Machine learning models can be unreliable after they are deployed in production. The goal of Arthur.ai is to help companies monitor their models to make sure they stay on track. Explainability and bias Mitigation have been added to the array of services.
A machine learning monitoring startup has raised $42 million in a Series B.
The company has been working on guarding against bias since we last spoke to them.
A lot has been done on the bias side of things. How do you keep the models from being discrimination? We have done a lot of novel intellectual property development around how to adjust the outputs of the models so that they meet the customers' goals.
Understanding why you got the results you did is called explainability. If you have high blood pressure, which could be from diet or other controllable factor, or it could be from a hereditary factor, you have no control over and may need to take medication. It is important to understand that there isn't a one-size-fits-all answer.
He said he noticed a change in raising this year. When there were people who were calling every five minutes asking, are you ready, we had to meet with a dozen different investors. Do you know if you're ready? Are you prepared? He said that it all worked out for them.
Maybe the company's growth is one of the reasons investors are interested. When you consider the economic ups and downs we have experienced over the last couple of years, you can see that the growth of the startup is even better.
The company has 55 employees today, up from 17 at the time of its Series A, and is working on diversity at both the cap table level and at the employee level.
In the research area, having a diverse workforce can help prevent bias from entering their software. He said that the team that published the papers was a lot better than the others.
Today's round was headed by Acrew Capital. Acrew and Coalition Operators are included in the table. The funding will allow him to join the board.
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