Ethics and AI in Predicting Criminality

Join Malcolm and Simone as they delve into the controversial and data-heavy topic of predicting personal traits from facial features. From the discredited study of phrenology to modern AI research, they discuss the potential and ethical implications of determining criminality, political views, sexuality, aggression, and even socioeconomic status just by looking at someone's face. With references to various scientific studies and a touch of humor, this episode offers a thought-provoking look at the intersection of technology, genetics, and human behavior. [00:00:00]

Malcolm Collins: Hello Simone. Today we are gonna be doing a very spicy episode on and very, very data, data heavy episode. On phonology for people who dunno was the study of people's skull shape and whether it affected outcomes or intelligence or anything like that. Get out your calipers.

Simone Collins: Ladies and gentlemen. This is gonna be fun.

Speaker 2: She's the sloping brow and cranial bumpy to the career criminal. Ah, sir. Ality was dismissed as quackery 160 years ago. Of course, you'd say that you are the brain pan of a stagecoach tilter.

Simone Collins: They

Malcolm Collins: used to put these big, like clamps on your head to like, measure and like certain, like rich guys, you'd go to their house and they'd like do this to you, to like, oh, it was

Simone Collins: like a party trick. Yeah. Like, you'd have everyone over and someone would get out the calipers and they'd be like, yeah.

And they had like little models of, of, of heads that would show like the different, you know.

Malcolm Collins: Here's what indicates this. One of our very wealthy friends is, is into this these days as well. Getting back into phenology. Yeah,

Simone Collins: a little.

Malcolm Collins: But apparently he is not wrong. Apparent. Well, so phenology does not appear to [00:01:00] work to my knowledge.

I, I can look more into it, but that is not what this episode is on. This episode is on just being able to tell a person's politics, sexuality, criminality. Any number of things from facial features. Can you actually like, even, even worse than phenology, 'cause phenology you need are the calipers. You need all that.

Speaker 3: Yeah.

Malcolm Collins: Invasive. What can you judge about a person just from looking at their face and how accurately can you judge?

Speaker 3: Hmm.

Malcolm Collins: And if you are like, well, okay, he's gonna point to some minor statistical differences, not that big. Let's just go right into criminality, right? Oh, because this may one of the, okay.

The spiciest. Okay. So fine. There was a study in 2016 called Automated Inference on criminality using face images. It used a supervised machine learning for classification CNNs on [00:02:00] 1,856 Chinese male ID photos, seven 30 convicted criminals and 1,126 non-criminals. It claimed an 89.51% accuracy in determining criminals.

Simone Collins: Oh no,

Malcolm Collins: that's around a nine. That's, that's 0.5. Less than 0.5 away from a 90% accuracy rate at determining who's a criminal. Oh.

Simone Collins: Just from looking at their face.

Malcolm Collins: That is probably more accurate than any other way you could determine a criminal. In fact, I bet court cases aren't even that accurate in determining criminals.

Yes. Seriously, meaningly that the mistake could be in the court case and not the machine. So if you're wondering how, how can you tell a criminal it identified structural face shield features like narrow eye, corner distance, 4%, norm distance, higher lip, cur curvature, 23.4. Wait, so the more

Simone Collins: curvy the lips, the more criminal?

Malcolm Collins: Yes. And smaller [00:03:00] nose, mouth angle, 19.6% as predictive. Which I find wild. So, and, and, and, and note this isn't the, the only paper to do this, right? So we also have predicting criminality from facial images. This was a 2020. Paper that was covered in Wired that had to be redacted and was never published because everybody freaked the F out.

But it built software that based on facial recognition stuff that was a single image could, was 80% accuracy depict the, the criminality of an individual. And you can be like, okay, well how good is an average person at this not using ai. 'cause obviously AI is gonna be like super humanly good at this.

The a a paper titled, the accuracy of inferences about criminality based on facial appearance in 2010 showed only a 53% probability. Mm-hmm. So humans are litter better than half at this. Butis are incredibly good at this. And [00:04:00] of course, a bunch of people freaked the F out after this happened. And I think we're gonna see this like as.

We DOIs on more of this stuff and we'll, we'll go into some other areas where humans are very good at determining things about people based on their face. But I wanted to start with a criminality thing. 'cause like the phenologist dream has come true in 2024. Five. Feels very much like the dream of the nineties came true in Portland, right?

Like the dream of the Phenologist came true in 2025. With AI able to immediately determine just from looking at someone if they're a criminal, but anyway, so obviously progressives freaked the F out when this happened. Right? So, there was a a, a paper, the. Criminal, the criminality from face illusion.

2020 trends in cognitive sciences. Reviewed claims like the 2016 study and found no valid evidence for predicting criminality from faces. Arguing that algorithms detected artifacts like smiling bias and dataset rather [00:05:00] than innate traits criticized as pseudo silence reviving ology. Here's the problem.

Okay. First they claimed that it was the non-criminals who were smiling. Yet we actually know that the uplifted lips was the 23% tied to criminality and the original dataset, right, which implied it was the criminalities who were doing this. The, the, the worst is that I imagine what they're saying here.

They're saying that from a facial expression was a 90% AC accuracy. You could determine who's a criminal. That's just as bad, first of all, but also stupid and obviously wrong, right? I can't determine, and I don't think an AI could only boiling it down to facial expressions, who's a criminal just from the facial expressions they're wearing.

That is significantly less plausible than me being able to determine it from facial features, which are correlated with genetics, which we know [00:06:00] from polygenics cores correlate to criminality. I.

Malcolm Collins: But most of this paper, if you go over it, is just whining about phenology and racism and blah, blah, blah, blah, blah, blah, blah. So obviously they are at a, a freakout about this.

Any thoughts before we go further? Simone?

Simone Collins: I feel like Palantir must already be doing this as much as other people are freaking out. I can't imagine a world in which I, this is not being used.

