AI and the ‘Unconscious’ Hiring Bias

Is your hiring tool augmenting the ‘unconscious’ bias?

How often will a woman be passed over for a leadership position even though she would have been the best choice for the business. HR teams are now trying to fix this – many have launched leadership programs for women to address this gender gap. Sheryl Sandberg’s has attributed this to a deep-seated problem of women’s internal biases about themselves – women lack ‘leadership ambition’- and the societal barriers make it difficult for HR to translate their commitment of ‘inclusivity’ into practice. Be it their internal dilemma or external factors, women in the corporate landscape are still underrepresented at every level. The issue is not just at the tip of the iceberg, Global Gender Gap Report 2020 states that we will need another 100 years to close the gender gap in the workplace.

So, it’s not an ambition gap but a perspective gap.

AI to the rescue

The Global Gender Gap Report 2020 report reveals that the greatest challenge preventing the economic gender gap from closing is women’s under-representation in emerging roles – in cloud computing, just 12% of professionals are women, and in data analytics and AI, the numbers are 15% and 26% respectively. While the world is embracing technology at an unprecedented speed, women are shying away from it. Today, women represent 25% of the global workforce in IT and a mere 5% in Asia. Only 5% of the tech startups are owned by women across the globe.

The field of artificial intelligence (AI) is growing at a rapid pace, developing algorithms and automated machines that show promise in making the workplace less biased and more efficient. HR teams have introduced AI into many work processes, especially recruiting and talent-management functions. They are dependent on the dumb keyword-matching AI-enabled technologies which use algorithms to sort through numerous factors to profile people and make predictions about them. But what happens if the algorithm is actually relying on biased input to make predictions? AI is good but it’s also known to be susceptible to bias.

Silicon Valley natives would argue that when it comes to bias in decision-making, artificial intelligence is the great equalizer. If we delegate complex decision-making to AI, it becomes a math problem, and results will be computed without any bias or prejudices we humans may hold. The field of artificial intelligence (AI) is growing at a rapid pace, developing algorithms and automated machines that show promise in making the workplace less biased and more efficient. Technology will remove human bias from people decisions, but this will take time – even Amazon realized that their AI recruitment system was gender biased. The engines were unintendedly augmenting the biases in hiring. The engine learned from the historical data that males were preferable, and it began excluding women from its recommendations for software engineering job positions. This is a classic case study about the limitations of machine learning and embedded gender bias is threatening the integrity of technology.

To address the gender gap, we need unbox the potential within the female gender and generate awareness for implicit bias in the workplace. So, what needs to be fixed is the ‘unconscious' human bias which impacts people-related decisions at work specially when it comes to recruitment, promotion, talent development, performance management, and succession planning. HR would ‘unconsciously’ be reluctant to improve diversity, even though gender-diverse management teams have been proven to consistently perform better.

Towards a better future workplace dynamic with EasyTalent

AI is only as effective as the data it is trained on. AI hiring and talent development systems have the potential to move the needle on gender equality in workplaces by using more objective criteria in recruiting and promoting talent.

Hiring is a human decision. While we can leverage technology to evaluate and assess candidates’ capabilities, the selection process needs to be calibrated by the HR teams and not by machines. EasyTalent allows HR teams to administer multiple formats of psychometric test based the Big Five framework. The results are then juxtaposed with the historical data of the workforce (complied by the labour department of US) called the O*Net - one of the world’s most reliable data networks with an extensive analysis of over 900 jobs, developed and continually updated. Candidates personality traits are granularly mapped with job role to predict future performance.

EasyTalent also has multiple formats of video interviews assessments. HR teams can administer Structural Behavioural Interview (SBI) - an interviewing technique where the candidate is asked to describe a real-life situation they had in the previous job. The interviewer can access candidate’s responses based on real experiences, as past performance is a reliable predictor of future performance.

During the interview our AI-powered engines automatically analyze candidate's facial expressions (sentimental analysis) and measure the expected emotion like calm, surprised, confused, fear etc. over and above her/his performance. Interviewers can get real-time insights into candidate’s behavioural traits and thoughts. Since behaviour patterns are formed over a person’s lifetime - these are vital pieces of information that can predict future behaviour.One Simple Thing HR Can Do Right Now for Gender Parity.