Blog by: Linc Markham, Vice President of Ecosystem Strategy at Beeline
Artificial intelligence (AI) has disrupted every area of our lives — from the curated shopping experiences we’ve come to expect from companies like Amazon to the personalized entertainment recommendations channels like Netflix use to market their latest content. AI has also begun to disrupt business processes, especially processes involved in talent acquisition.
From intelligent search to candidate evaluation, AI finds patterns invisible to the human eye. But to become a trusted resource, AI systems must ensure both accuracy and fairness. Failure to do so can result in unintentional bias, as Amazon famously experienced with their automated recruitment tool from 2014 to 2018. Fortunately, a great deal of progress has been made since then in designing AI programs that are both ethical and explainable.
‘Explainable AI’ and ‘Ethical AI’ describe an AI model, its expected impact, and potential biases. They help to characterize the model’s accuracy, fairness, transparency, and the outcomes of AI-powered decision making.
Explainable AI allows users – and candidates – see that the program values causation over correlation. If a candidate has an attribute that is associated with success in a role – as modeled by an approved and validated data set – the hiring organization can clearly see and explain the links between the two.
Ethical AI ensures that causation is not based on factors such as race, gender, sexual orientation, or other attributes that should not be considered in making the hiring decision.
Adhering to an ethical, explainable AI model is crucial to build trust and confidence when using AI in the hiring process.
How is ethical, explainable AI applied by Beeline clients?
Contingent workforce programs use Beeline Extended Workforce Platform to source talent that allows their companies to respond flexibly to market challenges and opportunities.
For faster and more efficient sourcing, Beeline’s technology partner, HiredScore, uses AI to review, sort, and prioritize candidates. To ensure that candidates are presented to recruiters in a consistently unbiased manner, HiredScore’s proprietary compliance layer provides bias auditing and explainable AI logging of every client’s AI program.
HiredScore’s program complies with all governmental non-discrimination standards and policies in every supported country. It also offers automated bias testing which can demonstrate unbiased AI for every client’s program automatically
AI is not the only source of recruiting bias
Removing bias from AI is not easy. But it is no harder than removing it from humans themselves. When it comes to identifying and evaluating talent, recruiters tend to spend just a few seconds looking at a resumé before deciding who to “weed out.”
Hiring managers make quick judgments and call them “intuition” or overlook hard data – a problem made worse by the general absence of objective and rigorous performance measures. Though critics may argue that AI is not much better, they often forget that these systems are mirroring our own behavior.
To cite Amazon again, criticism about their biased recruiting algorithm ignores the fact that human-driven hiring in most organizations can be equally flawed.
Realistically, we have a greater ability to ensure both accuracy and fairness in AI systems than we do to influence recruiters and hiring managers. The cognitive mechanisms that make us biased are often the same tools we use to survive in our daily lives. The world is too complex for us to process logically all the time. If we did, we would be overwhelmed by information and unable to make simple decisions.
Humans are very good at learning but very bad at unlearning. That’s why it is easier to remove the bias from AI algorithms than to change the human mind. So, for bias-free sourcing of contingent talent, we strongly recommend using ethical, explainable AI.
To learn how our approach to ethical, explainable AI can make your extended workforce program more effective, visit beeline.com.
About the author: Linc Markham, Vice President of Ecosystem Strategy at Beeline, is passionate about defining and implementing a compelling Ecosystem Integration vision for customers and partners of the world's first Extended Workforce Platform.
