, which is him being
the CEO of this huge company. He reached out to me afterwards and said,
”There’s [very few] of us. I want to make sure that I see you succeed, because
what you’re doing is incredible and it’s strong but you have to make sure that
you play the game. You have to play the game a little bit with people because
unfortunately that’s just how it is, but I want to make sure I do everything
in my power,” is this what he said to me, ”to make you another successful
Black leader in a community that doesn’t really reflect us.” INTERVIEWER:
That’s really interesting. What do you think he meant by play the game?
PARTICIPANT: Like the good old boys type of people, they’re typically pretty
conservative or they’re [typical workers in entry-level positions], but the
higher ups, this is just how they think. And I’ve seen it. I’ve spoken to a
lot of these people and the way that they think is fundamentally different
than the way that me, or I think anyone that’s close to me thinks, so you have
to kind of dress the part, speak the part, walk a straight line, be careful
with who you interact with. It can be a little political, so we just got to
make sure you know how to walk the walk. INTERVIEWER: That sounds really
difficult. Can you think of examples where you’ve changed the way you talk or
how you’re presenting to companies? PARTICIPANT: Oh, definitely all the time.
Even my COO who just started, he even notices. It’s called code switching.

  * •

Hiring in universities is a big thing. So it’s very hard to find women
engineers, which is something that we’ve been looking for and bringing more
people that just aren’t the norm into positions of high power. So that takes
looking at different places for recruiting. You don’t look at the Harvards and
the Princetons of the world, because you know exactly what you’re going to
get. You’re looking at the Howards and the Spelmans of the world to kind of
pull those people. […] You’re even helping in mentoring kids in high school,
kind of getting there, kind of catching them a little early, even in college
and bringing them up in that light. INTERVIEWER: […] Howard and Spelman are
also elite institutions. Is there a ton of competition then for candidates out
of those kinds of pools as well? PARTICIPANT: No. No. INTERVIEWER: Really?
PARTICIPANT:Yeah. A lot of it’s still posterity from what it feels like. My
COO came from [another company in the industry] and you’re seeing these career
fairs and a lot of these big companies aren’t even going there. They’re not
even looking. If they do, they’re not hiring anybody. They’re not making the
decision to do it. It just seems like just PR.

  * •

And [a related company], I guess were founded in [a few years ago], but
they’ve done a really good job of democratizing access to data, and helping
people understand their data and create some actions around them, that can
actually help improve [an aspect of user health]. INTERVIEWER: So, would you
say that that’s similar to the mission that you’re trying to achieve?
PARTICIPANT: I don’t know that I would say it was similar to the mission
necessarily, but democratizing access to data and helping people improve their
life, yes. That’s similar.

  * •

If you have a model that is more accurate [for white people], then it’s not
really useful if you have a very diverse crowd of people and considering the
fact that we operate worldwide, we’ve got to make sure that we’re covering all
nationalities and ethnicities and all that as best as we can.

  * •

[Referring to the security industry] Their whole job is to over extend, to
protect the interests of whichever entity that they’re trying to protect. And
so it’s just traditionally a community and school of thought of trying to get
as much information as possible instead of just what is necessary to
accomplish a job.