Malcolm Collins: Yeah. I, I, by the way, love the progressive, like, pushback on this. It's like, ugh. It, 90% accuracy. It must have been looking at facial in the other paper.

80% accuracy. I oh, we don't want these in courts. Imagine the freakout, right? You just, you just put on our minority report in courts. You walk into the court and it's like, this is criminal, non-criminal, criminal, [00:07:00] non-criminal.

Speaker 4: When the Precogs declare a victim and a killer, their name is embedded in the grain of wood. I'm sure you all understand the legalistic drawback to pre-crime methodology. Let's not kid ourselves.

We are arresting individuals who have broken no law, but they will.

Speaker 5: You're talking about predetermination, which happens all the time.

Why'd you catch that? Because it was gonna fall. The fact that you prevented it from happening doesn't change the fact that it was going to happen.

Simone Collins: Yeah, it's like walking. It's, it will, I mean, yeah, maybe the TSA will start using these just like, well,

Malcolm Collins: come on, come on.

I want my minority report. AI just, just looks at people and then it, it'll be there's a show, psycho psychopath, psycho filter, anime. Great anime.

Speaker 6: Our target's name is Noboo. Okra. A street scanner flagged him during a hue check. He was ordered by a security drone to receive therapy, but he panicked and ran. His recorded psychopaths was forest green, so he can expect his aggression and compulsion to be high. [00:08:00] Why would somebody choose to let their hug get so cloudy over receiving treatment?

There's a strong chance he's using incompatible narcotics.

Malcolm Collins: And it's a world where they can determine criminality before it happens. And the people who pass, but who don't look like criminals but are still criminals, their, their brains are harvested to create the giant AI network that can.

Detect criminals. Very dystopian, very cool world without any murder. Like the guns that the cops use, like stun people, but they can like turn into a second mode where they like get bigger. When they do go lethal, it's, it's a good enemy, but I'm just saying you guys should check it out. If you haven't seen a psycho filters a good enemy anyway.

Let's go to political affiliation or voting.

Speaker 3: Hmm.

Malcolm Collins: All right. Can you tell the way somebody's gonna vote by their face? Facial recognition technology can expose political orientation from naturalistic face images. 2021 scientific reports, algorithms predicted liberal versus conservative [00:09:00] orientation with a 70 to 73% accuracy.

Note, this is before AI just algorithms outperforming humans who could only do it 55%. And personality questionnaires that can only do at 66% and persisted after controlling for demographics 65 to 71%. So we are getting 70 to 73% accuracy from the system that outperforms even personality questionnaires.

Simone Collins: That's crazy.

Malcolm Collins: Cues included head orientation and expressions. Liberals showed more surprise. I love Liberals are just constantly surprised, more surprised in dealing with scientists.

Simone Collins: I wonder if this changes too. Like if we were to get a picture of me. In my liberal phase and then a picture of me in my conservative phase.

Like would my, you were

Malcolm Collins: always secretly conservative from the moment I met you, Simone,

Simone Collins: but when people change it, political affiliation, like genuinely, do you think that they are,

Malcolm Collins: you look like Winslow's wife from like [00:10:00] that old farm painting, right? Like I think it's Whistler's

Simone Collins: mother.

Malcolm Collins: Whistler's mother.

Yeah. You, you look very. Naturally conservative.

Simone Collins: Yeah, I know you mean that as a, a good thing, but our audience will recognize it as the sick burn that it is.

Malcolm Collins: Right. The whistle on like what a bird saying. Your wife, I think my wife is a very beautiful and champion woman who has, who has this,

Simone Collins: looks just like, or like, the American Gothic wife.

Yep. Yeah. Thanks.

Malcolm Collins: Okay. Shut your face. Shut your stupid face. Simone. This is not why I married you, Uhhuh other people were saying life goals. Enter my little pony episode to, to be able to have a wife who you talk for an hour with about my little pony, pony Lord,

Simone Collins: trade offs. Gentlemen, unless you want your AI yfu, in which case you'll just get everything.

So just except for children, you know? Well, you could have, mine will die with you. Yeah, I mean, I guess we can wait for artificial looms, but I, I think

Malcolm Collins: No, no, I mean, [00:11:00] artificial, just have the AI act as children. We'll, we'll build on this for you. We're building our fab.ai where we will have we're working on a sentient version of ai where it will have a persistent and separate memory from what you are interacting with that represents this consciousness.

And we can create children and stuff with that for you and your AI yfu. Oh my God. What? What? Yeah. Give people an option. Absolutely. Okay. We want to help the, the week go into their sweet goodnight. No longer a part of human civilization. Anyway predicting political elections from rapid and Unreflective face judgments.

2007 PNAS. Rapid competence judgments from candidate faces predicted US election outcomes. EG 68.6%, gubernatorial 72.4% Senate in 2006. 100 MS. Exposure matched longer. Ones deliberate. A deliberation, reduced accuracy versus quick judgments. So [00:12:00] basically flashing people pictures of politicians within their districts and being like, what do you think had a, a very high accuracy in predicting the outcome of those elections?

72.4% for Senate elections.

Simone Collins: Wow. Oh, and this, yeah, this tracks with all the other research that involved asking people who you think looks most presidential and those people just. Happen to win very consistently.

Malcolm Collins: Yeah, maybe. Maybe. I mean, it's, it is, it is wild. Which is why I should be, do I look presidential to you, Simone?

Simone Collins: I don't know, but I'm thinking that we can genetically engineer children to look presidential

Malcolm Collins: if these characteristics are so, Simone, are you and I gonna create clone high. Oh, I'll put the clone high intro here. We're we're gonna create clone high, right?

Speaker 9: in the 1980s, secret government employees [00:13:00] a.

Simone Collins: Well, I mean, the Kennedys, they tried, right, but the problem was they, they couldn't really shape who their kids were, genetically or behaviorally.

