COMMUNITY BIG (HR) DATA, BIG (HR) CHALLENGES · engine can continue spinning out faulty and...

COMMUNITY‘BIG (HR) DATA, BIG (HR) CHALLENGES’POOLSTOK

26/02/2019 – De Punt Gent(brugge)

Welkomstwoord Vincent Van Malderen

01

2

3

COMMUNITY

Coachingtrajecten onder de loep

Leuven - Stadhuis

15 juni 2017

Evaluatie²

Gent - Zebra

24 oktober 2017

Welzijn, pick (y)our brain

Mechelen - Lamot

13 maart 2018

Reflecties over Flexwerk

Antwerpen - Havenhuis

13 september 2018

Big (HR) Data, Big (HR) Challenges

Gent - De Punt

26 februari 2019

7

AI

Machine learning / neural networks

Algoritmes/ predictiveanalytics

Blockchain in Gov

RPA / Robotics / ...

NU

TOEKOMST

ETHIEK

PRIVACY

GDPR

EVIDENCE-BASED

Powered by …

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Poolstok community:

50% discount!!

De code 'OpenBeliever_Poolstok' geeft 50% korting op een regular

ticket. (Geldig van dinsdagochtend tot woensdagavond)

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1JAAR

NIEUWE WEBSITE! ➔ Nieuws, blog, community, …

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13u30 - 13u40 Welkomstwoord door Vincent Van Malderen, algemeen directeur, Poolstok

13u40 - 14u20 HR analytics: een brug over troebel waterBernie Caessens, Resolved

14u20 - 14u50 Ethische aspecten van big dataKatleen Gabriels, assistant professor Maastricht university

14u50 - 15u20 Pauze

15u20 - 15u50 Een brug slaan tussen wetenschap en data, voer voor HR innovatieCédric Velghe, The VIGOR Unit

15u50 - 16u20 The Pros and Cons of Digital Footprint Analysis in HRVesselin Popov, Business Development Director for the University of Cambridge Psychometrics Centre

16u20 – 16u40 Debat

16u40 – 18u30 ReceptiePowered by Rooffood

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Dank u

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RESOLVEDdata-science for humans

HR ANALYTICSEEN BRUG OVERTROEBEL WATERBERNIE CAESSENS | MANAGING PARTNERPOOLSTOK COMMUNITY | 26.02.19 | GENT


THE UNIVERSALPROBLEM-SOLVING

ALGORITHM

WRITE DOWN THE PROBLEM

WRITE DOWN THE SOLUTION

THINK REALLY, REALLY HARD ABOUT IT

1

2

3


THE ANALYTICSVERSION

TAKE YOUR HR DATA


1

2

3

APPLY HR ANALYTICS TO UNDERSTAND AND SOLVE


AITO THERESCUE

TAKE YOUR HR DATA


1

2

3

USE AI: LET THE COMPUTER SOLVE IT


DO NOTSTART WITH DATATHINK SMALL

DATA

INSIGHT

STORYDATA

INSIGHT

QUESTION

THE EMPIRICAL METHOD


[2]

WHAT IS HRANALYTICS

UNDERSTANDING HOW HUMAN BEHAVIORSYSTEMATICALLY INFLUENCES THE RESULTS OFAN ORGANISATION THROUGH THE USE OFMEASURABLE (QUANTIFIABLE) INDICATORS


A FAMILIAREXAMPLE

CULTURE IS KING

Improving our culture will improve our company

Improve productivity

More applicants

Outpace competition

A two-step approach to improving culture

Release satisfaction survey

Develop plan

1

2

How we’ll measure success

70% Survey completion

10% Increase in satisfaction

20% Increase in applicants

5x more referrals


THE PROBLEM

IN HR

A STATEMENT, NOT A PROBLEMBY THE WAY, WHAT DO YOU MEAN WITH CULTURE?

SOUNDS LOGICAL, BUT IS THERE A REAL CAUSAL LINK WITH ALL THREE CONCEPTS?

IMPLICITLY ASSUMES A LINK BETWEEN CULTURE AND SATISFACTION

NO SUCCESS FOR PRODUCTIVITY?ONLY KPI’S WITHIN HR


[1]

HC BRIDGEFRAMEWORK

IMPACT EFFECTIVITY EFFICIENCYWHAT IS THE IMPACT ON OUR STRATEGIC GOALS OF INCREASING THE QUALITY OR AVAILABILITY OF OUR TALENT POOL?

