HR Can Deconstruct Bias In The Workplace With Machine Learning

Conscious and unconscious biases influence every hiring decision made, and it’s up to Chief Human Resources Officers (CHRO) to take a strong leadership role to reduce its negative effects.
Women are 10% less successful than men at passing initial filtering for a new job and 35% less successful than men in interviews, according to two findings from’s internal analysis that quantifies the effect of biases in hiring today.
Centuries-old practices of hiring based on conscious and unconscious biases, intuition and resumes that only show a small part of a person’s skills deny companies the potential to achieve greater personalization at scale across every aspect of talent management.
Data is the great equalizer, with proven ability to eradicate conscious and unconscious biases from hiring decisions and enabling true diversity by equally evaluating candidates based on their experience, growth potential and strengths.
These and many other fascinating insights were shared during’s recent panel, Deconstructing Bias At Work. Panel participants included Monika Fahlbusch, Chief Employee Experience Officer at BMC Software and former Senior Vice President, Global Employee Success at Salesforce, Ciara Ennis, Senior Marketing Manager EMEA for Twilio, Russell Williams, former Vice President of Human Resources at PARC, A Xerox Company and Ashutosh Garg, founder. The Honorable Ananth Kumar Hegde, Union Minister of State, India, also addressed in detail his plans for re-skilling India and empowering youth in his nation. The panel provided insights into how CHROs are meeting the many challenges of talent management today and provides valuable lessons learned.

How Machine Learning Is Solving One Of HR’s Greatest Paradoxes

Winning the war for talent must be multidimensional with a strong focus on making every HR process more employee-centric. Monika Fahlbusch, Chief Employee Experience Officer at BMC Software, emphasized this point on the panel, articulating how Salesforce created a unified team with the specific goal of making the company one of the best to work for in the world. “We succeeded by having a diverse team across the company share the common vision with goals each could contribute to,” Fahlbusch said. Using a similar approach, under Monika’s leadership Forbes has honored BMC as one of America’s Best Employers for the last three years. BMC has also been included in India’s Top 50 Best Places to Work as well as India’s Top 15 Technology Companies to Work for the last two years. Starting with recruiting, Monika and her team work to redefine every employee-facing process so that prospects, new hires, and employees have a chance to see how their voices matter and how their contributions make a difference. Attaining personalization at scale is essential to becoming one of the best companies in the world to work for and was one of the most valuable key takeaways from the panel.

Pursuing the goal of being one of the best companies to work for in the world takes exceptional effort in the area of recruiting too, which continues to be one of the most paradoxical areas of talent management. Russell Williams, Vice President of Human Resources at PARC, says the best strategy he has found is to define the ideal attributes of high performers and look to match those profiles with potential candidates. “We’re finding that there are many more attributes that define a successful employee in our most in-demand positions including data scientist that are evident from just reviewing a resume and with AI, I want to do it at scale,” Russell said. Ashutosh Garg, founder, added: “that’s one of the greatest paradoxes that HR departments face, which is the need to know the contextual intelligence of a given candidate far beyond what a resume and existing recruiting systems can provide.” Ashutosh added, “it’s a perfect problem to solve with machine learning and AI, as CHROs can make use of all available data on candidates, free from bias, and make the best possible hiring decision for the company and the applicant.”


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