The most typical challenge is the basic one. Most hiring systems and technologies are biased, as they are designed for use cases involving non-disabled individuals. Hence, disabled individuals entering the hiring pipeline face certain accessibility challenges. Ron recalled his blind friend’s experience with an AI scheduling system, where the AI couldn’t help him switch from a phone to a video interview, which he preferred. Such bias could also happen with humans. 

Only 5% of employers have recruiting programs that support disabilities, while there are more than 75% of programs for race, ethnicity, gender, etc. Leaders are not directly at fault here since disabled employees often don’t disclose for fear of job security. By raising more awareness at the leadership level, leaders can push aside the stigma, limit bias, and champion disability inclusion so that their teams will also begin to embrace impartiality and empathy. Ron quoted Albemarle Corporation, which has walked the talk with such initiatives. 

Today is about doing more with less. And with AI systems, this is absolutely possible. When asked about how to get empathy built into AI systems to eliminate any bias, Ron responded that the root of bias in AI comes from training it with biased data. But he’s optimistic about AI evolving further in the future, becoming bias-free and empathetic toward disabled candidates, ensuring equity. 

Most enterprises deal with high applicant volume, where one-on-one engagement and candidate experience will be in jeopardy if not for AI, which can talk with candidates on behalf of recruiters. AI could at least respond to them, which is far better than not responding at all. AI enhances communication, especially with overworked recruiting teams, and connects the dots with the human factor, thus optimizing candidate experience. 

If individuals build AI systems by carefully identifying and eliminating innate bias, AI could be a game-changer, since you only have to train it once and scale from there. AI’s tenure is extensive, and continuous improvement is possible to get closer to the ideal bias-free hiring model. There will be unique situations, but by learning from failures and integrating those lessons into the model, AI can get better.