AI Analytics Reveals Facebook’s Preferred Employee Traits
The advancement of AI (Artificial Intelligence) has started exposing the true behavior of companies that is impossible to know before. A startup company with strong drive to AI development, Diffbot has made public their AI report titled Knowledge Graph. It is an AI-generated reference that pits information from various public sources and compiles them to a usable and readable form for public consumption.
“What we’ve built is the first Knowledge Graph that organizations can use to access the full breadth of information contained on the Web. Unlocking that data and giving organizations instant access to those deep connections completely changes knowledge-based work as we know it,” explained Mike Tung, CEO and Founder of Diffbot.
One of the most glaring revelation contained in Knowledge Graph was the product of the data crunching process used by Diffbot, using the publicly available data coming from 22,000 employees of Facebook. The result of the AI computation shows that Facebook has the habit of hiring people who used to work for Microsoft and Google. The business intelligence data reveals that applicants for a Facebook job have lesser chances of getting employed by the social media giant if they came from Twitter and Linkedin. The company also favors hiring more men (66.6%) than women (33.3%).
Facebook’s recruitment team according to Knowledge Graph also heavily favors Stanford degree holders, while preference for a Harvard graduate was only in 10th place. There is a big gap between the number of men vs women in Facebook’s talent pool primarily due to the aspects of having an Engineering and Development job (the majority of Facebook employees) which is typically considered as a male-dominated job.
Other than just pure curiosity, aggregated information produced by AI-based is a powerful tool to enable talent targeting and planning. As many professionals seeking and actively working today have a LinkedIn account for the professional profiling, Artificial Intelligence can be used to aggregate public data from LinkedIn (and other similar sites as well) in order to build a strong list of candidates for a particular job vacancy.
This will help lessen the burden of recruitment specialist in compiling qualified candidates for the job they are advertising for online. Compared to traditional classic recruitment, computer programs can be used to filter who are the qualified candidates from those that aren’t without the recruitment specialist spending a lot of time doing it manually. For huge companies that are constantly losing employees due to piracy from rival firms, the use of an automated system for looking for new hires will lessen the cost of looking for a replacement employee.
AI-based recruitment system generates real-time analytics with data such as: what concerns each generation (millennials vs boomers), areas of the country (focus of union issues), positions and hierarchies (leaders vs. collaborators), among many other factors of interest. The objective of any company recruiting for new employees is to attract high-quality candidates, near to the performance offered by head hunting companies. This is the future of personnel management which no company can ignore.
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Kevin Jones, Ph.D., is a research associate and a Cyber Security Author with experience in Penetration Testing, Vulnerability Assessments, Monitoring solutions, Surveillance and Offensive technologies etc. Currently, he is a freelance writer on latest security news and other happenings. He has authored numerous articles and exploits which can be found on popular sites like hackercombat.com and others.