HOW STRONGLY DO OUR HR PROGRAMS AND PROCESSES INFLUENCE THE CAPACITY, ACTIONS AND INTERACTIONS AMONGST OUR TALENT POOLS

HOW MUCH HR PROGRAMS AND PROCESSES CAN WE GET FOR OUR INVESTMENTS IN TIME AND MONEY

HOW HIGH IS OUR ANNUAL TURNOVER?

HOW MUCH OF ANNUAL TURNOVER IS REGRETTED LOSS?

HOW MUCH DID REGRETTED LOSS IMPACT OUR CAPACITY TO CLOSE DEALS?


ANALYTICSTHE FAILURE

LACK OF A FRAMEWORK

LOGIC

LACK OFPROPER

DATA


LACK OF AFRAMEWORK

IMPROVE PRODUCTIVITY

MORE APPLICANTS

COMPANY CULTURE


SOURCES FOR

LOGIC

DECOMPOSE THE PROBLEM AND

CONNECT TO YOUR KNOWLEDGE

SCIENTIFICLITERATURE


ASK QUESTIONSTO BUILD AFRAMEWORK

Which values, beliefs and norms guide individual’s and group behavior within our organization

Overall, more than 25% of our projects are over budget and over time. Is there anything related to our culture that plays a part in this?

TEAM

VALUESCOMPETENCIES

PROJECT OK?


EXPAND AND

REFINEFRAMEWORK

PROJECT ON TIME/BUDGET

TEAM MEMBER VALUES

SUPPLIERS

Attract applicants with the right set of values

BETTER PROJECT

ESTIMATES

PM SKILLS

TEAM MEMBER MOTIVATION

Attract the right PM skills


TEAM COMPOSITON

ANALYTICS &LOGIC GUIDEACTION


TEAM MEMBER VALUES

SUPPLIERS


EMPLOYER BRAND &RECRUITMENT PROCESS

LEARNING &DEVELOPMENT

PERFORMANCE & REWARD


SUPPORTSDECISIONS

TEAM COMPOSITON


TEAM MEMBER VALUES


EMPLOYER BRAND &RECRUITMENT PROCESS

LEARNING &DEVELOPMENT

BUDGETTALENTSKNOW-HOWEXPECTED IMPACT


LACK OFPROPERDATA

HR DATA ARE EVERYWHERE

HR DATA ARE MESSY

HR DATA ARESPARSE & DISCRETE

MANY INTANGIBLES

THE EMPIRICAL METHOD


[3]

THE STATE

OF AI

PATTERN RECOGNITION ON PAR WITH HUMANS

WITH MASSIVE DATA FOR TRAINING


[4]

THE HOW

OF AILOW LEVELRAW DATA

NETWORKRESPONSE

CORRECTRESPONSECOMPARE

ADJUST TO MINIMIZE ERROR

LEARNING


LEVEL 2 | USE MACHINE LEARNING TO MEASURE BEHAVIOR AND CAPTURE REAL-TIME DATA

LEVEL 1 | USE MACHINE LEARNING TO CLEAN OR LABEL DATA AUTOMATICALLYAPPLICATION

IN HR?

