Searchie hosts regular live sessions on linkedin live and youtube where you can join in the conversation.

1. Bias In Recruitment And AI
Why old practices deserve to be challenged and
do we really want a meritocracy?
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2. Often in recent history, signals identified by recruiters
are inherently subjective and can lead to biased
decision making.
Biased historical human decisions corrupt data which is
now being used to create predictive models. How do
we train a machine to be less biased if the lessons we
have to teach it are unconsciously prejudice?
Recruiting Is Intrinsically Subjective
Introduction
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3. First year attrition rates 29% in UK
Second year unemployment rate 6% in UK
Productivity in the workplace has only increased by
<2% in USA over 10 years
Employee engagement rates 32% in USA
What does the data say?
Evidence we are getting recruitment wrong
-6
-4
-2
0
2
4
6
8
10
Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Productivity Percentage change from Previous Quarter
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4. Gender distribution over time at Facebook US,
Engineering team
24% YOY Growth in
technical headcount; 2019-
2020
Only 7% growth in female
representation in the
technical team
0
10
20
30
40
50
60
70
80
90
100
2014 2015 2016 2017 2018 2019
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5. Ethnic distribution Facebook
Senior leadership and
decision making is a
predominantly white male
job at Facebook
Inclusiveness exists at a
lower level of
seniority/responsibility
0%
10%
20%
30%
40%
50%
60%
70%
Technical Business & Sales Senior Leadership All Roles
White Asian Hispanic Black 2 Or More Other
37/6323/77 57/43 33/67
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6. 17% of Facebook’s talent come from 10 schools,
how do they stack up ethnically?
Going to one of these
schools increases your
chances of working at
Facebook.
But only if you are white or
Asian.
Top Feeder Schools
Ethnic Diversity
Facebook All Roles
ethnic Diversity
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
50.00%
White Asian Hispanic Black 2 or More Other
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7. Join me in the conversation
LinkedIn Live Every Tuesday 5pm GST
Harvey Bennett Co-Founder & CSO | Searchie
/Company/Searchie

8. Where do “The Four” hire from?
Facebook top feeder
schools (44,931)
Google top feeder
schools (116,503)
15% in previous 12 months
17% in previous 12 months
Amazon top feeder
schools (272,377)
11% in previous 12 months
Apple top feeder
schools (182,105)
5% in previous 12 months
Top
feeder
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9. New hire job function distribution at “The Four”
Disaggregating the data
shows Apple and Amazon
having a lower
concentration of engineering
resource
Is this a reason for their
openness to education?
Non-EngineeringEngineering
0%
10%
20%
30%
40%
50%
60%
70%
80%
Google Amazon Facebook Apple
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10. In other interesting workforce migratory news from
2019…
Intel lost 1,100 people to Apple in the previous
12 months
Microsoft lost 566 people to Google in the previous
12 months
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11. Merit is hard to measure because of the criterion problem. It seems like merit
may be fairly unfair.
How meritocratic is meritocracy anyway?
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12. The problem with meritocracy
Measuring and quantifying everything
is impossible
Many external, unmeritocratic
influences
Network beats merit 9/10 Join the conversation today
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13. Join me in the conversation
LinkedIn Live Every Tuesday 5pm GST
Harvey Bennett Co-Founder & CSO | Searchie
/Company/Searchie

14. Question to the rich: What is important for success?
0
10
20
30
40
50
60
70
80
150k – 199k200k 100k – 149k 45k – 99k All Others
When people are asked
what contributes to their
success, they believe it is
because of their hard work
and merit
Nothing to do with the luck,
social background or ethnic
group then.
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15. Why it matters
Engagement is low
Reduce inequality
Attrition rates are high
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16. The data disaggregation paradigm
Without disaggregated models and data you cannot be truly representative of
the population.
The cost, time and speed to acquire data/train multiple disaggregated models
is difficult to overcome.
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17. How much are we prepared to share to be
predicted?
Predictive accuracy
Intrusiveness
The greater the volume of
signals and the closer the
context to the truth (and
consequently, the more
intrusive) the more effective
the prediction model.
Where should we draw the
line?
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18. In Summary
Your hiring patterns may be telling you a story you are not
aware of
Training machine learning on biased data will give you
more of the same
Avoid arbitrary recruitment decisions, strive for predictive
validity
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19. Join me in the conversation
LinkedIn Live Every Tuesday 5pm GST
Harvey Bennett Co-Founder & CSO | Searchie
/Company/Searchie