ADP

“We're investors in a company called Zen Payroll, ah, and we're investors there because they demonstrated in, on a very small amount Really, of their own money, that they could gut ADP. That customers deserted ADP in droves to have essentially single click, ove…”

“Education's an interesting factor, and it's in our system, but skills turn out to be a much more interesting factor as well as, not job titles, but the nature of the job.”

“Some of the numbers are kind of astounding, so we pay in any given month about forty million people around the world.”

“In France, we're also, we pay something like 20 or 30% of the French population.”

“We actually have something in our system like 30,000 downstream systems. You know, Fidelity, Blue Cross Blue Shield. They all get reports.”

“We're in full production on top of some of those large, large language models.”

“If we're paying 18% of the country, we're recruiting about 15 to 18% of the country as well through those systems, okay?”

“So we've got about 950,000 clients in this database.”

“The piece we didn't bring from the data mesh was the notion of federated query. Because the problem with federated query is the latency of the query is the worst performing member of the federated group. Right? And so we still have a centralized data repositor…”

“The first thing we did after our bias statement is take a decision that we want to treat the employees of our clients as consumers. And so, each employee has the ability to opt out.”

“We went with Informatica, and the reason why we're on Informatica is very clear. Not, as Spencer was saying, not everything is in the cloud. And unfortunately for startups, they're making decisions about where to build, and so they're building in the cloud. In…”

“The turnover predictor, for example, uses 57 attributes.”

“Instead of having it all done by hand, it's, pfft, 95% automated.”

“We use our own aggregation rules, but we call it a five in 10. Right? We won't give out data about, in, in any aggregation unless there's 10 employees spread across five companies.”

“So last year we moved about 3.2 trillion dollars.”

“We pay in the US, you know, something like 18 or 20% of people, and then we do the taxes on another You know, similar size.”

“We do use an awful lot of MLflow for our MLOps. We do SageMaker work. We're working with this really interesting company called Robust Intelligence in California that's helping us with bias monitoring, compliance, supply chain attacks on machine learning”

“We use Databricks as our, as that data layer, and we use Snowflake to do very specialized analytical database workloads that it's very, very good at.”

“So in any given month, right, those forty million people, there's about twenty one million job titles. We run some pretty sophisticated, ah, machine learning to boil that down to about 6000, ah, or now 7000 key job categories.”

“We have external people on that as well. We have industry experts. We invite external representation. People that aren't associated with the company, except for the ethics board participate. They're under NDA. They see exactly what we're doing in terms of prod…”

“So the EEOC has a thing called the, ah, Test of Means, and we actually use that internally to gauge our machine learning systems, and then we'll test and monitor for that.”

“So, I can launch a product today and have 5000 clients on it in a year.”

“At least in my team, they don't get the meeting if we didn't actually already know what they were doing.”

“And what we said was, is that we don't believe that bias doesn't exist. In fact, we believe it does exist. And so our role isn't to mitigate all bias, but our role is to at least measure it, understand it, and express it, and make sure that people understand i…”