Protecting Your Ideas While Using AI Tools with Kelsey Silver
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It's absolutely going to learn. But you are a tiny grain of sand, tiny grain of sand in the massive beach that is its training data. Even when artists worry, and I'm not devalidating their concerns at all, but when artists worry about their artistic talent being utilized, the algorithm is processing so many different inputs that it's all

going to be an amalgamation, a mushugash of all of the different learning that it's done. Just like my daughter is a combination of my language patterns, my partner's language patterns, the patterns she's learned at school, the patterns she's learning from her grandparents, the patterns she's learning from TV. So I might be able to pick out the words she used from me because I happen to know I'm one of the only ones, but I can't say that her language pattern is copying mine. The same goes for proprietary models.

Welcome to Authentic AI for Entrepreneurs, the podcast that shows you how to leverage the power of AI technology without wasting your time or selling your soul. Let's embrace making AI work for you.

Hey, hey, my human friend, and welcome to the Authentic AI Podcast. I'm your host, Kinsey, and I am so stoked to share today's guest with you because I'm sitting down with the incredible Kelsey Silver, and we are diving into a topic that so many entrepreneurs have questions about, and let's be honest, worries about.

How do AI tools actually work and are they going to steal your original ideas or proprietary content? I always get questions and worries about this. And don't worry if you've been ever feeling that little twinge of fear when you go to type your genius into ChatGPT. A lot of people worry about that. And also this episode is for you because the brilliant Kelsey is not only breaking down the complex problems,

tech stuff in a superhuman and relatable way. She actually talks about her daughter in an analogy and it's so cute. But she also brings the most thoughtful, empowering perspective to this conversation. I cannot wait to share it with you. So without further ado, let's dive in.

Hi, Kelsey. Thank you so much for joining us on Authentic AI for Entrepreneurs. I am so excited to have you on the show, lady. Me too. Me too. I can't wait to get into the details of some of this stuff. I'm very much a nerd, so this is going to be a fun conversation. Oh my gosh. And if you guys aren't watching the YouTube, if you're just listening, Kelsey was just doing like the evil fingers in front of her face. That's how I feel too. I get so excited. I'm just like, let's talk about it. So if you guys

I have not yet watched our AI political climate round table. Kelsey was one of our amazing guest panelists. So definitely go over and listen to that or watch it on YouTube because oh my gosh, that conversation was so freaking awesome. But I wanted to invite Kelsey on to just deep dive a little bit further

further into data training when it comes to AI tools. So we're going to talk all about how these tools work. And then also other things like data bias that we need to be aware of and how we can use these tools in an intentional way. So thank you so much for coming on and sharing Kelsey, let's go ahead and just kick off the conversation because I know we have so much to talk about. Can you please

share with us like a little bit about how these tools work, right? Because I know that a lot of people in my audience, especially entrepreneurs are kind of worried about, okay, I'm going to use an AI tool. But like, if I put my original thoughts into these tools, my strategies, my teaching methods, are these tools going to be trained on my data and then share my data is like my original thought going to be stolen.

And, you know, that's like a huge concern for entrepreneurs, especially. So could you please share with us your thoughts about that and kind of a little bit about how these tools work? Yeah, absolutely. OK, so we're going to we're going to get a little dirty here, but we're going to try to keep it pretty easy to understand. So I like to use the analogy of my four year old daughter in the sense that these and a lot of models kind of learn in the same way that kids learn. So the easiest way to conceptualize it that I've that I've shared with others is kids

know what an apple looks like. They know what an apple looks like because you've shown them an apple many times. They've seen it in a book, so they know what an apple looks like in two dimensions. You've handed them an apple, so they know what an apple looks like in three dimensions. They know what it feels like, sometimes what it smells like. They know what it tastes like. You've also given them an apple in slices or in cubes. Maybe you've given them an apple inside of a salad.

So the Apple in this case, in all of its different versions, is what we would call in the technology space training data.

And so the more diverse you make the training data, the better able the algorithm or the AI is able to recognize what is an apple. Now, I'm talking visual here, but it works the same with words, images, sound, all of those different things. It's just the way that you give it the AI, the training data.

