Dan is the CTO and cofounder of BiasSync, a company out to create more fair and respectful workplaces by fighting unconscious bias. He cofounded the company in 2018 with Michele Ruiz, a 5-time Emmy Award winning journalist, and Robin Richards, a prolific serial entrepreneur.
Dan’s had a prolific career in technology over the last 20 years, cofounding 3 companies that have been acquired as well as being an investor, advisor and mentor to dozens more successful startups. Before founding BiasSync, Dan was the VP of Technology at Tinder, where he led all the algorithmic, infrastructure, and data science work that helped the company grow to over $1 billion in revenue. Prior to that, Dan cofounded Chill, a video discovery tool acquired by Tinder, and Newroo which acquired by Fox News Corp.
This conversation is both tactical and strategic. We dive into the issues of unconscious bias and systemic inequality that have shaped our society, and we tactically discuss how one can attack systemic problems effectively as an entrepreneur.
SCROLL BELOW FOR LINKS AND SHOW NOTES
- Key concepts
- Books & resources
- Dan’s recommended resource from NY Times to start learning about unconscious bias
- White Fragility
- Thinking, Fast and Slow
- Harvard Implicit Bias test
- The Lean Startup
- Outcomes Over Output
- Testing Business Ideas
- Other ENLIVEN episodes
- People & organizations
Andrew: Dan welcome to the show. Excited to have you.
Dan: Absolutely. Well, hello to the ENLIVEN audience. I’m excited to be here.
Andrew: I have to say, first of all, thanks for being here. This is a real treat for me, a true pleasure. You know, there’s somebody I have always wanted to have on the show. and to the listener who doesn’t know our backstory, Dan and I have been friends and used to work together for coming on a decade now at this point.
And, it’s been been a dear friend mentor. all the things. So it’s a real, real pleasure for me to have you here. Cause there are, there are actually no bullshit. There are few people I can think of who have actually had a bigger impact on my life than you. So thank you for being here.
Dan: Well, well, thank you. And, it’s just been super fun. It’s great to have great friends who are out there doing exciting things, meeting exciting people, you know, building a great life and world around them. So.
Andrew: You know, there are so many places we could go and, you know, one of the things, one of the things that’s so interesting to me [00:03:00] about you is like, you’re, you’re one of the people I think of as like a, a quiet, giant, right? You’re you’re one of the, like the real players in the LA tech scene that most people who aren’t in the game, wouldn’t know, you know, you’re, you tend to be one of these you’re you’re like a Ninja bad. You’re like one of these master Ninja types, which I freaking love.
Dan: Oh, thank you. I try to hide out a bit. I definitely liked building interesting things, but, don’t necessarily want to be out there.
Andrew: Yeah, for sure. I mean you, but you, you have such a fascinating story and we’ll get into some of that today. but one of the things, you know, I wanted to start a little bit more of the personal side and that we’ll pivot into the bulk and the bulk of this conversation, but. I was hoping you could, you know, you’re, you’re a technologist, par excellence and you’ve had such an interesting career covering quite a few industries at this point.
And, and someone from the outside looking in who, you know, has seen the work you did, with new roo, with Adley, with chill and Tappy and Fox. I mean, you’ve been like all over the place, but particularly in the tech and the media side of things, but. I’m curious to you, [00:04:00] like, what are the through lines for you inside your career other than being the technologist, but what, what connects the dots for Dan?
Dan: Absolutely. So my interest. This is in part what, the intersection of technology and humanism. So a lot of what I’ve done has been, for example, in social software where it’s, building tools for people to communicate more effectively. And then on the other side, that’s evolved into a lot of technology meets social science.
So how do we use technology in ways that, let people measure and change things in society? So to jump back a bit about that aspect, Right now, AI is becoming a big thing, but computers were invented basically in the forties. And there was this idea that developed around cybernetics and other areas that evolved to be the field of AI.
And so computers were thought of in kind of two ways, a few future visionaries thought about them for AI, whereas, A lot of other people said, okay, [00:05:00] these are useful for tabulating machines for doing accounting for doing math. and. There were millions of dollar machines. The idea that people would waste all their time on graphics on human interfaces was kind of silly.
And one of my heroes is Doug Engelbart who really invented the notion of computers, serving people of instead of artificial intelligence, of doing augmentation. So computers are there too. Augment human abilities. And I think over time that evolved in my mind from augmenting single people’s abilities to because people are social beings to augmenting the ability for groups of people to be able to do things.
So then if you go. Through just some of what I’ve done. I ended up working for awhile for Andy Vandam, who is a computer scientist. He was actually the second person ever to have a PhD in the field of computer science, and practically invented the field of computer graphics. [00:06:00] Co-invented hypertext. So really built a lot of key early systems.
So he was one of my mentors in getting into this and. really evolved a lot of my thinking on tools around individuals. So at some point I was actually in his research group and was building personal information management tools, which nowadays we’ve seen a decent evolution of,
Andrew: Where were you? Like, where is this? When, when and where is this?
Dan: I had worked doing computer stuff in biology. I kind of had a weird school and leaving and coming back thing. So I’d worked. Previously at NIH. Then I was at the Brown university computer graphics group, which is where, Andy is a professor and was the founder and working with him. I was doing some projects on the side around. Personal information management tools thinking about, this was really around the birth of the web.
So thinking about hypertext, thinking about the web and tools to be [00:07:00] useful, to improve people’s lives. So what happened was I realized that all of that was too personal. It was tools for personal information management and everyone wanted to do things their own way. So having tools for groups of people was far more important than built.
What was. A predecessor to modern social networks. So what, we built was something called corridor. That really one of our users was a guy, Adrian Scott, who built rise. One of his users built Friendster. Some of their users ended up building my space and then to Facebook. So that was the at least design and academic lineage.
Andrew: Were you always this clear about the intersection of your interests? Like you, you said very clearly when I asked you this, you to you’re really at the intersection of technology. And humanism, which is something I’ve actually never heard you say. And we’ve been friends for a decade, or were you always this clear about this?
Or how did you, like, how has this formed in your mind over time as you’ve explored the various things you’ve worked on?
Dan: I don’t really think that anyone has a clear vision of where they’re [00:08:00] going upfront in their lives and careers. Maybe there’s someone who really has this life vision, I’m going to build this. But I think almost everyone tries to weave together what they end up passionate about in retrospect. So, no, it, it w I couldn’t have clearly said it right when I started, but maybe 10 years ago, I could have said that eyeball.
Andrew: Okay. Okay. You’ve been holding out on me, man. I love that language. I love that language. As soon as you said it, I was like, Oh yeah, that explains a lot. so I love that frame to look through. So I want to shift gears a little bit here and exploring this through line. So we’re going to fast forward in time.
You know, we went back 20 odd years to kind of the birth of the early net social networks and everything let’s fast forward, about 18 years. So let’s go up to roughly 2018, you know, at this point, you’re at Tinder and Tinder had acquired your last startup where I worked. And then at this point you’re basically running tech, running all technology for Tinder in, I think it’s 2018 or so.
[00:09:00] What’s the story. What happens next? How did, what, what shifted that brought you to biasing where you are now?
Dan: Absolutely. Well, so first I was running. Part of the tech side, there was someone managing really a lot of the engineering process side, but I was doing data science and AI and other pieces like that. I had had an angel investor in my previous company, a guy named Robin Richards, who has done a lot of things in enterprise software.