We can Malcolm

Malcolm Collins: to create the perfect Collins. Yes. I love you. You're a crazy woman. I, oh, there, I was talking to her today and I find it very weird. I was saying like of these sort of like. Darwinist focused on leaning into technology and improving, like, like this, this tech forward version of, of, of moral context.

You know, we want to lean into technology as much as possible to improve humanity as much as possible and take to the stars one day. There isn't any other mainstream influencer like. I'm not even like conservative influencer who has this perspective. [00:14:00] Obviously Elon exists, but I don't consider his main job as being an influencer.

But outside of us, I'm not sure of any of the major talking heads that have this really clear moral context which I've been a little surprised. Actually because, you know, it's such a part of our pop culture and sort of the, the cultural waters that we as humanity swim in, that this is like a major faction and it is a major faction.

I mean, I know many other people who secretly hold these beliefs, but it, it is just not a major faction among talking heads.

Simone Collins: Yeah. Fair points.

Malcolm Collins: Weird. Anything about like Noor Asma, gold leaflet, like all the major conservative influencers who I like watch these and these are the ones who are like, are sorting for me, watching them.

They don't hold this perspective at all. True, but yeah. Weird. Anyway. Income or social status, can you tell that just by looking at somebody's face? Hang on.

Simone Collins: Is there a rich face and a poor face? No.

Malcolm Collins: The visibility of social class from facial cues 2017 Journal of Personality and Social [00:15:00] Psychology, perceivers accurately categorize social class high versus low income from neutral faces.

This is with no expression. Above chance accuracies are 52 to 64% across studies. Even with rapid exposure, positive effect in median adjustments with rich faces rated higher for employability.

Speaker 3: Oh boy.

Malcolm Collins: Perpetuating, inequality, smiles, obscured cues. I, I, oh,

Simone Collins: interesting. Okay. Okay. So smile, smile.

More people

Malcolm Collins: smile. And especially our female viewers. I, I want this on the record. I'm telling you. A trigger smile more. Yes.

Simone Collins: Yeah. I don't

Malcolm Collins: understand how that became like a negative thing. Like you were saying your mom saw that as a negative thing.

Simone Collins: Oh, my mom. Yeah. It was like a big thing for her that she had a job and they asked her to smile.

Malcolm Collins: The nerd. You do need to smile if you're in a publicly facing job. Well, she was a receptionist too. I mean like of course she has a smile as a receptionist. Jesus,

Simone Collins: you scowl at everyone is think like what is, what is. Anyway, women smiling, like [00:16:00] don't tell. It's like it. I feel like it's a version of telling women to calm down, like we're not allowed to say certain things.

Although, let's be honest, I don't. When has telling anyone to calm down ever actually worked?

Malcolm Collins: I'm, yeah, I'm gonna be honest here. You know, people get mad at us because we do corporal punishment with our kids. What I wanna know is why I can't spank a receptionist anymore. Anymore smile enough. You know?

Oh my God. This is, this is when society fell off. Anyway, okay, so next year, am I gonna get in so much trouble? Well, no

Simone Collins: one spanked. They just, they just grabbed their asses.

Malcolm Collins: That's fun. That was punishment. No, I think there was spanking in offices back in the day. No, you,

Simone Collins: you spank men. You squeeze women's asses.

Malcolm Collins: No, Simone, this is a common saying from husbands to wives with spanking. Yeah, but not receptionist.

Simone Collins: You squeeze a receptionist ass and that like in the office you spank a man. I don't think so. Little, little, [00:17:00] little, you know, little football little. Just a little,

Malcolm Collins: you know what I'm talking about? If, if a guy actually did one of those football spanks today, like everyone would turn to them and be like, what the, like Actually we should have mentioned that in our depravity of the 1950s episode.

That's actually pretty messed up. The football spank like, yeah. Or, or the guys would whip other naked men with towels in the restroom. Yeah, that hurts in like, the, you know, the men's locker because these are locker rooms

Simone Collins: stuff towels are wet. It's, it's, it's a wet towel whip is

Malcolm Collins: that, that could break skin.

That's that, that is BDSM stuff was a normal, you could just subject any other man to that walking around. Our ancestors were truly debauched people. Yeah, they were.

Simone Collins: But come on. So fun. So fun,

Malcolm Collins: inconsistent facial cues to social class across two different Western contexts. 2025. Assertiveness of positivity, consistently [00:18:00] queued higher with higher social classes in Canada.

And this is in photos and Iceland. Mediating actual perceived class links, eg. Attractiveness accuracy varied by context. So basically yeah, attractiveness, attractiveness and, and positive facial expressions are how you tell somebody's rich. That tracks.

Simone Collins: Well, it does to a certain extent, extent, cost, money to do both, right?

Because if you, if you have a miserable job, if you don't have enough time to sleep or eat well, or take care of yourself, all of these things affect both your facial expression and mood and how attractive you look so. Yeah, that makes sense.

Malcolm Collins: That's depressing. Do you wanna do gay versus straight? We gonna get in trouble with this one.

Of

Simone Collins: course we are. Are we, can you tell

Malcolm Collins: if data are real? Can you tell if somebody's gay from their face? Yeah, about the voice ones. But can you tell from face. So deep neural networks. So this is like proto AI from [00:19:00] 2018, right? Deep neural networks are more accurate than humans at detecting sexual orientation and facial images.

Deep neural networks predicted sexual orientation from dating profile was an 81% accuracy for men Wow. And has 74% for women, whereas humans are only 61% for men and 54% for women. Facial morphology, nose and jaw, and grooming cues contributed, suggesting prenatal, hormonal influences. Ig, these people are phenotypically different.

It is biology that's causing this.

Simone Collins: Yeah. That's on its own is super interesting.

Malcolm Collins: Yeah. Se 81% is 71%, 81% for men. It's wild. A replication study. Machine learning models are capable of predicting sexual orientation for facial images. So somebody did a 2019 replication study and what they found was 68% for men and 77% for women with humans at 56 and [00:20:00] 58%.