So the most basic way to think about how do these algorithms learn is they learn the same way kids do by observing and figuring out patterns. So

So the way my daughter uses language that I use and picks up on some of those words that she probably shouldn't be saying at four years old is because she's seen my pattern, right? And so when she sits there and says a word and I go, oh, whoops, that's because I've inserted my own patterns into her learning training data. Right?

And so when you talk about your fear of yours, I'm saying the general we here, of the algorithm starting to learn your proprietary data, that's a very real concern. This, I think we've probably heard the most chatter about this from artists because it's very easy to see visual similarities in art, but it goes the same for science

sound. We hear conversation about someone's voice being taken and modeled. And we see the same, of course, in course creators and other creators who have proprietary models. That's why it is important

To do things like creating your own custom GPT where you can set the actual training data to private because that training data will only train that GPT that you have control over and you can delete it if you want. There's no real way in like a free chat GPT chat to say, hey, forget I ever told you this, but use it for me.

Now, so when you have settings on and there are absolutely AI models out there that allow you to turn settings on to keep your own proprietary information contained within the little environment you're working on, that's not going to work.

That's when you know your data is protected and your proprietary models are protected. But if you're feeding your proprietary models into anything where that setting is not on, yes, it's absolutely going to learn. But you are a tiny grain of sand, tiny grain of sand in the massive beach that is its training data. So even when artists...

And I'm not devalidating their concerns at all. But when artists worry about their artistic talent being utilized, the computer is processing, the algorithm is processing so many different inputs that it's all going to be an amalgamation, a mushigash of ideas.

Hmm.

The same goes for proprietary models, unless someone is specifically saying based on Kinsey's proprietary model of authentic AI, develop me a and that is absolutely possible. But they'd need to have the algorithm would need to have a lot of data on Kinsey's proprietary model in order for me to really steal it. I hope that was clear enough.

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Oh, I absolutely adore the way that you talk about data training and whatnot, because exactly it feels like this big, scary topic sometimes, or like just like one of those topics that turns my brain to mush that I don't want to talk about. Right. But like when you explain it like that, it makes a lot of sense. I'm so glad that you're not like devalidating concerns and whatnot. And at the same time, like shifting the perspective of what is like

copying specific IP and what is like find a pattern in a lot, a lot of data that then

turns into an output that could be similar to like your ideas, but it's like not exactly. I don't know. I always think about two things. Firstly, I don't know if you read Big Magic by Elizabeth Gilbert back in the day. Oh, yeah, it's fun. It's like really like a whimsical take on like creativity, but also kind of practical and stuff and just makes you feel warm and fuzzy inside, which I love. I should probably reread it right now. So actually, but

Yes. But like, so she talks about like ideas floating, being their own thing, you know, floating in the wind and like how there really isn't such a thing as an original idea. And like through history, we can see lots of people have like invented similar things on like opportunities.

Yeah.

ever. You know what I mean? So like, I always hold on to that, that like, really, there's not original ideas out there. I don't think so. Like in general, or like, we don't like necessarily own an idea. But like, also, like, just thinking about

And we'll talk about this more so, but like kind of almost a responsibility to like share your ideas with these tools. You know what I mean? So anyway, what are your thoughts on what I just said, I guess? Fully agree. And I mean, I'm not a copyright lawyer, so please, you know, don't come at me. But if I'm not mistaken, I think to escape copyright, something only needs to be changed 20 percent, something along those lines. Again, I could be off on the percentage, but even in...

in free AI. You don't have to change something all that much to no longer be considered the same thing.

And so there is, for those of you listening who are really petrified, oh my God, I could be copying someone else's work. Because I know that was my concern. I don't so much care if it copies my stuff because I'm one of those people that I pretty much share anything I could teach online for free anyway, because it's the way my brain works that I actually monetize.

So I don't care really about sharing it. I was petrified. I was accidentally going to steal someone's stuff. And then I realized that, well, first of all, I'm rephrasing it or editing the work that it gives me anyway, right? Because I want it to sound more like me, even though I've got it trained to the point now where it spits out like it could have just come straight from my mouth. I'm still going to edit it.