And he called me up one day and said, Hey. I’m working with this woman to Michelle on this project that we’re figuring out. And we need someone who does what you do, who, builds things at that kind of weird intersection of tech and social science. And to be honest, I had an earnout still at Tinder. So I had a lot of stock and Tinder was skyrocketing.
So he said, I know you have an earnout, so you can’t join us, but help us to find someone like you. So I said, okay, I’ll at least meet with Michelle and hear what she’s up to. Then we had lunch and she filled me in on what she was doing. And [00:10:00] I said, this is important enough for the world. That at various moments you have to decide, you know, often you say, okay, am I in it for the money or the values?
And I had said a lot of times I’m in it for the values, but then you actually get confronted with the things where you have to choose based on your past. And I said, okay, if I actually am who I believe I am, that I’ve got to go and join and be a part of this.
Andrew: I love it for those who are not familiar with it. What is biasing?
Dan: Yeah. So biasing is for large organizations to be able to manage and measure levels of unconscious bias in the workplace.
Andrew: Just to set context for the listener we’re in June, 2020. And let’s just say there has been a wee bit of unrest in the world lately, starting with, we had the COVID pandemic, which is still going on, and then we’ve had massive. social unrest around, following the murder of George Floyd and everything that is unleashed.
So that has really brought into the forefront of the public consciousness, these systemic issues of inequality of racism, et cetera. And that’s actually what led me to [00:11:00] reach out to Dan to come on the show. I was like, Oh, who do I know that’s doing something right now about this really important issue.
And that was you. So I’m so excited that we could make this work. So, With all of that in the background. Just, yeah. Let’s let’s not assume, but what, just for anyone who’s not familiar with that term, what is unconscious bias? Is that the same as implicit bias? Just let’s lay a little conceptual foundation here.
Dan: Absolutely. So first thing, I think it’s useful to talk a little bit about the unconscious and one of the things that we’re finding through a lot of research is that nearly all thought. Is unconscious. So we used to have this notion of people did what you could describe it first or to predict theologically.
Oh, I reason through this and a lot of experiments have been done. And as with any experiment, there’s always controversy. And, you know, for every PhD there was an equal, but opposite PhD. but I would say there’s a pretty strong consensus in the cognitive science community that. Most, for example, their experiments, where they ask people to make a [00:12:00] choice, to choose a red thing or a blue thing, and they have them in an FMRI machine.
And then they say, when did you become aware of that? And yet they can find. signals that suggest in the brain that the decision was made before the person thought they were making a decision. So before it really entered that sort of reasoning part of consciousness. And while we could dive into details of where these happened in the prefrontal cortex and so on, because some of this is still under active research.
It’s probably better not to get too, too deep into it, but to say we believe pretty strongly that a lot of decisions are made in a very automatic split. Second moment. A lot of you’ve probably run across works like Daniel Conoman who talks about type one and type two, or system one, sorry, system one and system two, thinking where you’ve got a set of systems that really are about.
Automatic responses. You see something, you respond and then you have [00:13:00] much slower, much more explicit forms of thinking. So there’s a bunch of research that was done on how this applies to things like race and gender. And likewise, a lot of our responses to people where we make assumptions happen in an automatic manner.
Now it’s often possible. To overcome that by thinking in a much more explicit, careful way. But one of the useful things is to measure those automatic responses. So I see someone who’s black and do I make assumptions about them that are very, very likely not true, but that’s our automatic response. So first to answer your question.
At first, that was called implicit bias where people used implicit measures and as people have gotten more sophisticated about it, they’ve tended to shift towards the term. Unconscious implicit, I think is a better term for describing some of the tests and measures because there are explicit measures.
Whereas the [00:14:00] bias itself is unconscious, but more or less treat them as synonyms outside of the academic debate.
Andrew: Okay. Great. So, so now we’ve got, we’ve established unconscious bias. Is this automatic? Unconscious, assessment or reaction to someone or something that we’re, it hasn’t even hit our w our awareness yet. It’s just, but it’s happening in the background, like you said, we have, we have great evidence now and, and proof in science from, from, from neuroscience that decisions are being made so rapidly.
They’re, they’re being made before. We’re even aware they’re being made, which is crazy. but that’s a whole other like rabbit hole we could go down. So, okay. Now we know what unconscious bias is. Why is this a problem? How is this a problem? What does this problem actually look like? As it manifests in reality?
Dan: Yeah. So first thing, the problem is very widespread and is manifesting itself in all kinds of fields. We have issues around. In healthcare, we. A lot of doctors are very good at diagnostics where they see a patient and they pretty quickly know what’s going on, but because they’re doing so much [00:15:00] of that automatically these unconscious biases can come in.
And to be honest, they’re killing people where rates with exact same symptoms, rates of heart attacks are diagnosed. at much lower rates in, in minorities, there are assumptions around gender. we’ve seen this obviously a lot in policing where people will assume that a black man they meet is involved in some crime and that can lead to police getting scared and reacting with dangerous force.
So we, this is creating problems. In all kinds of areas and fields and people’s personal lives in their work lives. Now, biasing in particular is dedicated to the workplace side of it. So biasing its mission is to create more fair and respectful workplaces. And we do that through looking at unconscious bias.
Andrew: So we’re talking about unconscious bias here and. I’d like you to [00:16:00] sort of paint a picture for me in the listener about what, what ends up happening to another. We know what unconscious bias is, what effect does that have? If we say we all have these biases, we’re unconscious of them. What happens as a result of that in our organizations and writ large in our society.
So I want you to get paint me the case as it is now. And then. What is, what does the future look like? If let’s say bias think is massively wildly successful and this all works. What does a happier future look like for us? So where are we now at? Where are we going?
Dan: So for organizations and for individuals, it creates all kinds of problems, right? People. Are slighted in various ways. Sometimes this is called microaggressions where people have little attacks on them and they wear people down over time. But sometimes that leads to much bigger problems. If you have really systemic racism in a lot of systems where you see across our companies, you have issues where leadership is almost entirely white men.
You have, lots of people whose talents are not taken advantage of. [00:17:00] And we as a society really need the talents of all the people we have. You know, there are so many tough problems. so many important things are missed. People, feel bad, people feel offended. people leave their jobs and their companies.
So really this whole big host of problems happens then at a more. Company level they’re often lawsuits. So you think about this incident that happened a while back with Starbucks, where, there were some employees who, I forget the details at the moment, but basically, acted offensively towards a black customer and it caused lots of people to.
Stop going to Starbucks. And then Starbucks said, we’re going to have a strong response against this. We’re going to look at unconscious bias through our whole organization. So you’ve got negative PR and legal things, but the bigger picture is we’re really hurting both so many individuals and so many organizations that could be doing so much better.
Andrew: So that’s what’s happening today. And then what, as this problem gets solved, hopefully. And that’s what you’re working [00:18:00] towards. What is a better future look like here?
Dan: Absolutely. So I think a better future is one in which really everyone has the opportunity to. Be a part of all kinds of organizations. So there they are treated fairly as they learn, as they interact with all kinds of people in society, be it healthcare workers, law enforcement, and that ultimately we end up with a society that’s multicultural that celebrates all kinds of differences, but that treats people fairly with respect to this.
