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Can an algorithm select the perfect team?

Published on 11/17/2016 | Technology

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Adi Gaskell

Adi is a social business blogger and community manager that writes for sites such as Social Business News and Social Media Today. Away from the computer he enjoys cycling, particularly in the Alpes.



The growth in big data has seen a number of hefty claims, not least in the HR community, where advocates have long suggested that there is a smarter, more data driven way to recruit and manage our teams.

The hypothesis

It’s a hypothesis that researchers at Microsoft are putting to the test.  They’ve written a recent paper documenting their attempts to recruit the most effective teams via an algorithm rather than more traditional recruitment methods.

The study saw the researchers try and find an algorithmic solution to selecting the best candidates from a pool of applicants.  The study utilized both synthetic data but also real life data pulled from oDesk.


“The recruiter needs to learn workers’ skills and expertise by performing online tests and interviews, and would like to minimize the amount of budget or time spent in this process before committing to hiring the team,” the authors say.

Choosing the right person

The aim is to devise a system whereby the right people for any given project can be selected automatically, thus ensuring a more effective and productive team.

The algorithm uses probably approximately correct bounds that analyze various characteristics of each candidate, such as their skill, reputation, social network and so on.  It then attempts to construct the perfect team whereby all of the ‘pieces’ fit together seamlessly.

The authors also believe that the algorithm, and others like it, could be used to optimize the team building process.  They believe it can be effective at connecting recruiters with candidates around the world without needed advanced contact.

Suffice to say, more work will be required to put such thinking through its paces in real world environments, and the authors accept the need to try the system out in more complex environments.

“We see promise in extending the results to incorporate more complex relations among team members, such as the matching of task types within teams to balance the workload, capturing diminishing returns of growing teams, learning and representing costs associated with communication and coordination among people with different skills and abilities […] and other combinatorial constraints,” they conclude.

It seems counter-intuitive that computers can succeed in such a complex task, but given the inherent flaws in human attempts to recruit the right talent, it seems only fair that they be given a chance to test their mettle.

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