So nine times out of 10, I'm already editing it enough to escape that concern that I've already edited 20%. And plus, as you're working through it, you're inserting your own ideas. You're asking it to think about it differently. When I say it, I'm referring to all the models out there, inclusive of the ones that are visual. You're prompting it and inserting your own ideas throughout the process. Now, if you're on the opposite end of it,

The same concept stands, even if you feed it your entire proprietary model, someone else on the other end

If they steal it identically, well, they're probably still not getting the same grain of sand. They're getting the one next to it, right? Because the algorithm is still going to insert, hallucinate, whatever, a little bit of a change. So it's still not going to be 100% your stuff. And then they're probably changing it a little bit anyway.

The only thing that might be a thing, and again, not devalidating those concerns, they are there. But this is why it's so important to stay vigilant on your copyrights and your trademarks, which you would be doing anyway with or without AI.

The if someone was going to steal your stuff directly, likely you have enough out in the world already that they could steal it and they would have stolen it regardless. It's just now it's a little bit easier to steal it. And so you just have to stay vigilant with your copyrights and your trademarks on that proprietary IP.

And now it's just a little bit easier for them to do it. I fully recognize that I have a much more blasé perspective than many people do.

But at the same time, for me, the accessibility opportunities that AI provides far outweigh our concerns around IP. There are still ways for us to protect our IP. There are legal routes for us to protect our IP that have always been there and will hopefully continue to be there. I'm much more excited about the possibility and accessibility that provides it.

Oh, yes. And okay, let's talk about that. But first, before we dive into that, too, I just want to like point out something that was popping up on our AI political climate roundtable to that kind of goes along with this.

I'm like totally forgetting where I was going with that. Can I insert what I think you might have thought about? Yeah. So we also have a duty to help train these models. Our input, particularly as individuals who identify as female, who were raised societally in female gender roles, we have an obligation to speak that voice.

And by putting our proprietary models into these algorithms, by...

training it on how we speak, how we ask questions, the kind of information we want to see and challenging the algorithms based on the way our brains work. The algorithms are trained to see patterns. So the more of our own patterns we can provide it, the more diverse those answers will become, even to someone who's not asking those questions in the way that a female would. And so we have an obligation to interact with these models to provide it that data.

Yes, yes, yes. I love talking about this too, because it's really about like making sure that a diverse like group of people are using these tools. And and not only will that help like future models be able like will work.

Exactly.

Let's talk about accessibility with these tools, because I know that that's something that you're passionate about. And when you're talking in the live roundtable, too, it's not something that I had necessarily thought about yet. So I love when people bring these ideas that we're like, oh, yeah, great point. So tell us a little bit about accessibility and why you're so excited about AI with that. Yeah.

So I come from a background in therapy. I'm a licensed marriage and family therapist. And I, you know, quote unquote, grew up in my corporate career within a health care organization. And so I

A lot of the accessibility I talk about, just to be very clear and transparent to everyone, I talk about mental health accessibility. I am not an expert on physical disability, so I just want to preface this conversation with that, although I can speak to it a little bit. So I am severely ADHD, and I work a lot with other neurodivergent individuals, so that most commonly is dyslexia or sykinesia or ADHD, autism, etc. And

I'll speak very personally here, so I'm not speaking for others, but as someone who has severe ADHD and a touch of dyslexia, having AI be able to listen to my verbal dump in an audio format

and put it into something that is readable and accessible for me to then go put into action is insurmountably helpful. The amount of time AI saves me personally in planning

With my own ideas. It's literally just me speaking into chat. I use the chat up the actual audio chat option in chat GPT a lot. Also, her voice is very soothing. But, you know, so I use that chat a lot because in truth, my particular brand of spicy brain, I have to verbal process a lot. And so I just need a sounding board.

But I also have a lot of social anxiety. Like I'm talking to hopefully a bunch of people right now, but Kinsey, I'm having an intimate conversation with you. So my social anxiety is not through the roof, but it's hard to go find somebody to bounce ideas off of.

My best friend has never owned a business. She has no idea. I can't call my poor Voxer friend every five seconds. Kinsey, you're busy. So having chat GPT or something of that nature to say, I just need to talk this out and just brain dump and then have it produce notes for me.

and not have to try to go back and read through a stream of consciousness. That's a huge accessibility for people, for people who have physical disabilities where it's difficult for them to type.