Andrew: Hearing you say that reminds me of one of the many reasons. I, I so appreciate you, which is like, we share that desire for every single person to be able to get up and go out and make their unique contribution to the world. And there’s both drivers for that and blockers against it. And I love that you are.
You know, systematically bringing all of your talents to bear all of your massive talents to bear on one of the major blockers to that problem. So I want to dive in now and let’s start to explore how you actually did this, because I think for the [00:19:00] listener, this is like one of those things. I think this is going to be a wonderful case study for people of how, one can approach a big problem that they see in the world, which for so many people, you know, People are experts on their problems, but they’re not always experts on the solutions to those problems.
And I know so many people, both who listen to this podcast and just other people, I know who don’t, who see problems in the world and Jesus, they want to do something about it, but they don’t know how right. It’s such a big problem. It’s a systemic problem. It’s like, it’s so big. How could I do something that is a very common refrain that I’ve heard many, many times.
I’m sure you’ve heard that as well. So I think this is gonna be an incredible case study for, to walk through. Like someone who is one of the best in the game that I know of. And I’ve met a lot of people in this game taking on a problem like this. So let’s go ahead and dive into the weeds. Walk me through how this started.
How did we get to where we are now? So now if I understand correctly, you all have you have working software working tooling. It is actually your. Deployed in any enterprise already, you’re working with customers. You’re [00:20:00] actually already really starting to make this difference. but how did we get here?
Like how did you go from, wow, we see this problem of unconscious bias. We know it’s a problem. Fill me in like let’s, let’s, let’s paint the picture from how, from, from that starting point of like, Jesus, this is a huge problem too. We’re doing it. And it’s working. So how did that happen?
Dan: Absolutely. Well, first let me jump back and echo what you said of, that’s why I love both being friends with you and with what you’re doing with ENLIVEN that you’re trying to maximize human potential. I think if there’s a through line to everything you’ve been working on, it’s that maximizing human potential.
So I think that’s exactly why we’re totally in line on this. So in terms of the process for building bias sink, it’s. Actually less me then, as I mentioned, this was something that, Michelle Ruiz, and others had been starting to think about before I even got into it. So I don’t want to claim credit or claim that it’s me.
And a lot of what I’ll work through is what they did. And then how my piece fit into this jumping back a bit. There’s been a whole field of research where [00:21:00] academics and others have been thinking about unconscious bias and really understanding it. And I won’t claim that I was an expert on it. I’ve generally looked at the literature over time, but I certainly wasn’t an expert.
So when looking at this problem, the way that bias thing even came about was that the friend who I mentioned who introduced Michelle and I, Robin Richards was CEO of a medium sized company, few hundred employees at the time. And. He tried to buy diversity and inclusion training for his company. And he looked at what was out there and he said, there’s a lot of this stuff, but it’s all awful.
If you know, Robin, you know, is a much more colorful language than that. It was very flowery in his language, but basically this is all awful. Right. And he called up Michelle and said, I know you can do better there. You’ve just got to solve this problem. Now, Michelle had been a journalist for a long time, so.
Her background was really a, not unconscious bias though. She [00:22:00] had done some stuff in diversity and inclusion, but in. How to interview people. And she said, whenever I don’t understand something, I go back and try and find all the experts. So she took about a year and reached out to a number of the experts in the space and did interviews with them as if it were a journalistic interview style and ask each one of them, you know, How does this work?
What do you see as the key problems and who do you think are the other experts? So she started to build up a list of who she thought were the top experts. And what did everyone say? And tried to build that as a neutral story around that. So to try and just. Synthesize what everyone was saying. So by the time I joined, she had had all of these interviews and a sense of what was going on.
So a lot of what I did was, read and listen through a whole bunch of notes and tapes and other things to see. And that then led me to academic [00:23:00] papers and other things. What we did was we said, let’s go through this. And I haven’t even mentioned all of what biases. Does because what the key pieces of biasing car is first thing.
Yeah. In large organizations, everything is really done through measurements. There’s a classic saying you can’t manage what you don’t measure. That’s normally attributed to Peter Drucker and modern management is very much done through data yet in the diversity and inclusion world. There’s been so little data that people are acting mostly based on feelings.
We said, we need to get people real data about what’s going on. So that’s the first pillar of fire sinking.
Andrew: Sorry. I was going to ask one other just for another clarifying things so that both me and the listener can follow with you. So I’ve heard the term diversity and inclusion a lot. But even as someone in this conversation, I would still not consider myself clear on the difference. So really quick, can you actually clarify the difference between diversity and inclusion as they’re used in this context?
Dan: Sure. So diversity is really about the [00:24:00] numbers. Are people from various groups, are there are women represented? Are men being represented? Are people of different races and ethnicities being represented, but you can have diversity without having true inclusion. Inclusion is. People actually feeling welcome and a part of things.
So that inclusiveness matters. A lot of you can hire a lot of people and still not change a culture. And there are lots of issues that come up around, you know, people think, Oh, I’ll just hire a woman. And therefore all women will be promoted or I’ll hire a black man. We’ll have solved this issue. No, unless you create that inclusive culture within an organization, you’re not really solving the problems now.
This has also been expanded even further. So you’re seeing a lot more people using not just diversity and inclusion, but diversity inclusion and equity saying that not only do people feel included, but that there is actual equal treatment or fair treatment. And there’s actually an important distinction [00:25:00] between equality and equity where people’s needs may be different.
People people’s experiences may be different. So equity is really includes that element of appropriateness too, of saying, okay. if you’ve been treated differently in the past, you may need different accommodations or different things to, to be a part of that culture. So you’re often seeing diversity inclusion and equity or diversity inclusion and belonging at some organizations.
So we’re seeing, the, for example, within large organizations, they often previously had chief diversity officers and you’re starting to see chief equity officer as a title that exists.
Andrew: Thank you so much for clarifying that. All right. So, so now that we understand that let’s go back. So. Pillar of advice. And Q mentioned was management through data.
Dan: Yes. So to do that, you need to be able to measure things and the. There are few different types of assessments that we’ve built. Basically the idea of BioSync is let organizations both measure levels of unconscious bias [00:26:00] and measure subjective feeling of inclusion too. So we try to do both the objective and subjective measures there.
One of the key things that’s happened is, there was a bunch of research done originally at university of Washington. Some of it moved over to Harvard and then to a whole variety of institutions where they came up with psychological tools for being able to measure levels of unconscious bias. And that was a really important breakthrough to say that, ah, we can measure.
In an individual, the amount of bias they have for certain groups. Well, before that you might have an explicit test, right? You might ask, do you feel, do you dislike this group more or less than the other, but in modern society, everyone’s going to say I’m not racist. You know, I don’t feel this way, but being able to know those levels of unconscious bias are useful for organizations to understand where the root of the problem might come from.
So. How, that unconscious bias while it’s possible to overcome at least [00:27:00] says, what do people do when they’re on autopilot mode? What do they do on automatic, which is often where a lot of problems start from. So what we built was we took those tools, those tests from the academic world, and they feel a little video game, like maybe not as fun as most video games.
And we took those and we adapted them so that they measure the sorts of unconscious bias that we care about in the workplace. So stereotypes that commonly occur, right. In the workplace, and then made them so that they’re enterprise software. So they’re deployable by these large organizations. And we coupled that with a few other tests.
