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Digital ethics in practice
AI 4 GOOD
“ TECHNOLOGY IS NEITHER GOOD NOR BAD; NOR IS
IT NEUTRAL.”
FIRST LAW OF TECHNOLOGY, MELVIN KRANZBERG (1917-1995), PROFESSOR...
Human digital
Proactieve en reactieve ondersteuning
Face to face
Wanneer nodig en met een meerwaarde
Digital first
24/7/36...
WAAROM DOEN WE DIT ?
Aanreiken van informatie en tools waarmee werkzoekende én
werkgever zelf aan de slag kan
Interactie m...
OPINIE
SIHAME EL KAOUAKIBI
VROUW? COMPUTER SAYS NO
● ✦Opinie
● ‘De VDAB is een voortrekker van
het ethisch gebruik van artificiële intelligentie’
Digital Ethics en Privacy : a “must have”
DPO team lead
Privacy
AVG
Ethische AI
AI 4 GOOD
WAAROM DOEN WE DIT ?
> Verantwoordelijkheid tegenover de Vlaamse overheid
en de burgers
> Evolutie naar een same...
Europese en lokale overheden definiëren richtlijnen voor het
ethisch ontwikkelen en gebruik van AI systemen
> Europa: ‘Eth...
Trust Transparency Benefit
AI 4 GOOD
AI systems which have impact on people must be devellopped and continuously monitored...
FROM THEORY TO PRACTICE
MAKE THE PRINCIPLES REAL
Principes Begrijpen Meten
Actie &
preventie
• Trust
• Transparency
• Bene...
CONTRETE OUTCOMES
USE CASE : KANS OP WERK
Meten
“TRUST” one-pager & detail
document gevalideerd door
het Core team
Princip...
Principles & playbook Discover with impact
Output from the ethical AI practice
Interpreted principles – this can be used f...
AI 4 GOOD JOURNEY
> Creation of ambassadors
> Evangelize on critical thinking from the idea and aks
the right questions when developping AI ...
Putting AI 4 Good into practice
Exploit
Experiment
Explore
Experimental
bias
Design
issues
Execution
issues
Data bias
Mode...
Explore
Exploit
Experiment
Define expected outcome, potential
value and impact of AI use case
(ensure this is aligned with...
Ethics oversight defined roles
Role Brief description
Executive Committee Is responsible to underwrite the advice of the D...
PURPOSE OF JOBNET
To make it easier for CITIZENS (and EMPLOYERS) to find suitable match of JOBS (or
CANDIDATES), through r...
SOURCES OF UNFAIRNESS IN AI SYSTEMS
What we will not assessWhat we will
assess
Training Data
The dataset for training the
...
Making a success of the AI 4 Good organization, we
need to ensure we have the right teams
CommunicationProject team* Analy...
You need a sustainable operating model
Ethical board structure
DPO – digital ethicist
Privacy & ethics
Director labor mark...
• Transparency with external parties ( blackbox effect)
• Enable and keep innovation capacity
• Governance model => keep i...
VINCENT BUEKENHOUT
DIGITAL ETHICIST & DATA PROTECTION OFFICER
VINCENT.BUEKENHOUT@VDAB.BE
0497 39 43 88
Tapping the ethical...
Ethical AI at VDAB, presented by Vincent Buekenhout (Ethical AI Lead, VDAB) at the Trustworthy and Ethical AI Conference o...
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Ethical AI at VDAB, presented by Vincent Buekenhout (Ethical AI Lead, VDAB) at the Trustworthy and Ethical AI Conference on Feb 13th in Brussels

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Vincent Buekenhout presented the various AI initiatives at VDAB, its AI4Good strategy, the way applications are designed, and most of all, the way ethics, measurements through KPI's, explainability and fairness play a role in this. Vincent also explained how ethics-by-design works at VDAB.

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Ethical AI at VDAB, presented by Vincent Buekenhout (Ethical AI Lead, VDAB) at the Trustworthy and Ethical AI Conference on Feb 13th in Brussels

  1. 1. 1 Digital ethics in practice AI 4 GOOD
  2. 2. “ TECHNOLOGY IS NEITHER GOOD NOR BAD; NOR IS IT NEUTRAL.” FIRST LAW OF TECHNOLOGY, MELVIN KRANZBERG (1917-1995), PROFESSOR, AND CO-FOUNDER, SOCIETY FOR THE HISTORY OF TECHNOLOGY https://www.youtube.com/watch?reload=9 &v=rPKrdxiEkQ0 73% van de werkzoekenden in Zweden denkt dat ze tijdens het interview worden gediscrimineerd. Door de mens te vervangen door AI-robotkop Tengai, denken de ontwikkelaars dat ze het screeningproces eerlijker en eerlijker kunnen maken.
  3. 3. Human digital Proactieve en reactieve ondersteuning Face to face Wanneer nodig en met een meerwaarde Digital first 24/7/365 online dienstverlening 3
  4. 4. WAAROM DOEN WE DIT ? Aanreiken van informatie en tools waarmee werkzoekende én werkgever zelf aan de slag kan Interactie met de klant op zijn maat en verwachtingspatroon Consulenten en instructeurs zijn geen dossierbeheerders. Het zijn vakmensen, met een hart voor hun metier. De eerste inschatting van de klant gebeurt met behulp van data Proactief detecteren van obstakels in arbeidsmarktintegratie = slim gebruik van onze rijkdom aan data Stijging aantal online inschrijvingen Stijging online zoekgedrag (12=>26%) 88% tevredenheid mbt eerste inschatting 60% tevredenheid over de jobsuggesties Snellere doorstroom bij nood aan persoonlijke dienstverlening (+20%) Op 6 maand na inschrijving is 68,3% terug aan het werk
  5. 5. OPINIE SIHAME EL KAOUAKIBI VROUW? COMPUTER SAYS NO
  6. 6. ● ✦Opinie ● ‘De VDAB is een voortrekker van het ethisch gebruik van artificiële intelligentie’
  7. 7. Digital Ethics en Privacy : a “must have” DPO team lead Privacy AVG Ethische AI
  8. 8. AI 4 GOOD WAAROM DOEN WE DIT ? > Verantwoordelijkheid tegenover de Vlaamse overheid en de burgers > Evolutie naar een samenleving ‘Human + Machine’ > Gebruik van persoonlijke data en AI voor geautomatiseerde besluitvorming > Bias in de data en algoritmes kunnen leiden tot ongewenste impact op burgers
  9. 9. Europese en lokale overheden definiëren richtlijnen voor het ethisch ontwikkelen en gebruik van AI systemen > Europa: ‘Ethics guidelines for trustworthy AI’ en requirements waaraan AI systemen zouden moeten voldoen > België: coalitie AI 4 Belgium werkt een AI plan uit voor o.a. het ethisch gebruik van data > Frankrijk: ethische principes om AI in dienst te stellen van de mensen > Duitsland: nationale AI strategie die stelt dat AI op een ethische manier geintegreerd moet worden in de maatschappij > Verenigd Koninkrijk: richtlijnen voor het ontwikkelen en gebruik van AI systemen in de publieke sector AI 4 GOOD WAAROM DOEN WE DIT ? VDAB wil een voorbeeld zijn
  10. 10. Trust Transparency Benefit AI 4 GOOD AI systems which have impact on people must be devellopped and continuously monitored following our three principles
  11. 11. FROM THEORY TO PRACTICE MAKE THE PRINCIPLES REAL Principes Begrijpen Meten Actie & preventie • Trust • Transparency • Benefit • