Detecting Bias and other Toxic Workplace Behavior using AI
The potential for bias in data and artificial intelligence programming has been widely acknowledged. Now AI is playing a leading role in rooting out and analyzing bias and other workplace risks to improve organizational health.
Undeniably, bias in data leads to bias in AI models. It’s a lingering issue that machine learning engineers and data scientists are careful to take into account. But AI can now be used in a novel way to detect bias in the workplace. Not only bias but also other negative workplace dynamics that can erode employee motivation, engagement, productivity, increase attrition rates, and tear at the very fabric of corporate culture.
Bias + Toxicity = Risk
A study by Gallup found that employee disengagement costs U.S. businesses $450-550 billion a year. Another study, by The Center for Talent Innovation proved these findings. Employees who perceive bias at work — based on gender, age, race, religion, appearance, politics, etc. — are three times more likely as those who don't perceive bias to quit within a year. These disgruntled employees are also much more likely to stagnate at work, to withhold ideas or solutions, and to speak ill about their company outside of work.
But bias is just one of a number of factors contributing to a workplace at high risk. Other irritants include:
• Employee burnout from overly onerous deadlines, tense office politics, and feelings of isolation from working remotely
• Toxic managers who play favorites, rewarding some and penalizing others unfairly
• Productive jerks who behave more like gang members versus team players
• Quiet volcanoes — talented employees who don’t bring up concerns but instead quietly look for other opportunities
All of these behaviors cost organizations in lost revenue, lack of competitiveness, and degraded reputation thanks to employee discontent, manifested by disengagement on the job, high attrition rates, and brand sabotage.
Capturing and Analyzing Pre-attrition Signals
Symptoms and causes of a dysfunctional workplace are all too familiar. Companies typically respond with good intentions. They make efforts to expand communications and understanding through mentoring, training, and opportunities for growth and advancement. Yet attrition continues to plague companies, costing American businesses alone $1 trillion annually, according to Gallup.
At the root of the problem is the lack of timely visibility into what’s happening among employees. Managers need this insight to understand what’s going on with each employee and to respond proactively and in the right way to address problems. Even working in the same building or campus, this insight can be hard to attain and sustain in our fast-paced workaday world and it’s even more challenging with remote workers.
Thankfully, AI is coming to the rescue.
Patterns of behavior can be identified in metadata, analyzed, and used to better and more proactively care for employees. By building and deploying AI models based on diverse behavioral metadata sources in enterprises, AI can detect and expose systemic bias and other talent risks with high efficiency and at tremendous scale. That’s exactly what our patent-pending talent risk management product Procaire™ does.
Procaire Talent Risk Management
By analyzing enterprise metadata, Procaire’s machine learning algorithms discover patterns that may include the early signs of institutional bias or other organizational risk factors. The software learns behaviors, identifies them, and provides early attrition risk detection. Multiple neural network layers detect connections among normalized signals that are impossible for humans alone to see. By analyzing key features that indicate enterprise risk, Procaire examines what its machine learning model has found and provides risk factor explanations, recommendations, and action process workflows.
There are hundreds of data inputs that generate many thousands of parameters derived from the organization's behavioral historical metadata, capturing each employee’s connectedness, career growth markers, contribution factors, and other contextual parameters.
Gathered as metadata and depersonalized to maintain employee confidentiality and privacy, these signals become early warnings to managers. The correlated data can also be used for diversity and inclusion compliance reporting.
Executives and line managers no longer have to guess what emerging risks and behaviors may be brewing within your organizations. Now, armed with facts, you can respond proactively to forge and sustain an equitable and healthy workplace with higher engagement, productivity, and retention.