So we measure things like, openness, cognitive empathy, things that, are academic. Experts have said is useful in overcoming unconscious bias in the workplace. Then we also built more subjective measures so that they, we built a culture and climate survey where people could tell us about their sense and levels of inclusion.
So once we have all of that data, the real key is people need to feel like they can do this, [00:28:00] honestly, and they’re not being targeted because we know that almost everyone has unconscious bias. Right? So we have a threshold that we say is okay, A moderate to strong unconscious bias and 70% of people, more or less who grew up in the United States.
So a strong to moderate level of unconscious bias against blacks, right? And so we don’t want companies going and saying, now we’re going to fire you because you have unconscious bias or now we’re not going to hire you instead. The goal is understand what’s going on. So one of the really key things that we do is that we say.
We’ll give the individuals their individual results. So you go through these assessments and you’ll see, okay. I have a moderate level of unconscious bias against African Americans. I have, no bias against women, so on, and then you’ll get those individual results. But then we throw away the. The keys that connect their test results from the personally identifiable information for the person.
So we give [00:29:00] companies only anonymized aggregated results, and the companies get just a picture of how they’re doing and not individuals, individual results.
Andrew: I see you, you’ve developed this sort of suite of tests, right. To measure both the bias, as it relates to diversity, as well as the more subjective inclusiveness biases that affect inclusiveness and the. If people have in that environment, and then you’re doing these, this suite of assessments, running people through it, and we’ll talk a lot.
We’re going to get a lot more into how you actually did this. I think the story is fascinating, but I just wanna make sure we kind of get the landscape the forest before we dive into the trees. So now you’re running people through these assessments and then you’re giving them. So if I, if I, the employee take this test or set of tests, I get my sort of personal results.
Right. And they’ll show me explicitly, okay. You know, I, Andrew have this bias. I have this bias. I don’t have a bias here, et cetera, et cetera. But then you’re scrubbing my data so that, you know, if I’m in an organization of a thousand people that I’m not getting necessarily penalized or targeted by the management [00:30:00] of that company.
So they’re seeing sort of rolled up, anonymous data so they can sort of see the pie chart across the organization, but they can’t zoom in on any one person and say, Hey, you, you know, you’re fired or something like that. Is that, am I getting that right?
Dan: Almost exactly. That’s a, that’s exactly what we’re doing and we let them. Drill down a bit. So we let them bucket people in things like department or level or location and office, but we do a bunch of statistical tests saying you wouldn’t be able to dis-aggregate any individual from that. So the first pillar was assessments.
And what we did was we went through and looked at, Oh, wow, there’s a way we can measure unconscious bias. Let’s figure out how to do it. And one of the things that I’m a big believer in is. Doing lots of prototyping. So lots of just little experiments where you see if something can work. On the other hand, when you’re dealing with things where there’s science involved, you really have to do things correctly and accurately.
So there was this tension there of, I [00:31:00] personally love to just build lots of little experiments the same time. This is data that really matters to people. If you’re telling them. well, having, having unconscious bias around race, doesn’t make one, a racist. A lot of people will feel that way, right? It’s like, Oh, you’ve got this unconscious bias.
It’s telling me I’m racist. While a lot of what we do is to disarm that and make it clear that that’s not what that necessarily means. You’ve got to get it right.
Andrew: As a quick aside, by the way, on that, racism, as I’m, I’m really exploring and educating myself, given all the social issues we’re going through, I wish I’d done it sooner, but better, late than never, highly recommend for anyone listening to this. If you haven’t read the book white fragility, and in particular, what you just referred to.
They talk about very, very effectively in there as a, the good, bad binary frame. And so I suggest everyone check that out.
Dan: Absolutely one of our experts, who we’ve interviewed on a bunch of videos, dr. Dolly Chu talks about you want to be a goodish person, as long as we’re trying to be good and to be better, that’s usually the thing to [00:32:00] strive for, to say, no, one’s perfect. And. I don’t even necessarily want to be perfect.
I just want to be a goodish person to try and be good. And, you know, sometimes I make mistakes and I spend all my day dealing in areas of race and gender and things that I certainly wasn’t an expert in. And sometimes I say things that offend people and I just seek to learn in each of those situations.
I just say, Hey, I’m sorry, this is what I meant to say. Could you help me be better? And. Most people. I think everyone I’ve ever encountered. I won’t say everyone in the world, but everyone I’ve encountered tends to be very open when you’re open to, Hey, I want to improve. I want to learn.
Andrew: Yeah. People are, people tend to be pretty generous when you, when you own that, you know, Oh, I made a mistake and, and, you don’t try to hide it.
Dan: Yeah. And I think that matters a lot. We’re in a very tough situation. We have just this huge history of systemic racism across our society and it infuses. Everything in so many areas of our society, [00:33:00] that it’s going to infuse almost everything that we encounter when we start to talk about these issues.
So it’s actually just really useful to have that open attitude to say, Hey, I want to help things I want to do better. And, you know, I admit that I’m, I certainly didn’t walk into this next spurt and not having lived a lot of it. I’m still not, a huge expert in a way a lot of people are today.
Andrew: Yeah, but I mean, as you’re, as you’re pointing out, like if, if we can’t even have a conversation about it, it’s hard to make it better. So I think, I think it’s such a foundational thing that you’re saying is that attitude enables the conversations, which can actually make, start to make the difference. So.
Okay. So you were, you were starting to tell me about the, the other pillars of, of the tool.
Dan: First piece, as I mentioned was data. The next piece is professional development content. So Michelle’s background was, as I mentioned as a journalist and she’s won a number of Emmy’s, so really knows how to produce video content around those stories. So what we did was we said, In that interview style. We first sat down with various [00:34:00] experts and researchers in the field and interviewed them.
Then we built transcripts of all of that. So we knew what they were saying and hired Hollywood screenwriters to write stories, to write ways to explain this right. then we filmed those scripts. So we have different forms of storytelling to try and communicate these messages. then we had the adult learning experts and the science experts come back and make sure what we were saying was true and presented and in an effective way.
So the idea was most training you’ve had to do. If you’ve had to do the California, sexual harassment training at most companies, right. It’s important, but it’s also painful and awful and such low production value.
Andrew: painfully boring.
Dan: there with your eyes closed, click, click, click, and we said, look, It was done with a good intent, right?
That we have a bad situation that we have to change. But if you produce low quality stuff, it’s not going to change people’s minds in the same way. You know, that you’re competing with Netflix. People are used to seeing really high quality entertainment. [00:35:00] In the evening. Now you’re going to say, okay, work, we’re going to give you awful stuff.
So he said, we knew that that standard was that we have to compete in various ways like Netflix. So for example, one of the things we did was there’s a famous experiment done in the 1940s by a Kenneth and Mamie Clark called the doll test. And. What they did was they took a, in their case, white and black kids.
In our case, we actually took, kids of a whole variety of races who were four to seven years old. So very young kids and. Presented them with two dolls, a white doll and a black doll. And they had to ask which one they wanted to play with, which one was good, which one was bad, why they liked them and so on.
And even about 70% of African American kids. Young like that preferred to play with the white doll and even more among the white kids. Right? So this sort of a [00:36:00] racial preference comes up really, really early in people’s lives. And that happens across our society. So we wanted to make it clearer in our programming.
