Scraping The Dark Side Of Personality Online
Social Media Screening is the fastest growing element within background screening industry. Machine-learning, AI and computer-generated algorithms can examine people's social media updates for negative personality traits, which are then used in hiring decisions.
So what does this all mean for HR practitioners? First, if they are interested in avoiding employee or leader derailment paying attention to the dark side of personality is critical, particularly during the hiring process.
Second, in the absence of psychometric data, machine-learning algorithms can provide a relatively accurate proxy for candidates’ dark sides. And in comparison to psychometric tests such algorithms are free to implement – assuming organisations have the right data capabilities – and require no extra time or effort from candidates.
Third, while it is conceivable that (with the vast amounts of data already in existence for the majority of the workforce in the Western world) the future of assessment will consist of scraping passive data, or the data that individuals have already left behind, even in public environments there are important ethical constraints to using such data in the hiring process.
Since most Facebook and other social media users are unaware of the possibility that their data can be used for hiring decisions, even when laws and regulations don’t protect consumers, it would be hard to come up with an ethical justification for mining that data for employment-related decisions.
One way around this may be to enable individuals to ‘opt in’ to having their data scraped, much like they agree to a traditional psychometric assessment in the context of a job application. Furthermore, it would certainly mitigate ethical concerns to actually provide people with some feedback on what their footprint says about them – automated, private, individual feedback that can help them present themselves in a more positive way, or even reflect on key aspects of their personality.
Measures could be put in place to create a transparent transaction whereby individuals agree to having their data inspected in exchange for some helpful feedback and the chance of a suitable job – which would turn AI recruitment tools into useful talent agents.