Simone Collins: Wow.

Malcolm Collins: Respectively. So yeah, we're, we're, we're consistently, I, I find it interesting that that one had such good luck with women, 77% for women.

Simone Collins: Yeah, especially 'cause women are less, at least, I don't know, per, per my view, I figure women aren't really that oriented along gendered lines, but this kind of implies that they are more than I thought.

Yeah.

Malcolm Collins: You wanna do aggression levels. Can you tell with your, your caliper of ai how aggressive somebody is? Oh my God. We should put my face into an AI and ask it what it thinks my personality is.

Simone Collins: I, well, that's, yeah. After hearing all this, I want an AI that does all of them, like all how, how wealthy.

Malcolm Collins: Oh s**t.

Religious, conservative. That's a good product. A lot of people would pay for that,

Simone Collins: right? Like, tell me everything about this person. Like how likely are they to be a criminal, to be wealthy, to be gay versus straight, to make a lot of [00:21:00] money to be aggressive, like, I mean, well basically

Malcolm Collins: stereotypes are good and you can tell a lot about a person just by looking at them.

Simone Collins: Yeah, but, well, no, not, not you. AI people are not that good at this. Like a lot of the studies you referred to, might as well be 66.

Malcolm Collins: Yeah, 60% For, for people AI is 80, 90%.

Simone Collins: Yeah. 50. Yeah. I, no, you don't trust people. Apparently people actually kind of suck at this, which makes sense. 'cause there's all this stuff about criminal profiling by just looking at thesis and make micro expressions.

But no, no, no, no. Criminal profilers

Malcolm Collins: catch lots of criminals.

Simone Collins: They do. I know, but like not as well as ai. I want an AI that tells me all this stuff and then I wanna run everyone. Our kid dates. Through this, and then I also like, oh my gosh, when we do matchmaking. If we create just like profiles for all of the children in our networks, and it's like, well, according to the AI analysis of my child,

Malcolm Collins: yeah.

We're not even gonna do the genetic stuff. We're just gonna be like, face [00:22:00] faces is how we're gonna do. Oh, and

Simone Collins: we can do apologetic scores too. We'll just do like the full thing. You know, you get, well,

Malcolm Collins: everyone thinks that we're brother and sister, so we must have very compatible faces.

Simone Collins: Yeah.

Malcolm Collins: Or we get constantly called that on, on like when people get mad at us online.

The other one is that we're a lesbian couple.

Simone Collins: So sweet, so sweet.

Malcolm Collins: Whatever, you know, show, show your true colors, buddy. You gotta attack somebody's looks.

Simone Collins: Yeah.

Malcolm Collins: We, we will out breed you, so you go do your thing.

Simone Collins: Yeah. I, I, I have, every time I, I click through to the profile of someone. That criticizes to us. I, I'm just immediately like, oh,

Malcolm Collins: are they, we say a fat catwoman.

Simone Collins: Well, or they're a coward. And they have a completely hidden private profile and Okay. Yeah. Yeah. That's Aha. Wow. Ooh, you're

Malcolm Collins: so

Simone Collins: proud of yourself to put your money

Malcolm Collins: where your mouth is, buddy.

Simone Collins: Yeah.

Malcolm Collins: Okay. Men's facial wits to height ratio predicts [00:23:00] aggression. A meta-analysis, wait, facial

Simone Collins: width. So if they have a wide face.

Malcolm Collins: Sorry. Yes. Meta-analysis of existing studies found that a small but significant. Positive correlation are 0.11 between men's facial wits to height ratio and aggression. Robust across measures, but stronger in a lab setting and in the field. So basically having a further face makes you more aggressive.

Wait,

Simone Collins: so having a like narrow, tall face is more aggressive, or having a wide short face is more aggressive.

Malcolm Collins: I don't know. Wait, the widths divided by the height, so it'd be a positive correlation if the widths increased and the height did not increase. So it's a short fat phase

Simone Collins: is more aggressive,

Malcolm Collins: which is what I associate with aggression.

That

Simone Collins: totally makes sense. Even if you look at cartoon stereotypes of the, like meathead, bully strongman, [00:24:00] they got that round short face. That's crazy. That's crazy.

Malcolm Collins: Yep. Okay, next, in your face, facial metrics predicted aggressive behavior in the laboratory and in varsity and professional hockey players.

2008 proceedings in the Royal Society. Men's WH. W wait. FWHR was larger than women's and predicted reactive aggression in lab tests. 15% variance explained for men and aggressive behavior via penalty minutes. Being more harsh, 29% variance and professional hockey players, 9% variance. But this 2008 stuff really doesn't matter to me 'cause they didn't have AI at that point.

Yeah, I still get big five personality traits. Hmm. What? Hmm. Okay. You tell somebody's entire personality from looking at their face. We really need somebody to train AI on all this. You're absolutely right. I want this. Assessing the big five [00:25:00] personality traits using real life static facial images. 2020.

A cascade of artificial networks trained on self-reported big five personality scores. Was statistically significant for both sexes exceeding prior studies using selfies. The mean correlation observed and predicted scores was 0.243 with the highest for conscientiousness, 0.36 for men, and 0.335 for women.

So basically you can get an A i really good at this. The effect of facial features on first impressions and personality. 2014, several self-reported. Big five personality traits including. Extroversion were readable and above chance from neutral faces and facial features influencing the first impression that correlated with personality.

However, prediction models could not reliably infer personality from facial features on or first impressions though. First impressions were inferable from features interesting. People [00:26:00] assess faces similarly on average, so people have the same first impression of you on average, whatever you do, just based on ouch.

What you look like,

Simone Collins: looks matter. It's just so rough.