Like first thing, you know, do a cold open with that, make it very emotional and gripping. So people realized. Oh, wow. This is a real problem. You know, it’s how many online training videos do you cry at the opening scene of right then,
Andrew: I’ve never had that experience, but that would be powerful.
Dan: we want to say, look, this doesn’t make you a bad person. As we were talking about earlier that. Practically everyone has an attitude Phillips really young. So it’s not something that, that we’re thinking about and trying to be a bad person. So we build videos around all of these different themes. So we did a lot of hidden camera videos, where we have a bunch of people go into a construction site and tell them to find it, to meet with the engineer.
And we have a man and a woman who otherwise. [00:37:00] Are working on the same sort of thing across from each other. And nearly everyone goes and brings the clipboard to the man. So things like that, intermixed with videos. So that’s the second pillar. And by the way, I should mention those assessments I mentioned are interspersed.
So they’re interspersed throughout these video modules. Then the third real component of this is micro learnings. One of the things we learned from our adult learning experts was. That it requires sustained effort to change one’s mind. It requires time and effort to be able to act differently in different situations.
So instead of just having this one course, one activity, one, and done what we do is we deliver monthly micro learnings to our clients. So every employee gets sent a snackable, a bit of content. that’s three to five minutes. That’s a followup of something specific they can do or change. after they’ve done that baseline course.
So those three pieces really fit together. And what [00:38:00] we do is during that initial rollout with companies, we first do basics of unconscious bias. Then the science of unconscious bias, racial bias in the workplace, gender bias in the workplace, then some basic mitigation tips and strategies. So that’s the core of the baseline.
Then each month thereafter, we send those micro learnings out that are more specific changes that has companies stay over time. We add in other topics like LGBTQ age bias, other forms of bias, we’ll add at the one year Mark or the two year Mark. So they stay a part of this and really change things over time.
Andrew: A question specifically about the offering that BioSync is putting into the world? what does it mean? First of all, what is the micro learning look like? And then how do you actually validate over time that this stuff is working? Like, how do you validate that the tools that you’ve deployed into a given enterprise that they’re working and they’re actually having the intended effect, how do you do that?
Dan: Yeah. So first thing, what is a, microlearning [00:39:00] a microlearning is a small snackable bit of content. So it’s typically a video that’s three to five minutes, which has some concrete thing you can do. And some, you know, some principles, some concrete activity you can do, and often a follow up. So we usually, in addition to having the video have some downloadable.
A PDF or other activity of what you can do if you’re choosing to, to put this into place or to follow up. So the one I mentioned earlier about expand your network. It had a video talking about why expanding your network is valuable, how you would go about it. And then it had a. Downloadable PDF. That was a set of activities you could do, to expand your network now in terms of how we measure this.
So there are a few pieces at the small level that unless you want to, I won’t get into their psychometric validation of the scientific validation of the individual tests and assessments, but in the bigger picture of, are we changing behavior, there are two [00:40:00] ways that we’re looking at this, the first of which is that.
As I mentioned, we do this culture and climate survey. So it’s how do people perceive their organization’s doing? And that’s, you know, very classic large employee survey. So send it to a thousand random employees at your company and measure all kinds of factors around inclusiveness belonging. Incidents discrimination and other things like that.
So we run that survey at various times for the organization. We’ve work out schedules at the vote may say, okay, let’s do it a little bit before you start biasing. And then let’s run it after a year and see that, that things are different within the organization. Then. The other thing we’re doing to have outside independent things is we have a collaboration.
They don’t do partnerships. We have a collaboration with the Rand corporation, which is a think tank that specializes in quantitative measurements. So they’re the original, they started the field of think tanks. And, you know, they’re, they’re the ones that you go to when you’re like, we’ve got a very [00:41:00] complex problem.
So the U S government came to them, in the forties and said, and especially the fifties saying, wow, we need a strategy around, what we do with nuclear weapons. So they came up with all the protocols that the U S government has for. How do you, you know, who has the nuclear football and how do you decide which, you know, when you put the bombers in different States of alert?
So they basically are a group of smart people who solve incredibly complex quantitative problems. And so we have a collaboration with them because they’re interested in these issues, of race and gender in the workplace. And so with the collaboration there. working on how do they measure this within workplaces and that kind of before and after and see what happens?
Andrew: Let’s just recap that really quick. Cause we kind of covered a lot there. Let’s make sure we’ve got this solid before we kind of go, go deeper. So three pillars to this, to the system. First pillar is data really, really well scientifically. Validated assessments and data to help both individuals and the management of a given organization understand [00:42:00] what’s the baseline?
Where are we? Okay. So the second pillar is a compelling professional development content, which, I just think to most people probably sounds like an oxymoron because they’ve never seen it. but you know, basically I love that question of like, okay, this is something we all have to do cause we have a real problem and let’s be real.
The current standard of what’s out there. The status quo it’s sucks. It’s it’s I mean, it is painful. It is almost painful punishment for anyone to go through this stuff. It’s just terrible. All right. So that’s your second pillar is let’s make the experience for the employee compelling and emotionally compelling, especially.
Okay. Then your third one was micro learnings, right? So basically the idea, if I’m understanding it correctly, that. No, you can’t affect significant change all in one go. It takes time to, you know, I mean, really, if you go all the way down, it takes time to rewire the brain and it takes time to rebuild different structures in the brain.
And so you’ve got to apply consistent effort over a long period of time. You can’t just watch a video and think, Oh, good. I’m done. and so do I have that right? [00:43:00] All right. So I’m curious, did you kind of go digging into the thinking and the reasoning and, and how you, how you built this? Did you have all that upfront?
Did you know that was what I was going to take? Or how did you discover that was going to be the package that was going to work.
Dan: So we figured most of it out pretty early, actually at least those first two pillars that we would need the compelling content. Cause we knew that things out there were so bad. And we realized after talking with the experts, that there was this possibility of getting data and we knew that. it had a powerful experience cause we took some of the academic versions of these tests and at least for me, it’s one thing to know, okay, we all have bias.
It’s another thing to do an activity. And a system says, Hey, you have this racial bias. And obviously I don’t want to be racially biased, but the data they’re showing in a pretty. Compelling way to me that I am, I was like other people should have this experience. So it’s in some sense, useful for the company to have that anonymized aggregated data.
[00:44:00] But I actually think the more important part is that personal experience. So having talked to the experts had that experience, and then also having thought about the content we knew those pieces upfront. We thought from a business perspective that the micro learnings and some other pieces would be, Add ons.
So we’d sell them separately. Whereas once we actually started to talk to customers, we quickly learned that, okay, this really has to be one package deal that you can’t sell. That a lot of the pieces that we thought would be sell a separate pieces, right. They were only interested if it came as a complete package.
So maybe some of those initial models is where you imagine in your head. Oh, and this will also be a great business where you sell this and this and this, you know, get tempered a little bit, but it’s still, I think, a very strong, sustainable business given the need.
Andrew: I want to understand a little bit more about how we unwind conscious bias or what we do. You about it. Right? So at this point, we’ve established that we all have it. [00:45:00] It’s a real problem and you can measure it. And so, and then if we take the idea that you can affect it over time with micro learnings, how does that actually work?
Like, so for example, and I’m curious to explore this, we’re going to mostly be talking about how, how we do this organizationally. Given. Well, that’s what bias think does, but I think it’s also interesting. Cause a lot of people listening to the show are not going to be in a situation where they work in a big enough organization that it makes sense to work with biasing.
