Bill Boorman approaches chatbots in talent acquisition with both enthusiasm and a healthy dash of caution: "if we think we're AI now, and we use our technology as AI when it's not, the potential to just mess this up is huge. However, equally so the opportunity to do something different."
So what is the opportunity, the risk and the investment necessary?
The Opportunity of Creating Connection
Chatbots combine chat and task: "chat should be the way we gain information; the task is what we do with that." On the one hand, "the effectiveness of the chatbot as a user interface: taking static information and turning it into a conversation" was a pleasant, unexpected surprise. However, "the relevance is in the task."
"I don't believe there is an element of Recruiting or HR which we can't enhance by using chatbots."
In one example, 80% "of the tasks involved in hiring" could be already automated. Then, the question became: "if we've got 80% of our time back, what could we do that we don't do now? How could we change things, how could we do things better?"
"If we look at current talent acquisition, the bulk of the investment is spent on attraction. What if we already know these people, and we already know something about them, we could match those people to opportunities. The challenge then is: how do we stay relevant and interesting, so you want to stay connected with us?"
With automation, we can save time, as well as shift the focus from attracting applicants to creating meaningful connections with candidates over time. One possible opportunity, which has not been explored much but which benefits both companies and candidates, is supporting learning and development in non-vertical careers. "Today, people are invested in building their career paths, but moving away from a vertical career within a single organization, "losing their learning and development opportunities." Using a conversational UI in a learning context has proven effectiveness: you could read a book and "learn some foundational knowledge, but you could only bring that to life by coming and having a conversation with me." This is one possibility of how chatbots can be used not just to automate processes but create new value.
The Risk of Automating Bias
The risk is that we run is that if "we don't learn from the right data" and if we don’t define "good" outcomes in the right way, "we won't improve anything - we'll be not only biased, but biased more often, more consistently, every time we make a decision. If you've got your foundations wrong [or] your datasets, actually you're increasing the volume of things like unconscious bias.”
For example: "let's look at the data of the top 10 percent of performers in our organization, find the patterns, and only hire people who look like that. Which sounds like a good decision, [but] if those high performers were selected using unconscious bias, we're justifying an unconscious bias."
We can automate and improve many areas of HR with Machine Learning and Natural Language Processing (NLP); the temptation to call it "AI" changes our expectations, makes us impatient and makes it difficult to create these technologies carefully.
"In our enthusiasm to market and hype we've moved ahead of reality. A lot of products are geared around AI or Artificial Intelligence. The reality is we don't see any artificial intelligence in HR and recruiting technology currently. There's no AI there!"
The challenges we face are not only technical but organizational. "I could use technology to make sure I've got a balanced shortlist of genders, or of race, but that doesn't mean those people are going to be hired. Unless we [change the organization] so it's an inclusive environment, I think we're making the situation worse by putting a more diverse target list into that."
"If the decision-making is the same, what's the point of technology and data in there? You're getting scale and volume, but I want to see a matching technology that produces a list that I wouldn't produce as a person, that challenges some perceptions because the ‘good’ outcome has factored in diversity and inclusion."
The Investment of Time
So, what must we do? "Give it time to work. It will take test projects, and time. If I hire people from an automated matching that introduces people into the hiring funnel different from the people that the humans would have selected, the only way I can know whether that actually works is over time: are these people more successful? If we understand the stakes, we will invest the time."
"We need to make organizations agile. You can bet whatever problems we're thinking about now are not problems that are going to exist by the time we fix them."
As developers and designers, "[we must be] challenging the data, making sure the data is right. Challenging the perception of good and using data to do that. Challenging why do we believe the things we believe, and do the things we do. But also making sure we have enough time for our machines to learn and make the right decisions."
Bill joining jobpal as Advisor
To Bill, jobpal is building a real product supporting compelling use cases, not just "a hypothetical conversation about what might be." He was also interested in our commitment to develop our own NLP engine:
"Luc [Dudler, CEO] was very clear that through Machine Learning he wanted to develop his own NLP, and I found that really exciting. We're creating something new, which might create something unique for the organization. That made me think, actually, out of all of these chatbot companies, jobpal is the interesting one. I just got really excited by what you're up to, where it was going. So it just made sense to say, let's work together, let's try and shape this."
We are very excited to have Bill Boorman’s input in helping us shape how we personalize the candidate experience at scale, and explore future opportunities of chatbots in talent acquisition.