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How to Control Hiring Bias Using AI and Automation

Conversations around diversity and inclusion are ever-evolving in our society and the HR community. While the prior dialogues focused on protected classes, the scope of discussion has expanded to include characteristics, such as sexual orientation, disability, experience, privilege, education, and more.

Emerging technologies are surfacing new ways to enhance the value of human capital, which includes enhancing diversity and inclusion within the workplace. Companies are reexamining how they hire to build a stronger workforce and become an employer of choice.

Technology Designed to Mitigate Bias

Technology is being designed to mitigate biases and, as a result, enable more diverse and inclusive workplaces. To create sustainable diversity, companies need to evaluate both pre-hire and post-hire processes. The benefit of hiring from a wide range of backgrounds is the ability to add new perspectives, encourage creativity, and promote innovation. However, post-hire, studies also find a workplace that isn’t made up of “like-minded” individuals may result in more conflict. Therefore, it’s vital that HR teams optimize their hiring process, foster an inclusive company culture, and continue to make unbiased post-hire business decisions.

Do all candidates receive the same experience?

One study revealed that white applicants received 36% more job callbacks than Black counterparts and 24% more job callbacks than Latino applicants with identical resumes. An experimental study found that applicants with disabilities that do not limit their productivity received 26% fewer responses from employers to their job applications than non-disabled applicants with identical job applications except for disability status. Standardizing the hiring process and removing access to demographic data while screening is one way to help combat this.

AllyO RECRUITTM automates the hiring process so every candidate is given a consistent, fair, and accessible candidate experience. This is by design. AllyO does not ingest demographic data to enable decision making. Instead, AllyO’s conversational AI with natural language processing (NLP) asks candidates hard and soft job requirements without ever collecting demographic information. AllyO’s dynamic NLP can understand the candidate’s answers to determine whether they meet the desired qualifications, and if so, automate scheduling and reminders, ensuring a consistent process free of unconscious bias for all applicants.

That said, talent acquisition teams still need to evaluate their standardized process. Are job applications free of biased language to attract a wide pool of applicants? Are your qualifying questions knocking out fit applicants? There are tools that evaluate bias in human recruiters or make hiring recommendations based purely on algorithms. Pymetrics is one such AI assessment tool that uses behavioral assessments to evaluate job seekers.

Are HR and managers making fair business decisions?

Hiring a diverse workforce isn’t enough; the next step is maintaining a diverse workforce through an inclusive culture and mitigating bias in future business decisions. Workplace culture is complex and fluid, and HR leaders need to take a proactive approach in shaping it. Accenture’s When She Rises, We All Rise study shows that a culture formed from bold leadership, comprehensive action, and an empowering environment advances the career progression of women and lessens the wage gap, while also advancing the professional progression of men.

There is opportunity for technology to help standardize performance reviews, compensation increases, monitor employee attitudes, and more. However, only 35% of Chief Diversity Officers claim they track employee demographic data, leaving the majority without hard numbers to support their work. A good place to start is seeing if there is statistical evidence of bias in your company’s post-hire practices. Once identified, an AI-enabled framework can provide a consistent and objective benchmark for assessing employees and job candidates alike.

Limitations of AI’s Ability to Mitigate Bias

Technology is only as good as those who built it and the data it’s trained with. While AI isn’t inherently biased, if trained on data that reflects bias, the machine will mimic the very problem we are trying to solve. This was brought center stage when Amazon’s employment recruitment AI proved to be sexist. The bot dismissed female applicants because it was trained on data that preferred men for technical roles, requiring the bot to be immediately terminated.

Clean data from multiple sources and consistent auditing is the best way to ensure technology is operating without programmed bias.

Technology is Not Enough, Start a Conversation

Diversity and inclusion is not a box that can be checked off your to-do list, and implementing AI and automation that helps mitigate bias in decision making does not replace intentional workplace conversations.

LanguageLine Solutions naturally hires what is viewed as a diverse workforce, as their translation services often lend themselves to culturally diverse hires. Loren Pofahl, Director of Global Recruitment at LanguageLine Solutions, shared in a recent webinar with AllyO that while they already standardize their process with AllyO and attract a diverse workforce, their recruiting team is in constant conversations around how different cultures view workplace norms such as timeliness and how that will impact the way they hire and evaluate success. There is never a point of complacency and where they rest their laurels; LanguageLine Solutions is constantly evaluating how they can be a better employer.

So, let’s all start having these conversations. To speak with an AI expert about standardizing your hiring process with AllyO, schedule time today.

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