HR DATA ARE EVERYWHERE HR DATA ARE MESSY

HR DATA ARESPARSE & DISCRETE MANY INTANGIBLES

LEVEL 3 | USE MACHINE LEARNING TO MEASURE, TRACK & PREDICT

DATA-CAPTURE

DECISION-MAKING


EXAMPLESFOR HR

LEVEL 2

LEVEL 1

LEVEL 3

AUTOMATIC CV-SCREENING: EXTRACTING STRUCTURED INFORMATION FROM UNSTRUCTURED TEXT

AUTOMATIC CLASSIFICATION OF E-MAIL COMPLAINTS INTO CATEGORIES

EXTRACTING PERSONALITY PROFILES FROM SOCIAL MEDIA

EXTRACTING JOB POSITION FROM TWEETS

E-MAIL SENTIMENT ANALYSIS

PREDICTING PERSON-JOB FIT FROM CV-ANALYSIS


AUGMENTEDINTELLIGENCE

DATA

INSIGHT

QUESTION

LEVEL 1 | ACCESS TO RAW DATA

LEVEL 2 | ENRICHED DATA

DATA

STORY

LEVEL 3 DECISION MAKING

MORE RESEARCH NEEDED

RESOLVEDdata-science for humansCONCLUSION

WRITE DOWN THE BUSINESS PROBLEM

IMPLEMENT ACTIONS AND START OVER

WORK REALLY, REALLY HARD ON IT WITH THE PROPERTOOLS & METHODS

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2

3

ANALYTICS AND AI OFFER ADDED VALUE FOR HR BUTNEITHER IS A COMMODITY (YET) NOR AUTOMAGIC


THANK

YOUSOURCES

IMAGESAll Icons by FlatIconSlide 1 | Photo by Jonas Verstuyft on UnsplashSlide 2 | Photo Feynman Wikimedia CommonsSlide 3 | Photo by PixelRaw on UnsplashSlide 4 | Photo by Priscilla Du Preez on Unsplash

[1] Boudreau, J.W. & Ramstad, P.M. (2005). Talentship and the Evolution of Human Resource Management: From “Professional Practices” To “Strategic Talent Decision Science” Human Resource Planning Journal. 28 (2) 17-26.

[2] van den Heuvel, S. & Bondarouk, T. (2016). The Rise (and Fall) of HR Analytics. Paper presented at the 2nd HR Dvision International Conference, Sydney.

[3] Goodfellow, I. et al. (2014). Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks, arXiv: 1312.6082v4

[4] Radford, A., Jozefowicz, R. & Sutskever, I. (2017). Learning to Generate Reviews and Discovering Sentiment, arXiv: 1704.01444v2

Katleen Gabriels

Assistant professor UM

[email protected]

Poolstok

26.02.2019

Big data ethics

https://www.washingtonpost.com/opinions/its-okay-to-be-paranoid-someone-is-watching-

you/2018/03/27/1a161d4c-2327-11e8-86f6-

54bfff693d2b_story.html?noredirect=on&utm_term=.8a457c337d5d

https://www.academicforecast.org/about

• Big data ethics = critical thinking

- Correlation is not causation

www.facebook.com/depoorterdries

• Big data ethics = critical thinking

- Not everything that counts can be counted

Outline

‛Ethical implications of:

‛1. Tracking employees (IoT)

‛2. Machine learning at work (AI)

Quantifiedworkplace.eu

Quantified workplace

• Presented as self-tracking but: also other-tracking

• Surveillance and coveillance

• Neo-Taylorism / Digital Taylorismo ‘The corporeal turn’

• Privacy and physical integrity

• Control versus Self-Determination Theoryo ‘Success’, ‘efficiency’, ‘productivity’

‛Gabriels, K., & Coeckelbergh, M. (2019, in press). ‘Technologies of the self and other’: How self-tracking

technologies also shape the other. Journal of Information, Communication and Ethics in Society 17 (2).

https://www.businessinsider.com/amazon-patents-bracelet-that-tracks-workers-2018-2?r=US&IR=T

Humanyze.com

https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-

learning-deep-learning-ai/

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S. (2017). Dermatologist-level

classification of skin cancer with deep neural networks. Nature 542, pp. 115-118. Doi: 10.1038/nature21056.

‛“Algorithms are opinions embedded in code” (Cathy O’Neil)

https://www.theverge.com/2018/10/10/17958784/ai-recruiting-tool-bias-amazon-report

• Analyses (and correlations) with ‘bad’ datasets

• Algorithm is not inherently objective

• “statistical systems require feedback” (p. 7)- “Without feedback, however, a statistical

engine can continue spinning out faulty and

damaging analysis while never learning from

its mistakes” (p. 7)

• The model you are applying must be trained with a lot of data, should be transparent, and should be updated regularly

O’Neil, C. (2016/2017). Weapons of Math Destruction. How Big Data Increases Inequality and Threatens Democracy.

Broadway Books, New York.

‛“you could argue that WMDs are no worse than the humannastiness of the recent past. (…) But human decisionmaking, while often flawed, has one chief virtue. As humanbeings learn and adapt, we change, and so do ourprocesses. Automated systems, by contrast, stay stuck in timeuntil engineers dive in to change them. (…) Big Dataprocesses codify the past. They do not invent the future.Doing that requires moral imagination, and that’s somethingonly humans can provide. We have to explicitly embed bettervalues into our algorithms, creating Big Data models thatfollow our ethical lead. Sometimes that will mean puttingfairness ahead of profit”‛(O’Neil, 2017, pp. 203-204)

‛1. Codes of ethics and professional conduct• “the Hippocratic Oath ignores the on-the-ground pressure that data

scientists often confront when bosses push for specific answers”

(O’Neil, 2017, p. 206)

‛2. Strong regulation• “To disarm WMDs, we also need to measure their impact and

conduct algorithmic audits” (O’Neil, 2017, p. 208)

‛3. More research• Example: https://unbias.wp.horizon.ac.uk/

https://unbias.wp.horizon.ac.uk/

• No blind faith in data set and algorithms

• Does the problem need a big data solution?