It's able to translate different things in audio. So someone who is blind or otherwise sight impaired, it's they can still have these chat conversations and get notes and things read back to them.

Even our transcription process for the hearing impaired is largely powered by AI. And the more diverse the data set for our transcription processes, the more accurate those will become and the more likely our hearing impaired population will be able to not be impacted in their daily life. The definition or one of the many definitions of accessibility is or is

Something is accessible when the person who requires the accommodation can have the same or as close to the same experience as a person that does not require accommodation. So I don't know if you've ever seen the woman who signs at Eminem's concerts.

We've probably all seen the TikToks, right? And she's freaking grooving out, right? Yeah. Yeah. Like she's there. She's in it. She's signing like, nope, because she is making the experience as close as possible to the experience of someone who is not impaired.

AI is doing a lot of that work for, in my case, particularly mental health disability. And that's that's that's out of my soapbox is the piece that we individuals who are otherwise able bodied have the privilege to choose not to use AI. But a lot of those with disabilities, both physical and mental, they don't necessarily have the privilege.

to not use AI unless they want to have an experience that is completely different from that of a neurotypical human being. Yes. Oh man, I truly love this conversation. And there's so many things that I relate to too. I, my audience has heard me before just like gushing about brain dumping into these fricking platforms. Like I definitely, I have shiny idea syndrome all the time and like all these, yeah.

all these thoughts running through my brain. I'm like, get them out of my head and into ChachiBT. And it's been so, so helpful to use it in that way. And yeah, I love what you're talking about using like the different features and the privilege that comes with like,

being able to choose to use these tools or not and whatnot. Let's talk a little bit about data bias as well. I know that this is something that I feel really passionate about because I know that experience of like going to these tools and having them kind of like telling me all these things and just like...

X what they're telling me immediately, right? And then like you take a step back or someone else points something out to you and you're like, oh yeah, I guess this is happening. And this happens with life, right? Not just AI tools, but like, I know that these tools do have inherent bias because of just the way our world works and how we have collected data as a society that we then use to train these tools and whatnot. So can you talk to us a little bit about that? And then also like,

how to start spotting it as we're using these tools on the daily. So I want to validate for everyone for just a moment that the bias that we're talking about

It already exists, just like Kinsey said already. I mean, I am a social media hog. My partner hates social media. So I will often go to them and say, did you hear? And they're like, well, yeah, but it's actually this. Fine. So you read the article and I read the title. Like, so the bias already exists. We already have this existence.

built in bias based on the echo chambers that we participate, everything. So I want to validate that this is not a new problem. We just need new tools to spot it.

So we were talking about the apple analogy earlier that a child learns what an apple looks like and they learn it even better when you give them multiple types of apples and different versions of the apple. But if you only ever gave them a whole red apple and only ever showed them pictures of whole red apples and then you gave them a slice of green apple, it's going to be much harder for them to understand that that's an apple.

They might be scared of it. They may not want to touch it or they might just flat out not know what it is. Training data works the same way. So typically data bias creeps up when we haven't provided enough types of thing for the algorithm to label. This is a thing. And so typically,

Some really great examples are that when self-driving cars were first being, and I shared this in the roundtable, when self-driving cars were first being trained to recognize pedestrians, most of, in the U.S. in particular, most of the accepted training data for facial recognition was white males.

So highly melanin females, so very dark-skinned females, were not present in the data set. These self-driving cars were 47% more likely to not recognize a dark-skinned female as a person and not recognize them as an obstacle.

And so that kind of highlights some of the data bias. At one point, Amazon's hiring algorithm was identified as favoring words like executed and confident, which are present more often in male resumes than female resumes. So the algorithm was preferring male applicants.

applicants simply because those words existed. Again, because males are more prevalent in the workforce, males are more prevalent in terms of the training data that it was trained on, and thus that's what it knew to recognize as a potential successful candidate. And my favorite is that Google Ads had to change their algorithm because they were showing job searches

with higher salaries to men than to women. Because in the workforce, the training data that it has is that men have higher paying jobs than women. So it continued to show and advertise. It perpetuates bias that is already present in the population. That's the main thing you have to remember is that an algorithm will always perpetuate the bias that is already present in the population.

