What is the role of artificial intelligence in recruiting? This talk gives an overview about recruiting tech. It also cited studies on AI acceptance and shows potential use cases of intelligent technology in talent acquisition. Finally, I classify them into the good, the bad, and the ugly. Get inspired! Happy to connect: https://www.linkedin.com/in/futureofhr/

1. AI in Recruiting
D r. D a n i e l M ü h l b a u e r
T H E G O O D , T H E B A D A N D T H E U G L Y

2. Knowing vs. Doing
currently use or implement AI projects in HR.
think AI is a highly relevant technology.
79% of firms 8% of firms
AI-study 2020 by Deloitte

3. PEOPLE
TOOLS
DATA
ORG
PROCESS

4. Robot-Coworker = popular?
of the surveyed people are
optimistic and excited about
robot-coworkers (n=8000)
53% 64% 36%
AI@WorkReport by Oracle
of the surveyed people
would trust a robot more
than their manager (n=8000)
of the surveyed people think
that robots are less biased
than managers (n=8000)

5. MALE APPLICANTS
RECEIVE SIGNIFICANTLY
MORE RECRUITER
CALLBACKS THAN
FEMALE APPLICANTS.
(Rivera/Tilcsik, 2016)
MALE APPLICANTS
ARE PREFERRED FOR
"MALE
OCCUPATIONS", BUT
WOMEN ARE NOT
PREFERRED FOR
"FEMALE
OCCUPATIONS".
(Koch et al., 2015)
NO REVERSAL OF
DISCRIMINATION IN
RECRUITING AFRICAN-
AMERICAN
APPLICANTS SINCE
1989.
(Quillian et al., 2019)
FEMALE
APPLICANTS OVER
40 RECEIVE
SIGNIFICANTLY
FEWER RECRUITER
CALLBACKS.
(Carlsson et. al. 2019)
APPLICANTS WITH
A MIGRATION
BACKGROUND
RECEIVE
SIGNIFICANTLY
FEWER
RECRUITER
CALLBACKS.
(Moa, 2017)
FEMALE
RECRUITERS
PREFER WOMEN.
MALE
RECRUITERS DO
NOT PREFER MEN.
(Carlsson/Eriksson,
2019)
WOMEN
GENERALLY
RECEIVE FEWER
RECRUITER
CALLBACKS,
ESPECIALLY IF
THEY HAVE
CHILDREN.
(Gonzales et al., 2019)
SHOULD WE
PERHAPS GIVE
INTELLIGENT HR
TECHNOLOGY A
CHANCE AFTER
ALL?

6. RECRUITING FAILS?
46% of all
hires

7. The solution to the hiring puzzle
is finding your firm’s happy place
between HR Tech and HR Touch!
DANIEL MÜHLBAUER

8. THE GOOD
RECRUITING ANALYTICS &
PROCESS MINING
Intelligent technology analyzes your
recruiting funnel and identifies
errors, circularities, inefficiencies of
your recruiting.
INTELLIGENT STRING-
BUILDER
Intelligent technology analyzes your
job posting and suggests the best
Boolean string for active sourcing.
JOB-ADS w/ COMMUTE-
ANALYSIS
For each job posting, candidates
receive an analysis of the work route
including seasonal weather effects,
means of transport and rush-hours.
TEXT-MINING OF
COMPANY INFOS
An intelligent technology analyzes all
publicly available company
information and derives various
indicators of employer quality from
the language used.
Good AI-based REC technology focuses
specific HR tasks and enables a linkage of
artificial and human intelligence!

9. THE GOOD
SKILL-BASED MATCHING
Intelligent technology compares
requirements and qualifications at
the ski level and predicts a match
quality.
AUTOMATED APTITUDE
TESTING
Intelligent technology combines
validated methods of aptitude
diagnostics to predict aptitude in real
time.
PERSONALIZED
CAREERPAGES
Intelligent technology modifies
career site content based on target
group analysis and typical click
behavior.
AVATARS FOR RECRUITING
TRAINING
Intelligent virtual avatars enable the
realistic simulation of interviews for a
variant-rich training of recruiting trainees.
Good AI-based REC technology focuses
specific HR tasks and enables a linkage of
artificial and human intelligence!

10. THE BAD
EMOTION RECOGNITION
Emotion recognition is a very serious
research direction, but its results
have not yet reached market
maturity.
FIT-PREDICITION BASED
ON X
The fit between people, jobs, teams,
companies or cultures are very
complex. The data models are often
not reliable.
AD TARGETING ON SOCIAL
MEDIA
Job posting algorithms are not
transparent enough. They can subtly
discriminate.
Poor AI-based REC technology focuses on
contexts that are too complex, are
opaque, or are still in validation. As a
result, their use should not yet be full-
scale, but in pilots or validation studies.

11. THE UGLY
INVALID
SELECTIONMETHODS
Poor selection processescannot be
salvaged with AI use. Examples:
Graphology, MBTI, DISG, profiling.
DISCRIMINATION IN
VIDEO RECRUITMENT
Algorithms for image and video
processing often work significantly
worse for non-light-skinned people:
PERSONALITY-ANALYSES
-> PERFORMANCE
Personality is not a stable predictor
of subsequent job performance. AI-
based technology does nothing to
change that.
Bad AI-based REC technology is based on
dubious or even discriminatory
procedures whose automation reinforces
systematic disadvantage!
FACE-ANALYSES -> ATTITUDES
& BEHAVIOR
Analyses of micro-expressionsto
infer any traits or emotions of a
person are not valid.

12. YOUR USE CASE
Map the recruiting process
including all important
process steps (tasks)!
PROCESS ANALYSIS
Select the process steps that
can be standardized!
SELECT
Focus on the processes with
business relevance!
FOCUS
Translate the selected
process steps into
algorithms or out-of-the-box
tools!
TRANSLATE

13. Breaking down silos...the expedition
into HR's technological future will
not take place in a silo!
DANIEL MÜHLBAUER

14. HR MUST HAVE A SOLID
UNDERSTANDING OF HOW TO DESIGN
AND SCALE INTELLIGENT TECHNOLOGY
TECH MUST HAVE A SOLID
UNDERSTANDING OF THE
HUMAN DIMENSION OF WORK
THE MISSING LINK?

15. COMPUTER LINGUISTICS
EXPLAINABLE AI &
FAIRNESS
SOCIAL ROBOTICS &
AFFECTIVE COMPUTING
VIRTUAL & AUGMENTED
REALITY
THE BIG LEAPS?

16. HAPPY TO CONNECT!