- If yes, look for an evidence based one- Importance of education and critical thinking- Importance of long term vision- Stakeholders have to be informed

https://www.fatml.org/resources/principles-for-accountable-algorithms

Een brug slaan tussen wetenschap en dataVoer voor HR innovatie

Cédric Velghe@Poolstok Community

26 februari 2019

“Today, I will play the bad cop and shoot my own foot.”

(“HR analytics” OR “people analytics” OR “human capital analytics” OR “workforce analytics”)

● To date, this query delivers only 76 hits in Web of Science

● There is no generally accepted definition and conceptualization of what HR Analytics is (not)?

. Assumption 1 “HR Analytics is hard science.”

The research on the alleged benefits of HR analytics remains very scant (Rasmussen & Ulrich, 2015)

● Multiple pitfalls threaten to prevent HR analytics from delivering on its promises.

● The primary issue is that HR analytics too often seems to become an end in itself.

○ Governed by expanding data-availability ↔

starting from organizational challenges

○ Preoccupation with revealing statistical

associations ↔ testing substantiated theories

○ Dustbowl empiricism ↔ delivering

value to HR decision-making

. Assumption 2 “Organizations benefit from adopting HR analytics”

“Garbage in, garbage out.”

● Digital footprints are often incomplete, not necessarily accurate or representative, and often

irrelevant to the job.

○ → Collect digital data through controlled and standardized environments, e.g. digital

interviewing

○ ! Text analysis is gaining maturity, but, be wary of

psychometric claims based on image-, sound- or

video-analysis as we still lack substantiated theories

. Assumption 3 “Cybervetting using machine learning algorithms improves hiring decisions.”

“The query, (game* AND "personnel selection"), results in only 19 hits in Web of Science.”

● Statistical associations between in-game behavior and personal characteristics or outcomes are not

sufficient evidence for the validity, reliability and utility of recruitment games.

● Theories of how specific personal characteristics/outcomes are manifested through in-game

behavior need to be validated through controlled experiments

. Assumption 4 “Games are better at hiring than traditional psychometric tests”

Build HR analytics projects on the extensive collection of substantiated theories and meta-analytical findings in the academic literature (van der Togt & Rasmussen, 2017)

● ≠ cherry picking scientific studies

● = Systematic Reviews, Rapid Evidence Assessments (REA), Critically Appraised Topics

. Solution “Bridging science and practice“

“Consulting science can prevent organizations from losing time over avenues that have been extensively researched in the literature.”

● For instance, pay is a weak predictor of employee voluntary turnover

○ → Allocate data-analytical resources to other hypotheses, e.g. leadership

. Benefit 1 “Asking the right questions and focussing the efforts“

“Science can also inform organizations on the most adequate conceptualization, measurement and operationalization of the variables that they wish to analyze.”

● The distance between home and work is not a well suited metric for analyzing the impact of

commuting on employee voluntary turnover

● Commuting time can be estimated with the help of Google maps

. Benefit 2 “Adequately conceptualizing and operationalizing the variables“

“A literature review provides organizations with the opportunity to explicitly assess the potential of the analytical methods that have so far been used by academics and make well-informed choices on what methods to adopt.”

● E.g. in turnover research researchers have been overly reliant on cross-sectional and static cohort

designs (Allen, Hancock, Vardaman, 2014)

○ → Include temporal considerations, e.g. survival analysis with time dependent covariates and

time series analysis.

. Benefit 3 “Choosing the right analytical methods“

“One of the biggest risks with HR analytics is misjudging the statistical output (Wenzel & Van Quaquebeke, 2017). ”

● Whenever you obtain findings which do not align with the state-of-the-science, be careful

● Applying the effect-sizes from published meta-analyses as the prior belief in Bayesian estimations,

will reduce the risk for capitalizing on chance.

. Benefit 4 “Critically appraising the findings and improving prediction accuracy“

“Organizations can obtain valuable insights and recommendations from science which could never be obtained from the available data.”