So maybe they have to basically do this for themselves. So for example, like I took, I’ve taken the Harvard implicit bias test, and it showed me, you know, I have such and such unconscious bias and I was like, okay. Yeah. And it was, it was very compelling, but I still don’t know what to do about it. So what do we do about it?
How do we unwind this? Talk to me about that.
Dan: Absolutely. So the first thing. That was disappointing to me was the idea when we started the company was okay, what we’ll do is we’ll measure unconscious bias. We’ll do a bunch of things. That’ll change unconscious bias, and then we’ll measure it again. So companies will pay us to take [00:46:00] this test every year.
And as we dug into the science of it, that clearly was not going to be the case. It’s possible to change one’s levels of unconscious bias, but it requires huge sustained effort. And the science of exactly how that works. There’ve been a lot of really interesting studies, but the details of that outside of just being exposed to more does have an impact.
Yeah. Are hotly debated, but takes so much time and so much effort that people say, you know, you, if you’re a company, you deploy something that has this professional development. All of a sudden are your 10,000 employees all going to say, Oh, now I want to spend three hours a day. Training myself. That’s just not practical.
They’re not going to change in that way. So we realized that the goal really wasn’t to change levels of unconscious bias. They, we do hope through some of the activities will change a bit over time. But what we realized was the goal is not to change the unconscious bias itself it’s to mitigate its impacts so that the unconscious biases wouldn’t affect both people’s behaviors and day to day [00:47:00] life.
And then the, those are organizational things. So. What we instead did was that’s part of why the micro learnings became such a key piece of it that we would need to focus on mitigation strategies. Now, the next thing to say is I’m not the expert on these. What we then did was find the experts on these mitigation strategies and just in the same way, you’re producing this podcast and finding experts at different topics.
We said, Hey, let’s find experts at different topics. So we have. organizational change experts who talk more about the organizational things, personal development, people who are experts at each of these topics. And we are producing these micro learnings around their individual ideas. So that said the two.
The first, really big thing to change is important awareness. And in so many areas, people make fun of awareness. You know, you talk about, know various organizations that talk about awareness of certain diseases, but, you know, we say, okay, but we really need the research.
Andrew: We’re raising awareness. I’m doing [00:48:00] air quotes around this. You hear that a lot.
Dan: Yeah. And a lot of times I think that it’s silly, but in this case, it actually does matter because as we were talking about earlier with system one and system two thinking are the kind of quick automatic thinking versus the slow conscious thinking in a lot of situations, you can shift your thinking. If you say, Oh, I’m aware that this is a situation where my unconscious biases could impact my decisions.
So how I thought of. These 10 candidates who I met, for a position or how I’m filling out a, you know, a ton of organizations have annual, employee review processes and you say, Oh, do I think this person is less competent because I might have just had an automatic impression. Let me actually very consciously look at.
Their results, what they’ve accomplished, what they’ve done and, and compare it on a more objective basis. So taking things out of that automatic mode and moving them into a conscious mode and trying to use [00:49:00] in the cases where it’s available, objective measures can be incredibly useful. So there’s a whole set of things around that.
Dan: But the experts talk about all kinds of ideas. Like we just had a video that I don’t know if it’s released in our system yet, but I just saw a draft of where an organizational behavior expert talked a lot about expanding your network. So consciously choosing to. Meet people in different parts of your organization, people from both different divisions and departments, but different races and cultures and saying, can we grab lunch?
I know in this coven environment, that may be a bit tricky, but maybe saying, can we have a zoom call and meet each other?
Dan: Exactly. So there’s so many little things you can do around those sorts of changes that can change how bias plays out. One of the examples that we have in video that I saw was it was about a police officer who saw something about unconscious bias in.
Who they gave tickets or that there could be [00:50:00] unconscious bias and how officers gave tickets. So what he did was before getting out of his car before he saw the person who he had pulled over for speeding, he would write down whether he was going to give them a warning or a ticket. So he made the decision before he had the information in that hiding information thing, which is sometimes useful.
And sometimes not. There’s a very classic example at a more. Rather than individual level, like what he did at an organizational level. So I’m someone who loves classical music and in symphonies, I think it was the Boston symphony orchestra started this, where they used to do auditions and something like 85 to 90% of the people picked four.
Major, roles like, you know, first violin or what to make it into orchestra, were men. So what they said was, Hmm, I wonder if we’re biased in our decisions here. So what they did was they put up, a screen. So there was a screen on stage [00:51:00] and, it was between the people judging
Andrew: a blind test.
Dan: playing and you know what happened, didn’t solve the problem.
Didn’t change anything.
Andrew: Really? Okay.
Dan: So then what they did was they put down carpet and did it against another there’s carpet there and a screen cause in a, when performing women usually wear heels and, they still knew who the gender of the people coming in, but once they put down carpet and put up a screen, then it changed things to be much closer to 50 50.
And the interesting thing is that wasn’t just due to one mechanism, it was due to two mechanisms. So the first of which is. The chance that someone, would win given that they auditioned went up by a meaningful amount, but there was a second effect, which is a lot more women chose to audition because they felt like it was a more fair environment.
So it changed both of those things, which move things closer to 50 50. So that was a great example of a more structural change that’s been adopted now across the classical music [00:52:00] world.
Andrew: Especially in an environment where you, where we don’t know the underlying base rates of the distribution of the population, we have to assume it’s evenly distributed, right. That it’s equitable across things, or, you know, is it possible that there’s, you know, actually that. If you, you know, let’s say, let’s imagine there’s a, a certain skill level.
That one needs to be, to be the first violinist of the Boston symphony orchestra. And it’s extremely high bar. Right. And let’s say that there’s, I don’t know, a thousand people in Boston who are good enough to be that person. And if we assume that let’s say the underlying real distribution is that 75% of those people who are good enough to, to be that, to have that role are women.
And 25. So 750 are women 250 are men. But in this case, you’re saying that the distribution, as it was playing out in the symphony was wickedly bias the other way. So I guess there’s sort of two questions there. The first one is can we ever actually know the underlying base rates? And if not, what do we do?
Dan: Yeah. So I think the tricky part there is that’s even measuring at [00:53:00] one point, which is to say at the point at which you’re good enough to make a world class symphony orchestra, which is already very far, but what happens is the. Sorts of biases add up over time. Right? So you’ve got children. So let’s say that the, that there’s some level of talent that is innate, that we’re born with, right?
That’s what you would almost want to be your base rate, right. Of who could do it, but then certain kids are encouraged to go into classical music. Certain kids are not. Then as they’re encouraged to go into it, certain kids are. Told you’re a prodigy. You’re a genius. We’re going to promote you. You’re not.
So that creates a problem throughout the entire pipeline. So. Even by the time you make it to this, test that we made more fair of who gets into an audition and who doesn’t. I think there’s still a lot of what I’d call more structural sexism and racism there, but at least they were able to fix things at one place.
And I think the process of changing this is changing that in lots of places. Now, the [00:54:00] pipeline problem that I was mentioning before. Might not be as easily solvable by just blinding things. By making, I think of the, the solution at that late stage as being purely a, a blinding and hiding information. But there may be mechanisms like affirmative action that are relevant in those early stages of the pipeline.