Kelsey, can I pause here real quick? Because I want to point out something too. And this has been something I keep mulling over, right? Okay, so I was sharing with a male friend of mine that you were going to come talk about data bias with us. And that, you know, and I was trying to kind of probably poorly explain the Apple, the Apple's drive, or sorry, the self driving car data. And, and he, his reaction was kind of like,

Like, I don't think that sounds like a thing and stuff like that. You know, like, I feel like when we talk about a lot of this stuff, firstly, I mean, it's just how like the patriarchal society that we're that we're living in. But also, like, it's not our fault, necessarily. Like, I think that a lot of times when we talk about this stuff, people tend to be in disbelief because they they think that there's some sort of intention there.

Right. And like the thing with data bias, right, is that there's not there's not like purposeful intention necessarily by like individuals. It's like a cultural thing and a self-perpetuating thing, like you're saying, where it's not something that like there's an although. Yeah.

Current political climate aside, you know, like, but like in general, you know, there's not always malicious intent. It's just kind of how the data presents itself in the society that we live in. Can you speak to that a little bit? Because I think that like, I just want to deflate anyone who might be feeling, who might be having the same reaction as my friend, right? Where they're like, it doesn't sound to me like this is right. Right. Fully understood. So yeah,

I'm going to use again, for those of you who are not parents or guardians in the audience, I apologize in advance for all the kids, kid references, but it's the easiest way to think about it because these computers really are just like really fricking smart kids. So my, so when you're, when you're, when you take a child to a toy store and you take them into the, into the doll section, are they going to pick a doll that looks like them?

Or they can pick a doll that doesn't look like them. Children are inherently selfish. They're inherently pattern conscious. So they're likely going to pick a doll that looks like them. There is children are not born racist. We know this for a fact. They are not born racist. They are not born against populations. They are simply very good at pattern recognition and inherently selfish.

By the way, I really love my daughter, so please don't take this negatively. But there's nothing malicious about that. Them picking a doll that looks exactly like them, regardless of their own skin color, because children of all races, they're going to hopefully, if it's available, hint, hint, nudge, nudge to the biases in our society, right? If it's available, they're going to more than likely pick a doll that looks like them.

And that's the same thing that the algorithm is doing, except it's not picking for its own selfishness. It's picking what it thinks will please you. And since, especially in the US, the majority is white men using these algorithms, it's going to pick the answer and the response that it believes is going to please a middle-aged white man.

So there is nothing malicious in the algorithm. There's no nothing malicious in the way that you prompted the algorithm. It's simply giving you what it thinks is going to please you. Now, you had mentioned what are some ways that we can get around this? Yes. Ask it. Say, hey, because that's the beauty of the chat option, right? Hey, assume that you've got built in biases towards white males.

Think of this from a totally different perspective. What would you change? Or be devil's advocate here and think about this from a totally new angle. Or think about this as though you were a 80-year-old black African-American woman.

Because anytime you prompt it to think differently, it can. It now has the information of what will please you. And thus, it's going to provide you with the answer that will please you for the new context you've given it.

So spotting at first is just, does this feel right? And if it feels right, does it feel right because it pleases me or does it feel right even taking into my values and my own biases? That's the first thing, because you got to do some self-reflection. I'm sorry, everybody. But if you don't recognize your own biases, you're never going to be able to recognize an algorithm's biases.

So do some inner reflection, recognize your own bias, analyze first based on your own bias, what it spits out, then start asking it to explore alternate viewpoints.

It's very good at spitting out information from alternate viewpoints. You just have to ask. Oh, my gosh. So many things. I just like geek out about this conversation. Right. Because I mean, OK, firstly, there's like critical thinking skills. You know, I'm one of those people. I really love to help people lean into who they truly are in their practice.

brands, find their core messaging that excites them, find their core values that they not only feel themselves that are a part of their journey, but also that they want to share with their audience and encourage their audience to align with. Right. And like starting with you is truly, truly helpful. And also remembering that like you guys, you don't know how many times I go to Chachi PT. I'm like, actually.

this is what I think chat GPT and then it refines the response to be more aligned with me right just remembering that you can bring your own ideas and opinions to these tools first and then also tell it no chat GPT actually you're wrong or actually this isn't how I feel I always use my own social media marketing perspective as like an example is like if I would just go to chat GPT and be like write me a blog post about social media marketing I