Malcolm Collins: So what are your thought phonology is, right? Are you not? Because I remember when our friend was into Phonology and you acted like he was a fool. Like

Simone Collins: how Well, and I think there's, there's additional research that people have linked to that I haven't looked deeply at that are like, yeah, bigger heads, more intelligence.

Malcolm Collins: Well that's true. My head is giant, by the way. My head is tiny. All of our kids are in the top 1% though.

Simone Collins: Yeah, well that's 'cause my mom had a giant head. Your mom had a giant head.

Malcolm Collins: So yeah.

Simone Collins: I'm just glad my head's so soft. We'll see your

Malcolm Collins: compacted little brain. I can only

Simone Collins: fit so many tiny thoughts in my head at one time and it's a lot less stressful.

I'm sure it gets real tough for you having, well, you are a woman. Big thoughts in there. Yeah. [00:27:00] Yeah, it's I love, my ignorance is bliss. They say,

Malcolm Collins: I don't mean to burden you with all these ideas, Simone, it's

Simone Collins: okay to go in one ear, ear and out the other. As you know, right over my head. I, no, I just, I think this needs to be a product.

This totally needs to be a product. I want it. I will pay for it. It it, honestly, to me, I would use it. I would use it more than background checks. Like all those investors who did these personality tests, absolutely. Background checks on it make so much

Speaker 3: better.

Simone Collins: Like one you can, oh my God.

Malcolm Collins: Yeah. Earning potential.

If I could just look and be like, who's gonna be a successful entrepreneur?

Simone Collins: Mm-hmm. Yeah. Yeah. Like you have the face of a winner, but then people would start getting cosmetic surgery, I think, to mm-hmm. Look like they have. One of those are like, yeah, I have good jeans. Like, marry me. I'm so sexy and I'm like, I think, haven't there been men who've sued their wives when they discovered that?

Like what they used to look like?

Malcolm Collins: I think it was an urban legend. I don't know. Maybe it's true. It

Simone Collins: might be. I, I could see someone [00:28:00] getting really pissed. 'cause I think a lot of people do select partners expecting to have attractive kids. But that's why you gotta look at the mother. The girl you're dating or like the father of the

Malcolm Collins: Not that I was always told is look at the mother to see what she'll look like when she's older.

Simone Collins: Yeah. 'cause that is, man, it's underrated. And I mean, especially if they have a lot of siblings and you can see like, I, here's some variation. It's, it's pretty good. Yeah. Yeah. I, I like this. I like this also because one of the toughest lessons that you taught me when we first started dating was. That people will not judge me based on my merit and actions, but rather by how I look.

And I said, no, that's not true. You're completely wrong, Malcolm. And you said, okay, well just try me. Okay. Dress more professionally for once. And I did, and suddenly I got a promotion and I was treated completely differently and I was invited to speak at places. And [00:29:00]

Malcolm Collins: yes, I am a genius for saying the obvious things that society hides from you.

That

Simone Collins: was rough. I mean, it, it does suck that society's like, no, it's, it's what's on the inside that counts. You go, girl, you're beautiful. I think a lot of people argue that that's actually mate blocking behavior.

Malcolm Collins: Yeah, I've heard that too.

Simone Collins: Yeah. It's like, yeah, you, you go girl, you're

Malcolm Collins: just like trying to, they're like, Hey girl, you look great at that weight.

Simone Collins: Yeah, great. Start just talking about

Malcolm Collins: any guy you didn't.

Simone Collins: No, you don't need great. What a disgusting cad. You're beautiful the way you are. He's wrong, not you. Yeah, yeah, yeah. I, hmm. The, but this also really, it's, it's, it implies that there could also be like, let's say that there are fundamental Christian communities that are deathly afraid of having gay or lesbian children.

This does imply that there could be some kind of medicinal regimen that you can go on as a parent. [00:30:00] To like significantly reduce the odds?

Malcolm Collins: Well, no. What it implies is you can just face, scan them as babies and then expose 'em. It's too

Simone Collins: late. It's too late. No, you gotta do it in utero. But I, I do imagine that because there's all those theories too about like the, the, the, well not theories, like the odds of a son being gay if he is born much later in birth order are, are much higher.

I mean, you know.

Speaker 3: Yeah.

Simone Collins: Not, like, didn't, like 10 times higher when it's like 0.001% is still like low odds or whatever. Yeah.

Malcolm Collins: But the odds of being gay are not zero zero 1%. They're, yeah. It's actually 10%.

Simone Collins: Yeah. Like non trivially higher. I imagine and like who were the ones who have the large families actually, and I actually knew a Catholic family that had a ton of sons and some of the, how, how many, how many were gay?

I don't know the exact count, but like a bunch of the younger ones.

Malcolm Collins: Ooh. Like [00:31:00] a, like a, yeah, so, but like,

Simone Collins: just given that a lot of the people who are having high birth count are also a lot of the people who are like. I really don't want my kid to be gay. I feel like there's a product opportunity there.

Like it's the, the real version of Pray the Gay Away. Except it's, it's like supplement the gay away. Like I could just, and you could see, you know, like why sell the MyPillow stuff when you could sell anti-gay pregnancy bill? Not that I actually think that we could benefit from a gayer world. Like I was actually thinking about like, should we do an episode talking about.

You know, especially with the, the rise of surrogacy or artificial wounds and all these like, you know, cool rep tech solutions that are gonna come online soon. What is the ideal futuristic gay space colony? You know, like, 'cause we need it.

Malcolm Collins: Oh gosh, the fire island of space trouble with our fan. You're literally a problem.[00:32:00]

Simone Collins: I just think space should be gay. I don't know what your problem is, Malcolm. And also like,

Malcolm Collins: I think, I think the future of humanity should and will be sexless. When I say sexless, I don't mean genderless. I just mean I think sex is like a gross thing we do because our ancestors needed to do it to show Janes.

