Founded by a group of investment bankers who believe in the combined power of data and AI, Suited addresses the main challenges faced by recruiters: limited time, limited budget, and limited information. It does so through an AI-powered, assessment-driven ecosystem that empowers hiring teams to expand the way they consider a person’s candidacy, and also provides candidates with a way to better demonstrate their raw potential to employers.
Suited’s AI algorithms are catered to each employer it works with. They harvest past performance data and isolate key employee characteristics. But, knowing that this data in itself may contain biases, Suited’s data scientists generate synthetic data to balance out both underrepresented and overrepresented information, in an attempt to achieve a more harmonious dataset. Suited also analyzes its own assessments and algorithms for potential biases: adjusting, reweighting, modeling, and testing their impact iteratively.
“We are constantly refining our process for testing and correcting for bias,” Myers says. “We always have at least one project in motion that is dedicated to improving our process for removing bias from a model.”
As a pioneer in the realm of AI-powered recruitment, Suited has learned several lessons along the way. Keeping the AI platform separate from the application platform has been particularly valuable, allowing the AI team and core engineering team to operate independently with increased speed and security.
Data integrity, model integrity, reproducibility, and event tracking are all more complex in AI deployments than in traditional data approaches, and balancing those resultant issues with the AI team’s time and resources has been a critical task.
Currently, Suited caters only to the legal and investment banking industries, but it has plans in the short-term to extend into other areas of financial services, such as commercial banking, wealth management, equity research, sales and trading, accounting, and asset management. In the medium-term, they see Suited functioning across an even broader spectrum of the professional services landscape.
“The reason these industries are so ripe for AI-enablement in their recruiting processes is due to the nature by which they recruit: often in annual or bi-annual cycles with vast amounts of entry-level positions becoming available at once,” Myers says. “In these tight recruiting timetables, a high volume of decisions must be made quickly, which is where AI does its best work.”
Suited is committed to the ideals of equity and diversity, but it’s not a social justice organization—it’s a corporate hiring solution. This means that it’s not simply replacing one bias with another, but instead trying to create more effective matches between employer and employee. Clients will come to judge Suited’s success over the medium- and long-term horizons, where diversity and equality will ideally be byproducts of a more efficient hiring process.
“The ultimate determinant of success is how candidates perform over the long term, which is still too early to tell,” Myers says. “However, there are a number of interim metrics on which we can be evaluated, including process efficiency, candidate quality, demographic representation, performance of candidates in the interview processes, and early on-the-job performance measures. In law, for example, using Suited as a selection tool has resulted in 48 percent more Black candidates being recommended for the job, as compared to normal resume-based factors, which we consider a huge success.”
One of the biggest challenges of AI-powered firms is educating the market. A 2018 survey by Brookings found only 41 percent of Americans felt either positive or very positive about AI in general, and 39 percent said that AI was at least somewhat worrying. Overall, 38 percent of respondents believed AI would reduce the number of jobs available.
But in Suited’s vision of the future, AI isn’t taking away jobs, it’s handing them out, and it’s doing so in a more effective and equitable way. That should win over a few more fans.
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Unlike fungible items, which are interchangeable and can be exchanged like-for-like, non-fungible tokens (NFTs) are verifiably unique. Broadly speaking, NFTs take what amounts to a cryptographic signature, ascribe it to a particular digital asset, and then log it on a blockchain’s distributed ledger.
First proposed by computer scientist Nick Szabo in the 1990s and later pioneered by the Ethereum blockchain in 2010, smart contracts are programs that execute themselves when certain predetermined conditions are met.
This is a role for tech-lovers, for logical thinkers, for those who like being given an answer and then are told to find the question. But it’s also a role for communicators, for relationship builders, for people who enjoy cross-departmental collaboration.
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