I mean, from personal experience, I know that it would probably spit out bro marketing tactics or like use maximize like all these power words, you know, which I that's not my brand, guys. I adopt what I call an Instagram eye roll strategy with chat or with social media platforms where I'm like, well,

let's take a more like lazy approach to it. Let's like roll our eyes at this platform and stuff and like make it feel good for us. Hence my other brand, Feel Good Social. So like I always have to tell it that's what I think before I have it do anything for me or else it would just go to the more generic response, right? And the same thing goes with like your perspective and like this perspective it takes on a world and all the different stuff.

But then also, like, okay, you just kind of, like, put a lightbulb moment on in my head. I'm sure you've already, like, thought this, of course. But, like...

I'm actually really excited. If we can start getting people to use these tools in this way, these AI tools are actually could be really awesome for actually helping us as a society overcome cultural differences and like the biases that we hold that we don't even realize we hold. Right. Like that's,

These tools are really awesome for actually being able to tap into alternative perspectives that you just have no idea existed. But like, if you ask the tools to share with you, then they will. Well, and that's the beauty of the chat based algorithms is that you get to ask those questions. So like, I fully recognize my own ADHD. I'm just going to go like,

full speed ahead. And so sometimes I'll ask it, Hey, what am I missing? That's making what I'm building and accessible. So I'm, I'm building an app. It's going to be like lead gen stuff like that. And every now and then I'll go to chat GPT while in the, in the thread that I'm planning it out and be like, Hey, what's not accessible about this? Or what makes this confusing for the read for the user? Or who am I forgetting about? Yeah.

And it will absolutely, even if it's not perfect, it sparks the thoughts of who do I need to be considering? What populations am I leaving out? And I'll be honest, I actually have like mission statement and some value statement that I feed that are like in my saved instructions and chat TPP, because I will forget to put those things in. And I have my own internal biases that I don't always remember to address.

So I've actually saved that to some of my instructions in chat GPT. So it tends to address some of that stuff more upfront. And then when I'm able to kind of get my brain on board and remember to prompt it, then I'll prompt it for deeper work. Totally.

Oh, I love this, Kelsey. I have so appreciated you coming on and sharing all of your awesome knowledge with us. Can you please share with everyone how they can connect with you? Any free resources you have? Because I know they'll want to snag those. Absolutely. So any anyone that wants to nerd out with me about anything.

AI, about data analytics, because that's my main job, right, is data analytics for entrepreneurs and learning how we can think about the numbers that are in front of us differently. That's why I geek out with Kenzie so much. You can find me on, the best way to chat with me is Instagram. And so I'm at Kelsey E. Silver, don't forget the E, on Instagram threads, all the nonsense. And I'm

If you want to learn the 15 metrics that 15 amazing six and seven figure businesses track in their business every single month and day, you can check that out at kelseysilver.com slash metrics.

And I'll give Kinsey that link. But that's a really fun experiment I did where I asked 15, six and seven figure business owners, if you were stuck on a desert island and could only measure one thing in your business, what would it be? And they told me. And so that's a super fun little download that you can get to really understand if you can't measure much, what can you measure in your business to have a huge impact on your ROI and your revenue?

I love that. Thank you so much for coming on and having this conversation. Thanks for having me. This has been a blast. Thanks, Kinsey. How great was Kelsey, you guys? I absolutely adored this woman. Not only did we get a chance to talk in today's episode, but she was also one of our awesome guests at our AI expert political climate panel that I hosted in February. So if you haven't caught that yet,

make sure you go give it a listen because seriously, it was one of the most important conversations that I've had yet this year. All right, I hope you enjoyed this conversation as much as I did. If you did, don't forget to share it on your Instagram stories and give me a tag at Authentic AI for Entrepreneurs because I will totally reshare it to my audience and also just give you a lot of gushing love in the DMs. All right, I'll catch you next time I catch you.

Thank you so much for tuning in to Authentic AI for Entrepreneurs, my friend. If you enjoyed this episode, don't forget to subscribe on Apple Podcasts, Spotify, or wherever you listen to your shows.