Home' HR Monthly : March 2019 Contents March 2019 HRM magazine 13
5 THINGS TO CONSIDER
BEFORE ADOPTING AI
Here are Applied founder and CEO Kate
Glazebrook’s top five things HR professionals
should consider before using or building an AI
1. Where is the ‘test’ data drawn from? If it’s
existing organisational data, what are the
potential biases that could be baked into the
data? Who might be ‘hidden’ or ‘overlooked’
in your dataset?
2. Who is developing the AI? How
representative is the team and what
potential blindspots could they have? How
will you test your assumptions with people
who might be affected by those decisions?
3. What question are you really trying to
answer? Machine learning and other data
science tools are designed to optimise –
often brutally – toward a particular goal,
so be sure of the goal you actually want
4. When, where and how do you want
outcomes of the algorithm to feed into your
decisions? Will they totally replace your
existing system? If so, it is critical to do a
thorough analysis to check for any potential
biases or unintended consequences that
could arise. If they’re simply designed to
complement and support other decisions,
make sure everyone who might use them
knows that, and is occasionally reminded –
it’s easy to lose the nuance over time.
5. Audit. Make sure any algorithm you use is
rigorously tested using one of the growing
number of ‘auditing’ algorithms designed to
test for bias.
The race is on to find the best way to make AI useful
to recruiters – and that means figuring out how to
effectively ‘unteach’ our biases.
BY THEA COWIE
In Australia, the uptake of AI in recruitment
has been cautious, says Michelle Hancic, lead
IO psychologist APAC for US-based hiring
technology startup pymetrics.
“People are thinking, ‘L et's see what others
are doing and how much success they're having
before we ju mp in with both feet,’” she says.
That said, the professional services sector
seems to be taking a lead, with ANZ, EY and
Alexander Mann Solutions all making large
investments in AI recruitment solutions.
Globally, recruiters say AI is helping them
save time (67 per cent), remove hu man bias (43
per cent) and deliver the best candidate matches
(31 per cent), according to LinkedIn's Global
Recruiting Trends 2018 report.
Of course the platform has its own AI-
powered tool, Recruiter, which scores and
prioritises candidates based on their similarities
to “top performers” that you nominate.
Other AI-powered recruitment tools perform
resume screening (including platforms such
as Ideal and Amazon's now-defunct tool),
testing and initial interviews (Triplebyte), video
interview analysis (HireVue and Paññã), talent
management (Tex tkernal and Talentswot) and
chatbots for candidates (Rai, Mya and Olivia).
AI systems are built upon data you provide.
Not being careful with that data can mean you
‘teach’ the AI you r implicit (or explicit) racial,
gender or ideological biases.
This became painfully obvious in 2016
when Tay.ai, a conversational chatbot created
by Microsoft, used live Twitter interactions to
become “smarter in real time”. Within a day it
had become a racist misogynist, a ref lection of
its interactions with trolls.
More recently came the news about the
shuttering of Amazon’s experimental AI. As
Reuters reports, it was trained on 10 years of
resume submissions. Since most of those were
from men (a reflection of the tech industry’s
gender bias), the AI downgraded new resumes
which contained the word “women’s” (as in
“women’s chess club”) and those that listed two
all-women colleges. Even after those shortfalls
were fixed, the AI’s designers were worried it
still valued other discriminatory data points.
There are plenty of disruptors looking to make
AI-powered recruitment work, including
pymetrics. It delivers 12 gamified neu roscience
assessments based on academic literature, says
pymetrics' global head of diversity analytics, Dr
“First we work with the client to identify
successful incumbents and build a profile of
their cognitive, social and emotional traits,”
says the former chief analyst at the United States
Equal Employment Opportunity Commission.
Pymetrics records how incumbents perform
in the games (which measure more than 50
personality traits), then it builds a custom
algorithm representing success for the specific
vacant job function and organisation.
22/2/19 1:18 pm
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