In today’s episode, I delve into the intricacies of integrating diversity, equity, and inclusion (DEI) initiatives into Artificial Intelligence (AI) models. It’s a complex and intriguing discussion, and I shed light on some key questions: Should we train our models from scratch or leave them ‘raw’? What does it mean to incorporate DEI principles from the start of model training? Using practical examples from the advertising realm, I illustrate the effects of unchecked biases in AI and how these can impact the end user. It’s all about making conscious choices when it comes to our training datasets and being proactive in eliminating potential biases. But more than just creating an equitable digital environment, I also delve into the practical side of DEI in AI – mitigating risk and avoiding legal pitfalls. So, if you’re curious about how to harmonize DEI and AI, or you simply want to understand more about ethical AI practices, this is one discussion you won’t want to miss.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, let’s talk about the inclusion of diversity, equity and inclusion initiatives.
I was having a conversation recently with a software developer talking about the various different models and one of the questions came up was, should we even be doing any kind of Di? Or should the model be trained? Essentially, as is? And let the deployment of the model handle any of those inputs and outputs? The answer is complicated.
The answer is complicated.
Because there is validity to the position of creating a model that has no has no editing to it is the raw model, the raw ingredients process, which can include a lot of crap, depending on how you train it.
Or doing some some weighting and some training and some parameter optimization to incorporate things like diversity, equity and inclusion into the model from the very beginning.
Here’s the differentiating point.
If you are applying strong dei principles to the data that is being used to train a model, then you don’t have to work as hard to try and balance the models output itself.
For example, let’s say you’re making a an advertising database of a piece of ad tech, and you take in a huge quantity of information from say, Instagram, that’s gonna have a whole bunch of biases in it, right? If you just connect the pipes and let the data flow, you’re going to have a huge number of biases that data and so you’re going to have to spend a lot of time in that model, trying to balance things out to make sure that audiences are shown the right ads that are appropriate, that are balanced, that are fair, that are equitable.
And it’s gonna be a lot of work to do that, to tune that model to have those dei principles built into them.
Or you take the training dataset that you start with.
And you say, Okay, well, let’s go through this and clean out all the crap.
So that it is a curated dataset is is highly curated, is highly tuned, we know the data set that the model will build from is fair, is equitable, is diverse is inclusive.
If you do that, then you have to do a lot less work in the model afterwards.
Because you know, what went in, was clean.
It’s, it’s like every form of software development garbage in, garbage out, right? If you put a lot of pollution into the model, then the model is going to spit out a lot of undesirable stuff.
That’s one of the reasons why you see all these different, like large language models like Bard and Bing and stuff, saying, hey, this thing may generate inappropriate content.
Well, yeah, cuz you’ve scraped a whole bunch of inappropriate content to begin with.
And so you now have to provide warning statements on this thing, because you didn’t clean it in advance.
You didn’t do any work on the front end.
So the back end is going to be misbehave.
Regardless of whether you’re doing it in the model, or in the training data set, I would strongly urge you to lean towards the training data set side, you do have to have dei principles in place.
At the beginning of the project, before you do anything, you have to say, here’s what we consider diverse.
Here’s what we consider equitable.
Here’s what we consider inclusive and whatever the situation is, for example, in advertising, if you’re building an ad tech product, should assuming all of the things being equal, should say a black man and a Hispanic woman, same income level, same audience demographic generally, should they receive the same ad? Should they receive an ad that is that has the same maybe discounts in it.
If you have a dei mindset, the answer would be yes.
And if they’re, if they have equal incomes and equal propensity to buy you, they should absolutely see the same ad.
But if you’re using a large language model, for example, OpenAI eyes, which clearly states on their website in their disclosures, that there’s negative sentiment in the model attached to African American women’s names.
If you have Linda and Leticia and you’re using that model.
And you you don’t know that there’s this problem in it.
Leticia is going to get a worse offer.
Even though those two buyers identical there Leticia is going to get the worst offer because of the language model itself.
So the warning the importance here is to have your dei principles installed in your company in your values in your projects from the start, the person or people who are on your dei committee.
They should have a seat at the table for any AI project whatsoever.
And they should be the ones that, among others, including the developers, including the engineers, including the project managers, they should also have a stop button to say, hey, we need to take a pause right now and reevaluate because the model is doing something that is not appropriate.
Right? The model is doing something and we need to hit the pause button, the stop button, stop the assembly line.
Let’s figure this out.
And then you apply these dei principles to every aspect of AI construction, the training data, the algorithm choice, right? What are what protected classes are in place and how they are balanced? And what constitutes an equitable outcome? Is it equality of opportunity? Is it equality of result, it depends based on the situation, your values, maybe the values of your culture, but you’ve got to have it written down and planned in advance, if you don’t, bad things are going to happen.
And by bad things, I mean, things that will get you sued, right.
dei isn’t only about making sure everyone gets a fair shake.
That’s important and should be fairly obvious.
But it’s also about liability protection, it’s all about risk mitigation.
It’s about not getting your butt sued.
So there’s sort of this carrot and stick with the AI and the carrot is you you make a more equitable, fair, just world with the software that you’re creating, or you’re having AI create, and the stick is don’t get sued.
So build dei into every API project from the start.
And if you have to choose where to spend time, invest time in the training data that goes into the model.
Now if you don’t have a choice, if you’re starting with a base model, maybe from like an open AI or from a llama or mosaic ml, then you’re gonna have to do a lot more fine tuning on that model.
To ensure equitable outcomes, there’s gonna be a lot of work on the back end, because you didn’t have control of the base model, it’d be like getting a pizza that has a whole bunch of toppings you didn’t ask for you got it, it’s going to take your time to pull off all the toppings right and then put new ones on and maybe add some more cheese to kind of cover up the the messy meat of it.
But if you’ve got people who can’t have shellfish, and someone put shellfish in that pizza, like okay, you’re gonna be spending a lot of time picking the little shrimp.
But the same principle applies when it comes to dei and AI.
If you’ve got a pre baked model, you’re gonna spend a lot of time pulling stuff out of there.
That’s the show for today.
Thanks for tuning in.
I’ll talk to you soon.
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