I'm looking for a boyfriend, and I've been asking around.
I've been soliciting some advice.
But how do I make sure that that advice is trustworthy, especially when it comes to something this monumental?
So I've been thinking and there are a few things that I would like out of this advice in order to trust any advisor in making this decision.
So first, I want to make sure that they're unbiased, right?
I want to make sure that they're open to everything.
I certainly am not ruling anything out.
I want this to be a fair process.
I also know that in order to get the optimized outcome, I'm probably going to have to share a good amount.
So it's going to be a give and take - - a transparent process.
But if I do share that data, I want to make sure that it's not being used for additional purposes, that they're not telling other people that they shouldn't.
I want to make sure that my preferences are private.
Next, I want to make sure that they're robust.
So my dating history maybe not that indicative of my current preferences.
I want this advice and this advisor to be able to adapt with me to take in these new parameters as we learn and grow together.
The last piece is when we finally do get to a point where some lucky fellow is selected,
I want to understand what was their thought process behind it.
Why do they think that will be a good fit?
These are the different pillars of trust that would be important for me for making this decision.
But they are also crucial to any business having any AI advisors help them make decisions.
And this is how we define trustworthy AI.
But now that we have these models or these different traits, we don't stop there.
Right? We actually have to create the model.
We actually have to create the selection process.
So we're going to switch to something that's a little bit more contextualized in business terms.
And this is hiring practices right now.
So many fortune five, fortune ten companies at this point do use HR
AI models to help them with their hiring practice for selecting top talent.
But this has proved to be tricky, to say the least.
And there's legislation that's coming out at a federal level in New York.
It's rolling out at the first of the year that says that you have to prove that your model is trustworthy by January one and it has to have been in production for over a year.
Prove this.
How do we do that?
We start with.
The model proposal.
So at this point, we need to make sure that we are selecting the right business outcomes.
We want to know who we're selecting for, why we're selecting for, what different data sources.
We also want to make sure that this is a team sport, HR departments involved, compliance, maybe privacy, if you're worried about that.
But this is the part where you set up the governance structure inside your model organization so that there is accountability throughout.
This is a crucial step.
The next is we want to make sure
That we're doing
the model development correctly.
So this is where we're pulling in our developers, our engineers, they're creating the model as we have defined it previously.
After that, we're going to do a pre implementation review.
And during this piece, we're going to make sure that our model here doesn't include anything around bias.
Right.
We want to make sure that we are selective of all groups.
Getting back to this fairness piece.
We also want to make sure that it's a transparent process.
We're not sharing any data.
It's private, and it's going to be adaptable and explainable.
So once we feel like we get this piece done, that's when we're going to come in here and we're going to involve the model approval.
And the model approval piece is going to bring in an overall governing structure again.
So remember, right, we're setting it here, but then we're actually having people come in.
We've created our model.
We've proven that it's got all of these different components of trustworthiness and then we're saying, we're good to go, we're going to put this in production.
Typically, we're seeing that organizations at this point are involving, you know, a board and ethics board.
This is something that's being outsourced into more and more divisions within your organization at higher levels, because this is not a technical discussion, this is the technical piece.
But in terms of implementing the model and creating trustworthiness, that's a line of business opportunity here.
So once this gets the approval, then we can go and we put it into production.
But at certain intervals we're doing the compliance and validation testing.
So we're not done there at certain intervals that we've set here.
Right.
We're going back and we're saying, okay, is this fair?
Is this is there any bias?
Has there been any drift?
Is this still achieving what we needed to?
So in this way, you can see that this is going to be an iterative process where we're going back and forth to ensure that your model will always be trustworthy.
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