Simone Collins: I mean, it is. It is. Yeah. Yeah, I wonder if it's gonna be seen as a bodily function akin to like pooping. And if you don't have Yeah, but be like,

Malcolm Collins: I know, I think, I think 500 years, 600 years from now, if people go the route of genetic modification and you know, artificial wounds and stuff like that.

Being like, I had sex with my wife. This will be like akin saying today, like, well, my wife and I have like a poo fetish, like pooed on my wife. Like, you know, it'll be like, why would you do that? Sounds disgusting. That's that. You pee from there. What are you doing? I could totally see that.

Simone Collins: Yeah, no, I think, I think a lot of people, I think already actually we've reached the point at which a lot of people are [00:33:00] extremely disgusted by sex, which is probably one of the reasons why we're seeing less of it.

I also think that. I, I, I realized, you know, we keep talking about this, this tech futile future, but it, it occurred to me after I read this New York Times article about the rationalist community that was on the front page of the New York Times today. Yeah, it's a picture of Light Haven on the front of it.

Malcolm Collins: Light Haven. We've spoken there. Mm-hmm.

Simone Collins: Yeah. I, that's where manifest is, has been. Yeah. And. Yeah, it sort of frames Light Haven as like this epicenter or Church of the Rationalist movement, and it largely frames Elie Eer Kowski as like the Jesus figure or like God figure of it, where they do like weekly readings of the sequences and that, you know, he made this and that possible and.

He, he also wrote Harry Potter in message. He t

Malcolm Collins: for people who don't know, like really

Simone Collins: the article would trigger you so freaking bad because it's like rationalism invented by e Eer, Kowski Made Possible by Ellie Eer, Kowski, [00:34:00] EI, Kowski offices in Light Haven. Ellie Ezer YOWs making Google Mind possible by introducing the founders to Peter Thiel.

All these things. But it occurred to me that I feel like so many movements. Really only exists because there is a wealthy financial backer. Like all these, these conservative movements exist because they were, you know, backed by the Koch brothers and whatnot. Yeah. And then, you know, like Jan Tolin and a bunch of other very influential tech investors, really shaping how the EA and rationalist community evolved as well.

Yeah. And, and we act as though these things have organically grown, when really the only reason they exist is because there are these techno futile, wealthy benefactors that make them exist. Like would the Heritage Foundation exist if it weren't for like some very specific No,

Malcolm Collins: absolutely true. Well, and this, what's interesting about our movement is we don't have that.

Simone Collins: Yeah. Yeah. I mean it, yeah, we, we don't, well. [00:35:00] Jan Tall did make the biggest donation of anyone to the private foundation. He did.

Malcolm Collins: And it's it, but it's, it's, it's Ry compared to what all the other groups are getting. That's true. Yeah.

Simone Collins: I mean, yeah, they're, yeah. I mean, not to say that the $500,000 donation that we received wasn't game changing for the foundation.

We didn't take salaries or anything from it. We only used it toward the cause. But yeah, I just, I, I realized like, wow, like so many of these big policy movements that we have. And cultural movements that we have aren't grassroots. I mean, they may have had grassroots origins, but the only reason they went anywhere was because someone with money gave them fuel.

And it was kind of sobering because I was like, I, I keep thinking about techno feudalism is something that's going to happen in the future. When maybe it's only something that's just going to become much more obvious in the future and, and also like more fragmented and large scale in the future as well.

So,

Speaker 3: yeah. [00:36:00]

Simone Collins: Still though I'm excited. So someone please build this so we can use it. This, this face scanner please.

Malcolm Collins: Oh, is that gonna be, have to be our next major project? Building the face scanner.

Simone Collins: Can we do it like again, I guess it, I mean we can do anything. It would be difficult.

Malcolm Collins: It's not within my proficiencies or as easy as any of the other technical projects I've taken on in the past.

Probably

Simone Collins: not. 'cause I mean like all of them are using probably the same thing. I mean, what they're doing is that that typical mapping of looking at distances and measurements.

Speaker 3: Mm-hmm.

Simone Collins: And you're just turning each face into a series of raw data points. And then you just can apply kind of how Nebula genomics takes your genome and then they just apply.

Oh my God.

Malcolm Collins: It'd be so cool in the future if we have like augmented reality and when you see somebody's face, it does a face scan and like their personality trait.

Simone Collins: Yeah. You're like, so you're wearing your ray bands. Who has the ray? It would be like, so

Malcolm Collins: in the new DSX games they have it, so like it tells you like the person's emotions and how likely they are to respond to certain things and like, that's basically what it would do.

Oh my god.

Simone Collins: Functional. [00:37:00] Yes. No, 100%. Yeah. Just it's like,

Malcolm Collins: Cody wants to partner with us on this business. You let us know. We'll work with you. Okay.

Simone Collins: Morning high aggression. Oh my gosh. I would, yeah, I would, I would probably buy AR glasses if, and I'm, I bet there will be like an app landscape for the various AR glasses that are coming online.

It was just, people would buy, it'd be

Malcolm Collins: so bad if you have like a face that looks like a rapist face because you are one, and it's like every girl you look at is like. The 98% rapist.

Simone Collins: Yeah. You mean because you aren't one?

Malcolm Collins: Oh, well, I mean I think the person probably is one, but you know,

Simone Collins: oh my God.

Malcolm Collins: Or you really, the whole world know,

Simone Collins: gosh, that is really gonna mess things up.

'cause this will happen. Like if the data's out there, people will develop it and then people will make hiring decisions and. Social [00:38:00] decisions based on, I mean, I'm sure governments, you know, people in democratic societies will create legislation that blocks this from being used in hiring and. In government and in law enforcement, but that is not gonna stop private individuals from using this.

Malcolm Collins: You prevent it from hiring. But of course, the HR manager has their, you know, yeah, just personally,

Simone Collins: they're just wearing their goggles with the app installed

Malcolm Collins: Simon own branded goggles for knowing who's the, the, the, the rapist.

Simone Collins: Yeah. And we should partner with Cutler and Gross who makes these glasses.