. Benefit 5 “Consulting the scientific evidence is a core aspect of evidence-based HR“

“Science reviews and insights from organizational data provide a systematic and judicious overview of what is (not) known about your problem.”

● Avoid losing resources over reinventing the wheel

● Avoid losing money over investments that could have been expected to be ineffective

● Substantiated conclusions and recommendations that can convince your various stakeholders to

engage with the suggested innovation

● Identifying and prioritizing potential angles of approach for inventing innovative solutions

(employee* OR worker* OR staff OR workforce) AND (turnover OR retention OR attrition OR leave OR

stay OR quit OR withdrawal) → 38.783 hits in WoS

. Benefit 6 “Knowledge and expertise are key to successful (HR) innovation“

Questions?

[email protected]

The Pros and Cons of Digital Footprint Analysis in HR

OBJECT IVE 2 OF 7

To harness methodologies from psychometrics and big data analytics in predicting and

understanding human behaviour in the online environment

Disruption in context

72% feel it’s important to embrace AI, but only 31% feel ready to address it

A. 24% automating routine tasksB. 16% augmenting human skills

C. 7% restructuring work entirely

Disruption in context

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•

•

•

•

•

•

•

•

•

•

•

•

•

PREDICT PSYCHOLOGY

MAKE A DECISION

CREATE DIALOGUE

PROVIDE FEEDBACKGET MORE DATA

PREDICT PSYCHOLOGY

MAKE A DECISION

RECORD OUTCOME

Obtaining a psychological signal with consent

Using tests Using social media

Data shared with 80+ Universities worldwide

Honest feedback was the only incentive

45 peer-reviewed articles since 2011

30 validated psychometric tests

All data collected through opt-in

6 million volunteers’ psych and social media profiles

myPersonality project (2007-)

March 2013

January 2015

45 peer-reviewed publications using our data since 2011

October 2017

Political Views Religious Views Financial Risk

+ Use of addictive substances, parents’ relationship status, profession, sexuality, ethnicity, gender, age and more

Intelligence Life SatisfactionBIG5 Personality

Kosinski, Stillwell & Graepel. PNAS 2013

Predictions from social media data

Computers assess personality better than we do

Number of Facebook Likes (log scaled)

Acc

ura

cy (

self

-oth

er a

gre

emen

t)

See Youyou W., Kosinski & Stillwell. PNAS 2015

Principles for AI implementation

Based on Psychometrics Centre survey of 34,267 respondents globally

PREDICT PSYCHOLOGY

MAKE A DECISION

Communications

AI in Communications

Pros

• Automation reduces costs

• Summary and transcription tasks

• Chatbots can generate interview questions and handle onboarding

• Detect emerging employee concerns

• Ongoing satisfaction measurement

• More relevance and engagement

• Personalised content more persuasive

Cons

• Can feel impersonal

• Breed linguistic determinism

• Closed vocab tools lack nuance

• Out-of-the-box products not sensitive to company culture

• Passive analysis tools can inhibit natural conversations

Same newspaper, same date

Psycholinguistic Tailoring

Park et. Al JPSP 2015

Predicting interpersonal deviance

Predicting interpersonal devianceD

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pp

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Study design

O C E A N

O Congruent

C Congruent

E Congruent

A Congruent

N Congruent

Dance like no one’s watching - but they totally are

Beauty doesn’t have to shout

Ad

Va

ria

nt

Target Group

Introverts Extraverts

Intr

overt

ed

Extr

avert

ed

Study design

Ad Variants:

Personality-matched content is 2 x as persuasive

RETURN ON INVESTMENT (%)

Introverts Extraverts

200%

400%

0

Dance like no one’s watching - but they totally are

Beauty doesn’t have to shout

Matz, Kosinski, Nave and Stillwell. PNAS 2017

The Psychometrician’s Dilemma

Perfect Match App

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•

•

•

Perfect Match App

Machine bias

AI in Communications - Compromise

CREATE DIALOGUE

PROVIDE FEEDBACKGET DIGITAL FOOTPRINT

PREDICT PSYCHOLOGY

MAKE A DECISION

RECORD OUTCOME

AI&

HR

[email protected]

@VessPopov

Vesselin PopovBusiness Development Director

COMMUNITY BIG (HR) DATA, BIG (HR) CHALLENGES · engine can continue spinning out faulty and...

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