  * •

[Regarding privacy] What I said was that people usually have two reactions.
Yes, I love it. Take all of my data. I don’t care at all. Just make my life
easy. Then we have some people that completely freak out and they get very,
very scared. We like being in the middle. We don’t want to be [at one
extreme]. We don’t want to be [at the other extreme] either. There are more
people who are worried than people who are not worried based on statistics, on
what we have seen. I’m always trying to understand, what are you really afraid
of? I think the fear comes from a couple of things. One, they don’t understand
how the technology works. When you don’t know how something works, you are
afraid of it. It’s default. When humans don’t understand something, we are
scared of it. This is a very useful biological trait in trying to protect
ourselves from the unknown. I think that’s what it boils down to. The second
thing is that they feel a lack of control, which is very fair. Very fair. What
I’m always saying is you shouldn’t be afraid. You should be concerned in the
following sense. If you educate yourself, you know the exact risks, the
dangers that come with any technology, and then you find ways of addressing
and mitigating it, but you have to be informed and it’s hard to inform the
public. If you’re trying to educate the market, they have estimated that it
takes about $200 million to educate the market. Lots of marketing and
education meant for promotion. We couldn’t do that alone. We didn’t see a lot
of other companies implementing things with the right safeguards. We said,
okay, what is the next thing we can go to so that we don’t have to worry about
this? INTERVIEWER: What do you think are the right safeguards? […]
PARTICIPANT: Multiple ones. First of all, the default has to be opt out. You
can have the default opt in and then people go back to remove data. That’s the
first thing. Second thing, retention. Don’t keep [data] more than you need to.
You know what you need to have another biometric enrollment […]. It’s not very
hard to do. Why keep it forever? That thing is giving control to people. If
you want to delete an account, you don’t know if they information is actually
deleted on the servers versus just deactivated. People need to have control
over that. It has been very clear and it’s not hard to do. It’s really not
hard to do. Implement it. It is the lack of will in most cases or concern
about people’s information. Once you put all of these things together, you
have already reached a very, very good threshold of security. It will never be
perfect. It will never be perfect, but it’ll be much better than what we have
right now.

  * •

But for us, we are getting more determined to be the ones that will lead
because we genuinely care. Plain and simple. Everybody in our team, we
genuinely care. I know we will not get everything right. I know we will make
mistakes. I know we will have backlash. I can see all of these things, but
when you want to be leading in anything and you want to drive progress, you
have to be willing to take the punches. That’s our approach. But going back to
your original question, the only concern we have on this one, the biggest
concern I would say is that [companies who over promise and under deliver] are
diluting the market in the following sense. If you’re a decision maker, let’s
say you are the CMO of Coca-Cola. You really care about privacy and you want
to select the best company and you really care about the results and you make
the investment. If you work with a vendor that they’re not so good and they
offer promise and you get PR nightmare or whatever, it’s very unlikely you’re
going to try again. They are delaying innovation. They are delaying innovation
that makes it harder to get good solutions out to the market.

  * •

I think to some degree [beliefs about privacy] can be generational. There’s a
shift towards privacy in younger demographics, in technology. There’s another
thing that has happened that most of the people, I don’t know if I’ll say our
age or younger, they’re starting their own businesses or they’re getting to
the C level positions. They were born and raised with phones in their hands,
with technology in their hands. They are more receptive.

  * •

We have investments from private individuals, and while we were trying to
raise funding, there were some investors that they really cared about the fact
that we are trying to take an ethical approach in what we are doing. They
really, really like that and supported that. There were some other investors
that were turned off by it. You see both. I heard by at least one person
saying it out loud and more than one implying it, saying, ”give [your product
away] for free and just get all the data and sell all the data,” which is not
what we want to do, but this exists. For the most part, we are trying to raise
capital from investors that have the same kind of values and mindset with us
and people who are not afraid to lose certain revenue or sales just to follow
the same values. We had clients asking us to do things that we said, you know
what? No. No, this is not something we feel comfortable with doing.

  * •

[When evaluating service providers] I think a lot about equity and promotion
of different people, people that don’t fit the ”norm.” So like women founders,
like Black, Latin, and brown people that are founders of startups, making sure
that they have a strong mission, and also have a great system or platform or
product. And so the [data labeling] company that we’re working with came as a
recommendation from a friend of mine who actually looked up their work. They
have great customer success rates, great customer success stories. Very, very
large companies utilize them, and they’re equitable not only because [the
company] was founded and started by a woman, but they also went a step
further. It really solidified [the partnership] in my mind that they went to
different parts of Africa where they teach individuals that wouldn’t typically
have access to these types of these jobs, how to do these types of jobs, and
[this company] actually employs them.