Mm-hmm. They also made the glass like the glasses frames from the Kingsman movie. Mm-hmm. And those glasses were AR glasses, weren't they? Oh yeah, they were. Yeah. And I mean, it works 'cause they're really chunky, so we should just partner. We should reach out to 'em and be like, Hey guys.

Malcolm Collins: Right. Love you dear Decone, what are we doing for dinner tonight?

Simone Collins: So if you

Malcolm Collins: really want, we can try to squeeze in another episode.

Simone Collins: No, I know you wanna, you wanna

Malcolm Collins: get outta here? It's fine. I know. [00:39:00] I just, we have so many backlog episodes to process it. Okay. As

Simone Collins: long as we get seven days a week patron on subscribers. Get, get more. Okay. Thank you. Then I was thinking, so I can do teriyaki chicken with rice if you'd like.

Malcolm Collins: That sounds great.

Simone Collins: Yeah.

Malcolm Collins: Oh, you know what'd be good tonight? Honestly, Mac and cheese.

Simone Collins: You feeling the The cheese? You got the cheese

Malcolm Collins: or pesto? We, we've got pesto. Let's do pesto.

Simone Collins: Do you want macaroni cheese with pesto?

Malcolm Collins: A little bit of cheese with the pesto is always good.

Simone Collins: Well, would you just prefer, we should probably just do pesto.

Yeah.

Malcolm Collins: Do pesto throw in a bit of mac and cheese.

Simone Collins: Okay. Well either I'm making mac and cheese for the kids and for, no. Okay.

Malcolm Collins: You've done it before. You've thrown in a bit of the, the mac and cheese into the paste and it was really [00:40:00] good.

Simone Collins: Okay. So you want me to do that for you? Okay. So I think I'm just gonna do plain noodles for the kids, 'cause that way I can make mac and cheese for them tomorrow night.

Okay. I have a plan. Are are we, are you gonna take them out on the boat? Should I charge the batteries?

Malcolm Collins: So much f*****g, sorry, I've got all these backlog episodes to do. You know, so that means sitting down there and listening to episodes and going through and editing them, you guys don't know how long it takes to produce one of these episodes.

It is enormously time intensive because you don't just have the recording period and the research period to begin with. But then you've got me going through it and editing the entire thing, watching the entire thing, very slowly adding. Blah, blah, blah, blah, blah.

Simone Collins: Not everyone is as conscientious as you are.

Malcolm Collins: Just, yeah. Other, other producers? No. And I watch other producers and they just waste their users' time, you know, and like whatever. I try to be, you know, engaging throughout.

Simone Collins: I appreciate that. Your dedication to the craft. Okay. Well then,

Malcolm Collins: yeah, I guess that's why I'm [00:41:00] confident that this will work out for us.

'cause I watch other people and I'm like, mm-hmm. Mm-hmm.

Simone Collins: Well, and I also, there are other people who do invest a lot in editing who have grown a ton, like who actually take it seriously. So like Caleb Hammer, he puts so much work into his episodes and he's put so much work into all of his like value added money making services and membership extras too.

So I'm like, okay, maximum effort pays off for YouTubers, at least some. And then some are just lucky. And then some are just kid influencers who get 85 million views on videos of them playing with army men. All right, I'm gonna go downstairs and make dinner. I love you very much.

Malcolm Collins: I love you very much, and I'm so excited for the life I spend with you.

And tomorrow is Saturday.

Simone Collins: Yeah. [00:42:00]

Malcolm Collins: So I will get two episodes to you for the Patreon viewers. Thank you. And and Substack subscribers of course. And thank you for making sure those go live.

Simone Collins: You welcome in. I'm excited. Bye Malcolm.

Malcolm Collins: Bye.

Simone Collins: I I was thinking about how you and I just kind of have this going assumption that we're gonna die at any moment

Malcolm Collins: and our kids do too. They have all sorts of plans about what they're gonna do after we're dead.

Simone Collins: Oh, yeah. Well, I mean, I'm so excited that we're almost 50,000 subscribers. Our son, Octavian, obviously wants 100,000 so he can get the plaque because he then wants us to die.

So he can take care of it for us. He is really, he just wants to get his hands on that plaque. That placard bull?

Malcolm Collins: No. No. Yeah. He said, well, when you die, I'll take care of the YouTube plaque for you. [00:43:00] Mm-hmm. And he was very excited about this. Yeah. And I can understand why, you know, if we can get, you know, before he gets to middle school, let's get to a hundred thousand.

If we can get to a hundred thousand. A kid in middle school was a YouTube plaque, parents. Mm mm

Simone Collins: That's how you know. Yeah. I don't know. I don't know. I mean, eight passengers kids thought it was terrible. Having parents, having a mom that was on YouTube. So who knows? I don't know. I just, I think, I think maybe he'll make it to like crazy YouTube status before we do.

'cause when I look at kid content on YouTube and I look at their views, like I look at top tier YouTuber views and it's, you know, 3 million a video, 4 million a video, 1.3 million per video. Then I look at the videos that our kids watch created by kid influencers. 87 million views. It just, it's like [00:44:00] clown world views it.

I, I can't wrap my head around it. He'll be, he'll be the money maker of our family.

Malcolm Collins: He'll make, he'll make the videos for the little ones that just will write on repeat. Yeah. Well, okay, so question Simone. Mm-hmm. Episode today. How'd it do the My Little Pony episode?

Simone Collins: I, I didn't look at comments yet.

You didn't get

Malcolm Collins: the comments?

Simone Collins: Yeah, I'm sorry. Well,

Malcolm Collins: we had the interview. Somebody's making like a Broadway play on us, and that'll be fun

Simone Collins: on fertility broadly.

Malcolm Collins: Mm-hmm. Mm-hmm. And she seemed interested. She was like, wait, are you guys under any things? I need to do more with you guys. Like in terms of who you're allowed to talk to.