And it, it, the solutions to problems, I don’t think are universal right there. As I mentioned earlier, Our site is just so suffused with so many of these issues that we’re going to have to be creative and work hard. And it’s not going to be an overnight thing to solve.
Andrew: Yeah, for sure. It, you know, as I’m listening, you say that it’s such a great point. And it reminds me of something that I learned from another guest, on the show, an episode that has not come out yet, but a woman named Nyla for merchants. And one of the things that Nyla for helped me understand was that.
Yes and power are self-reinforcing loops. Right? So the, you know, as you said in this pipeline problem, Bias or an inequity early in that process compounds over time. And so, [00:55:00] you know, maybe things were evenly distributed back when people were three years old, but then after you’ve had 20, 30 years of self-reinforcing loops, you’re left at the other end of this pipeline with an inequitable system.
So let me ask you this question as someone who has teamed up with a great group of people to go after a systemic level problem. There’s always the challenge of scoping. Right. You know, you’re at the w where you are, where biasing is addressing this problem is at the tail end of the pipeline. Right. And as you said, it seems like your first step is a mitigation step.
Right. Of okay. But maybe over time we can actually unwind and change unconscious bias. But step one is to just make it better by mitigating what’s happening. How do you think about where to draw your boundaries of what your intervention is going to be in something that you could have easily? I could easily have seen this said like, okay, we’re going to try and solve the whole pipeline problem.
We’re going to go upstream of where it is here. I’m sure you’ve seen this question. How do you advise people to think through that?
Dan: Yeah. So the first thing is I messed this up horribly early in my career, [00:56:00] where, when we had the idea for the first social software, we really said, okay, you can do this and this and this with it. And basically built something that way too early in the, the internet included. Everything that you’d think of as part of Facebook, but also of LinkedIn and Yelp.
And it had a professional area and a products area and all kinds of things and said, Oh, let’s build this. And it’s impractical to boil the entire ocean, right. That you can’t, you can’t do too much at once. So instead you do need to focus down and find a piece that works. And the one thing that I’ve learned about system design is that all big working systems.
Start as small working systems. It, it can’t repeat that enough. Every big working system started as a small working system. So what I think the most interesting pieces is to say, what piece can stand on its own? What’s one thing I can do that I can land and then expand from there, right? [00:57:00] Where I can build something that solves some concrete problem for some people who really care about it.
And rather than have. A million people who care about what you’re doing a little bit. I think it’s much more important to have 10 people who really, really care about what you’re doing. So with biasing, you know, we built software that deals with some complex fraud issues in society. And you need someone at these big companies to be the first one, to take a risk and say, Hey, I’m a multibillion dollar company and I’m going to deploy something that gets data.
From employees where they’re going to be talking about race and gender and all kinds of things like that. And so someone has to say yes, so you need to do something that’s meaningful enough to someone. You know, I was just talking to one of our first clients the other day, a guy named John Graham, who we met, who just, you know, saw what we’re doing and said, I am going to.
Have our company do this. Right. And he, you [00:58:00] know, right after we launched got a multibillion dollar company to say, yeah, we’re gonna, we’re going to at least pilot, I’m going to prove it. And we’re going to experiment and do a test launch of biasing and you need someone. Who cares enough to do that. So therefore you need to build something that matters enough to someone.
So fundamentally I think the thing is don’t try to solve the whole systemic issue. We’d love to solve the whole systemic issue, but it’s not going to happen. I want to say. So just find one piece where you feel like. Okay. If I did that, it will really have an impact on someone and someone will care about it.
And then once you have that, in our case, our clients start to say to us, okay, can you do this? Can you do that? So then they start asking you to build the other pieces and it’s nowhere near. The entire all encompassing vision, but it’s much better to have someone coming at you in particular. We live in America, right?
America is a capitalist country by and large business drives America. If you want to change America, you should change [00:59:00] businesses. As part of what we said. Now, we also work with a lot of other organizations, governmental law enforcement, but fundamentally, you know, I like to say, find someone who’s willing to pay you to solve their problem.
If they’re willing to pay you to solve their problem, they’re probably willing to pay you to solve other problems. So the way I think of it, as, you know, draft off of capitalism draft off of that fact that find something that someone’s willing to pay for you to solve it, and then keep going. Now this does lead to problems being ignored, right?
There are a lot of things that, that get ignored in that case, but I think it’s a useful way to start a lot of things where you want to operate at large scale.
Andrew: So what I’m taking away from that, just to recap, it is you have to start as part of the solution, not solving the. Entire thing, because it’s a systemic problem. There isn’t one solution. There are many, many solutions for all the different flavors of the problem throughout the system. So what you’re saying is you have to think really hard and figure out what are the high leverage places for an intervention and start there and figure out, okay, where, where, what is one thing I can do that will make an actual difference [01:00:00] that people also care about?
So they will pay me to make that difference. And that’s my starting point.
Andrew: All right. So Dan, we’re going to start to transition and close out here. So I want to ask you some rapid fire questions. There are short questions, but your answers don’t have to be. So the first one that I like to ask people is if there are any questions that you often ask yourself that help you think through things, or, you know, just help you do what you do are there are the questions you returned to, again and again that you find to be especially useful.
Dan: Yeah. So even more general than questions, I think it’s useful to have a whole set of mental models for things. So really humans in a lot of ways think by metaphor and by analogy. And so when you’re. Used to different sorts of systems. You can analogize things to those systems. And I personally follow a lot of science.
So I think in terms of a lot of, a lot of metaphors around physical systems, around scientific systems, things that come up, but I feel like I have actually a giant [01:01:00] list. I keep. Of things that occur in science that are good analogies for things or useful frameworks and mental models for thinking about things.
So then over time I try to refine those two particular questions. One of the most useful things is how will this matter? So I deal with people dealing with data a lot. And one of the things, when someone asks me, can we measure this? I ask them, okay, what will you do if the number is 0%. What will you do?
If the number is a hundred percent, if the answer is not different, there’s not clear why we should measure it. Right. And that actually comes from an area called information theory. So information theory is a lot about measuring the, the informational content of something. So what, so the best definition I’ve heard of information is it’s the difference?
That makes a difference, right? And if. You understand, based on data you get what difference it’ll make, then you can choose to act appropriately. So I’m a huge fan of asking [01:02:00] about how will you behave differently in different situations.
Andrew: Hmm. Yeah. I love that. And that also reminds me of the Josh side and episode for anyone who just listened, who who’s listening to this right now. if you’re interested in that idea, check out episode 18 with Josh. Seiden, who’s really all about strategic and outcomes thinking, which kind of goes to the heart of it is what you’re speaking to.
so let me ask you this. If you could go back to the start of whatever chapter of your life you consider yourself to be in right now, if you go back to the beginning of that chapter, would you do anything differently?
Dan: Well, certainly I’d say for some previous chapters. So, you know, I did a lot of early work on social software and we came so close in a lot of ways to building the right thing, but exactly what I said of trying to boil the ocean, trying to do too much at once, meant that we never built anything. So that was probably the biggest mistake, that I I’ve made.
And, you know, are you ugly? Had we done what we were planning incrementally rather than all at once we would have built what was the equivalent of Facebook, except I’d like to [01:03:00] think we’d do a lot of things better in terms of our behavior that said at the current phase, you know, happily married, having an amazing kid.
