Canadian HR Reporter

CANADIAN HR REPORTER DECEMBER 2018 8 NEWS CAREpath is the only Canadian Health Care navigation program of its kind offered in Canada. We have extensive experience in navigating Canadians through the health care system. Cancer Assistance Seniors' Care Assistance HealthCare Assist Your Wellness Partner CEO of HireGround Software So- lutions in Calgary. "And if it's mistrained, then it gives out wrong data. So, you're always working at getting a better set of algorithms that give accu- rate data," she said. "It's always a work in progress and… you're constantly trying to refine those algorithms and get- ting them better." e more data you have, the more accurate the results, said Kalman. "So that's your objective, to feed it more data and get better re- sults, and refining the data source so you can start to slice and dice your data a bit better and refine it so those algorithms are more accurate." People are expecting perfec- tion, but machine learning is still ultimately a product of human input, said Stam. "We hope for but don't expect perfection from humanity yet, and it's only as good as the input. It still leads to great conversations about what is neutral; we are try- ing to take out human bias, and it forces organizations to have this conversation, to think about what criteria are creating these tradi- tional barriers." Part of the problem with the recruiting AI is we are trying to create this new paradigm of gen- der and pay equality, she said, but "there's no pre-existing models or information or data to build upon necessarily, and so machines are only as good as their input, they're not a crystal ball. So we will have a few steps yet to go before it can be truly neutral because it can only base its learning on existing in- formation, or have humans at the table to tweak that input to reflect where we want to go." ere's an emphasis on AI to overcome biases by designing objective and beta-driven algo- rithms, with little recognition of how biases are baked within data sets, said Nicole Leaver, Boston- based fellow at the Jeanne Sauvé Foundation in Montreal. "Of course, the goal should be finding ways to manipulate algo- rithms to produce equitable re- cruitment practices, that's a good thing, but where we missed a step, where it's rapidly going forward, is it's not really recognizing the types of data that's being fed into the algorithm in order to really understand and comprehend the roots of existing biases and how they're maintained and perpetu- ated, especially when it's applied to labour rights." Google, for example, invested millions of dollars into a lab where it tried to figure out a way to create the perfect team, only to find out that perfect team synergy didn't really exist in a recipe, she said. "at's what I think really com- plicates AI is there's an emphasis on moving towards this automat- ed efficiency and unbiased world, without the emphasis on increas- ing and protecting the workers themselves," said Leaver. "To lay responsibility on coders or programmers would maybe be a jump or not necessarily fair. I think the responsibility extends beyond that into the data that's being used and the data that's not being used, especially when it comes to its application." Fixing the bias Part of the solution involves work- ing with AI to lead it into a more positive and equitable direction, she said, "and at least posing questions during its development phase, because that's been one of the major issues with a lot of au- tomated systems that were sud- denly integrated into workplaces was that the… ethics weren't mainstreamed throughout the process — it was an afterthought, and obviously when ethics are an afterthought, that will lead to ma- jor grievances." It's also about undoing the premise that technology and AI are neutral, said Molnar. "A lot of people make the mis- take if you introduce new tech- nologies, they're going to be more accurate and less biased than a hu- man being. But when you think about it, AI or algorithm just sim- plifies. It's a bit like a recipe, so if you have a biased recipe, you're going to have biased cake." Humans have bias and discrim- ination in their decision-making, she said, and "that data will be used by the machine that it learns on, so we are basically replicating issues we have already." Greater diversity at every stage of the process, such as the design and oversight mechanism, also makes sense, said Molnar. And that can mean interdisciplinary conversation too "because there's a lot of expertise out there about how technology needs to be thought about. But we're not hav- ing conversations across different disciplines and expertise — we need to have policy-makers, aca- demia, civil society, technologists, communities around the table to think about how we're going to be moving forward with these technologies." And it's also possible there are certain areas where this technol- ogy is just a no-go zone, especially when basic fundamental human rights are at stake, such as immi- gration or employment, she said. "You definitely need checks and balances… We need to have a broader conversation as a soci- ety on what it means to be aug- menting or even removing human decision-makers at any given mo- ment, because if that's where we are heading, we really need to think about what that is going to look like and also what the mecha- nisms for appeal are going to be." Everybody needs to be realistic about the purpose of AI, and what kind of tool it is, said Stam. "It's a great way to bring some efficiencies to process, to synthe- size or distill information in front of you — in this case, recruiting. I'm not sure that it should always be the final decision-maker, espe- cially in HR, when you really are looking to build a workplace cul- ture," she said. "A workplace culture is stron- ger when there's a great diversity there, but there's still some spidey sense that comes into play here." We're all still learning how to do it; it's still in its infancy, said Stam. "It's the adage of 'garbage in, garbage out,' or your putout is only as good as your input. We're learning what we need to put into the machine learning to punch out the right information, or punch out neutral information," she said. "So the AI is a tool (to use) alongside human judgment but I do love the role AI can play in terms of really helping to weed out as much human bias as possible, but not replace humans." And while it's possible discrimi- nation claims may arise around the use of AI, it would also be interesting to know how much worse the Amazon output would have been had humans done the same process, said Stam. "We just notice it more with AI because we're spitting out this formula and we can pick up the algorithms or the patterns a little more, but if you still had a whole decentralized army of human re- sources professionals doing this, I'm not convinced that it would have been substantially better, it's just that we're expecting higher standards with AI. So, I definitely don't think we should throw the baby out with the bath water, it's still a step-by-step process." 'You're constantly refining those algorithms' AI < pg. 1 Jeff Weiner, CEO of LinkedIn, in San Francisco, Calif., in 2013. The company uses AI features to help recruiters find candidates. Credit: Stephen Lam (Reuters)

• Cover