Oh my God, we have to go to this. Somebody's gonna be playing me on Broadway talking about, that would be

Simone Collins: fun. Yes. This would be your first experience of who is cast. Is Malcolm Collins?

Malcolm Collins: Yes. Oh my God. Are they gonna make it somebody who looks evil? Oh, I hope so. I hope I get a mustache that I can twirl. People who [00:45:00] don't know, by the way, I don't naturally grow facial hair.

Whole family

Simone Collins: doesn't. Yeah, thank, I'm not a facial hair person. I can't. Yeah,

Malcolm Collins: I'm lucky, right? I actually get a smooth face, thank

Simone Collins: God. Can he

Malcolm Collins: believe this? A guy? Was it like a I mean, not perfectly like my dad. I, I think if I get older it may get a bit coarser because my dad has a cos face,

Simone Collins: puberty hits your family at like age 55.

Malcolm Collins: Yeah. No. I, I think it might be because he just doesn't shave enough. Is, is why that happens. But yeah, I it's, it's, it that's sort, I've always been very annoyed by like the, you know, no shave November or whatever. I'm like, but nothing would happen. And everyone else in the office is doing it and they're like, Malcolm, why aren't you being a bro?

Why aren't you doing it? And I'm like, I don't grow facial hair. Best trait by like, honestly of I could have rolled like genetics and, and you're like, okay, grow facial hair or not do are, do you at all feel bad that you don't? I'm like, no, this is awesome. [00:46:00] So very, very lucky about that particular genetic role.

Simone Collins: Yeah, I I think that's absolutely fantastic. So don't change. Not that you can don't, don't get genetically modified to shift this.

Malcolm Collins: No, I know. I'll have our kids genetically modify to make it even less. I mean,

Simone Collins: yeah. Hmm.

Malcolm Collins: I, I'd prefer if I had literally none at all. Like, I, I just hate. I think it's, it's, it's, it's gross and uncooked.

Simone Collins: Oh. And I'm sure once adults can get genetically modified to eliminate hair in, like on legs, on armpits. Oh yeah.

Malcolm Collins: That'll be,

Simone Collins: people will go in right away. That, that is a form of cosmetic procedure. I would totally get. Yeah,

Malcolm Collins: if you could just

Simone Collins: stop growing something. Imagine

Malcolm Collins: removing like armpit sweat. Like

Simone Collins: instead of having Botox.

Yeah. Yeah. Just genetically removing. I mean, I don't know. Maybe we actually need it. Sometimes. Probably not though. [00:47:00]

Malcolm Collins: I, I, I think we did in a historic situation, but I don't know if we do. I mean, you might end up. Getting like a rash or something from things rubbing like, is it, I remember

Simone Collins: reading a sci-fi book where a character had been genetically engineered, I think maybe as like a, a sex bot kind of human.

And she had really small pores, but then. She overheated like a lot. It was, it was a big problem. And I, I, I don't know, I could see that being some new form of female malady, like women just choosing to get really small pores because who wants pores? But then at the same time, like, oh, right. There's a process.

What

Malcolm Collins: pores do. Yes.

Simone Collins: Yeah. We, these were evolved for a reason. Speaking of poorness, by the way a material that some people still argue is better than goretex for serving as a, a breathable, waterproof material. 'cause the problem with most waterproof materials people have used in the past to like stage drying cold environments or in wet [00:48:00] environments, is that if you sweat, then you could get hypothermia.

Because you're wet inside your clothing. Like it has, you have to evaporate away the moisture that you sweat out when you are moving around in the cold environment. And you know what they used, like what some Inuit peoples used as the proto version of Gore-Tex, I

Malcolm Collins: don't know, seal fur or something?

Simone Collins: No, no.

Seal and whale intestines.

Malcolm Collins: Oh goodness.

Simone Collins: Yeah, they just sewed them together in a way that made them like the, the, the seams waterproof, which is super impressive and it took a long time to do this to make, and you can see pictures, they. They look like, they look like what they are. They look like someone sewed together intestines and is now wearing them as their wait.

Malcolm Collins: Okay. Seal in? No. Look.

Simone Collins: Whale intestine, Inuit, raincoat. Let's see.

Yep.

Looks like [00:49:00] exactly what you think it would look like.

Malcolm Collins: Yeah, that does look exactly what I think it would look like. It's, it's a whale intestine raincoat.

Simone Collins: Yeah. But it also looks like, oddly in, in the way it's kind of puffy and, and, and sort of drawn together. It, it looks oddly modern.

Malcolm Collins: Yeah, it does look oddly modern.

Simone Collins: Yeah. It's just like a, like as if there was some gross like, skin color trend. 'cause it's kind of this, this disagreeable, shriveled, pale. Kind of like the color of my skin. It's that it just,

Malcolm Collins: I love that you think so little of yourself. Alright, I'll get started. So, let's do it. Let's do it. We're only gonna do one today.

Okay. We, we, we got started late. Okay. Okay. And I've got a lot of backlog. You gimme extra episodes. That's extra things for processing. I know, but

Simone Collins: we're trying to shift to seven days a week. Two for Patreon members. Every week

Malcolm Collins: you punish me. What have I done to deserve this? But yes, you've been too fun to talk

Simone Collins: to Malcolm.

I [00:50:00] I just wanna keep it going honestly.

Speaker 11: Oh my gosh. Is this a hospital? I get entrance, man. You're climbing on top of the entrance that, that's called running triage. You're the triage man. No, he, he means he's standing on the entrance. Well, he a triage doctor. Why are you, why are you standing on the entrance? Do you think that's safe? I just looking Mr.

If is Titan okay or not? Titan's a doctor. She's waiting for you to fall off and get hurt so she can make you all better. Right. Titan, what is that? What do you wanna be the patient or do we need a stuffed animal to be the patient? Um, we need, oh my, the.

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