Like I I’ve been incredibly lucky in life. So it’s hard for me to say I do something big, differently. There are lots of small things that I do differently, but I think most of the changes I would make or in previous stages of life and not the current one.
Andrew: That’s a good thing. That’s an excellent thing to be able to say good for you. And then who or what has had a really big influence on you in terms of how you see things or how you show up.
Dan: There are so many different things that I feel like if impacted me over life, first of which is having great mentors. So Julia of our Sony, someone I mentioned to Andy van dam, people. Who I’ve had a chance to learn, learn so much from the other thing that’s really changed. My life is the chance to be a part of, of things that we’ve been building.
And as you get experience, as you get to build and try things, I feel like you [01:04:00] just learn so much by doing things wrong. And it’s, that’s a very generic answer, but it really does matter a lot.
Andrew: Yeah, no, I get that. And I I’ve, I’ve had the chance to witness that personally. So that’s, I, I I’ll underscore that one. One other one I like to ask people is, you know, if you think just recent memory, whether that’s like a week a year, whatever, but what’s a small change you’ve made in your recent memory that you think has had an outsized impact.
Dan: The probably the most recent is since we had, since we had a kid, I spend a lot more time just at home playing with our kid. And it’s incredible how much that just time for reflection and time to spend doing what’s right. Kind of nothing, but also kind of everything really changes your disposition with respect to a lot of things.
There are a lot of moments in which post that I kind of say, Hey, at this moment, I could spend time with my kid or I could pursue this thing. That might be external validation, but doesn’t. Really changed anything. And I can [01:05:00] always weigh that against spending an extra hour with my daughter. Almost always.
The answer is I’d rather spend the extra hour with my daughter. So I think that’s something that I didn’t choose to change, but changed me.
Andrew: No, I love that. I love that. and then last, last question for you is what are any, you know, based on the conversation we’ve had today, What resources or things would you point people to, if someone’s listening to this and they’ve been compelled by this conversation specifically around unconscious bias, what resources or where would you point people to in terms of start taking action?
Dan: Yeah. So first thing, there are a few levels there’s understanding of unconscious bias and there are several books, probably the best on the more. Scientific aspect is by bangy. So blind spot is useful. The book you mentioned on the more general question white fragility is actually a great book and there are a lot of books at different levels of systems on from, you know, cognitive [01:06:00] science of unconscious bias all the way up through structural racism.
And I’ll admit that I’m not the expert I’m out there. Just trying to, to learn on the. Product design side that we were talking about of how do we build these things? Some of the things you mentioned around, lean startups and other things are great resources. I’ll admit that I’m not actually up on the current literature, that when I learned this, it was 20 years ago and things are far more sophisticated today, but I followed it in a much more day to day incremental, basis rather than knowing what the, the resource I’d point someone out.
But the main thing I’d say is. Both, you know, on unconscious bias and race, go out there and just try and learn and try and do better. And on product design, which we were also talking about, it’s go and try and build things, build little things, launch them, see what people do, build things for your friends, try things, and get feedback.
And I think that that feedback loop gets you learning so much [01:07:00] faster than anything. That’s a published resource. just. Do it, do it wrong a lot of times. And eventually you’ll start to do it right.
Andrew: I love it. All right, Dan. Well, we’re gonna wrap up there. What, just in closing out, first of all, thank you so much. as I said at the start of this conversation, it is a true pleasure for me to have you here, a friend and a mentor to have this conversation. so first of all, thank you for that. Thank you for the work you’re doing in the world.
It’s. Super important. And I’m glad we’ve got, I’m glad the world’s got somebody like you working on it. So thank you. And just in closing, what do you want to leave people with and where can people reach out and connect with you? Connect with biasing. If they, if they want to get in touch.
Dan: The first thing. Thank you too. I’m so excited by what you’re doing both with ENLIVEN and, and so many other projects of really working on human development, improving. People their performance, how, how they’re doing. And so, the feelings is incredibly mutual and it’s likewise been, and continues to be great to be friends with you and for listeners, Sandra and I talk, reasonably frequently, about all sorts of, big things [01:08:00] going on.
Andrew: big and small. We also, we also geek out about food food a lot.
Dan: Yes. And if nothing else from, from hiding out with this, quarantine, I’ve been having a chance to perfect. A lot of basics D you know, food and cooking wise, rather rather than my usual, which is cook very infrequently, but try and do elaborate things when I do it’s now like cook all the time, but perfect.
Those, Those basics, which is a fun thing. And I think ties in with what we’re saying of when you get to do something over and over and make it better. There’s something also just very rewarding about that. So for someone who wants to reach out, first thing, if you’re just interested in bias, think as a company, you can go to bias.com and we have a contact us form.
And if you want to talk to more me directly, it’s just my name, dan.gold at biasing.com. So, I, I’m not too hard to find.
Andrew: Okay, perfect. And just so people know, who, in terms of the types of organizations that work with biasing or where it’s a good fit, what [01:09:00] does that look like?
Dan: Yeah. So right now we don’t yet have good tools and infrastructure for smaller companies. So it’s really a hundred person plus companies and often a much larger than that. But then sector wise, we’re really trying to work with. Both large companies, governmental organizations, educational institutions to really all sorts of organizations that want to improve.
And we’ve tried to make it, you know, easy to implement and affordable such that we can reach lots and lots of people. One of my very, explicit goals when starting this is we want to reach at least a hundred million people. And when we onboard a new company, we know how many employees they have. So at our.
A weekly meeting. The, one of the first things we announced is how many lives, if we touched.
Andrew: I love that. What a great metric.
Dan: Thanks. Yeah. So that’s actually the number one metric that we, we look at. So we definitely want folks to, to reach out to us. I’m not on the sales side, so I don’t quite know as much about the [01:10:00] onboarding process, but really any larger organization that wants to, improve on these fronts or even learn about it.
We’d be happy to talk to. And beyond that, I don’t think there are too many constraints. Eventually we’ll reach. ways to reach smaller organization and individuals.
Andrew: Yeah. Are there just. Well, who are in smaller organizations today? Are there, is there, are there any, whether it’s a book or something like that, anything they can do today to start, if they don’t have access to the tools that you guys are building.
Dan: Yeah. So a, there are some, some courses and things online and I’ll admit that I reviewed them all. last about a year ago, and I can’t tell you which specific one to go to. but if they search or I’ll, get some stuff for the show notes, that will be some recommended resources. I’ll go back to my notes and, and see which ones I thought were, were best for that actually I’ll mention the New York times did a small series.
It’s a few videos that are useful a little while ago, and I bet a few search Erik times, unconscious bias. You’ll see those videos. Right.
Andrew: All [01:11:00] right. I love it. Thank you. And we will link to all this stuff in the show notes, but, Dan, any, any final thoughts? just as we’re closing out here.
Dan: I think anyone who’s a part of the ENLIVEN community is clearly someone who’s passionate about improving themselves and improving our society and doing things in a very conscious manner towards doing so. So that’s the thing that changes the world. And I, I couldn’t be more excited to be a part of it.
And for, for everyone, who’s a part of this community to be a part of it.
Andrew: Thank you so much. That means a lot to me coming from you, especially, and again, true pleasure. My man, thank you for being here. Thanks for doing what you’re doing. Keep it up.
Dan: Oh, it’s super fun talking with you. [01:12:00]