In the midst of controversy and emerging regulations surrounding AI for talent management, Beamery emphasizes explainability

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One of the many use cases for artificial intelligence (AI) is for talent management.

However, using AI for talent management and human resources (HR) purposes is not without challenges as regulators increasingly seek to control the technology. New York City, for example, is currently working on the Automated Employment Decision Tool (AEDT) law to help visibility and governance in the use of AI.

One of the vendors in the space is London-based Beamery, which continues to build out its AI-powered talent management platform in an approach the company’s leadership hopes will comply with existing and future regulations. Beamery includes General Motors, Uber, BBC (British Broadcasting Corporation), and Johnson & Johnson among its users.

Beamery was founded in 2014 and initially focused on talent acquisition, helping organizations find the right staff. The company’s capabilities and AI technologies have improved over the years, and Beamery’s platform now includes a host of other talent lifecycle capabilities, including skills development and mobility.

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“We really expanded the product suite, we really doubled up on the data layer around understanding people, their skills and abilities,” Abakar Saidov, CEO of Beamery, told VentureBeat.

To support the company’s technology and go-to-market efforts, Beamery announced today that it has raised $50 million in a Series D funding round. The new round was led by Growth of teacher enterprises (TVG).

The evolution of AI for talent management

The first generation of AI technologies for talent management was largely about matching job descriptions with resumes.

Sultan Saidov, co-founder and president of Beamery (and brother of CEO Abakar Saidov; hereafter referred to as “S. Saidov”) explained that matching basic talent patterns is a less than optimal approach to finding the ideal candidate. What Beamery has developed is a much more nuanced approach that uses graphical data models and AI to create contextual understanding. For example, he noted that it’s important to understand what a company does and what job titles mean within a specific company.

By having a contextual understanding, S. Saidov said it is also possible to better identify potential candidates who might otherwise not be found.

“We identify the types of people who are easily trained or who are trainable, even if those skills don’t exist today,” he said.

Having the contextual graph of how skills and requirements relate to each other also makes it possible to recommend career paths. S. Saidov said Beamery can now recommend new hires which courses can help them navigate their desired career goals.

The impact of HR regulations and need for explainability on AI

At the heart of the various HR regulations, in New York and elsewhere, is the need to ensure that the AI-driven systems operate in a fair and equitable manner.

A primary way vendors like Beamery aim to comply with regulations is by providing explainable AI approaches. Beamery has published a explainability statement to help users and regulators understand how the Beamery platform works from a visibility perspective.

S. Saidov explained that the regulation usually boils down to requiring organizations to prove that the AI ​​models are auditable. The audits should be able to identify whether there is overt bias in the hiring or decision-making process.

According to S. Saidov, many of the HR laws surrounding AI right now, including those in New York, are still in a somewhat ambiguous state, in part because the laws are often too vague even in the definition of what AI actually does.

In the case of Beamery, S. Saidov stressed that his company’s platform does not do automated decision-making.

“We never say ‘hire this person,'” said S. Saidov. “Everything we do is about providing explanations. For example, here are career paths you might have or, even if you’re a recruiter, parameters you might consider to help you evaluate people.

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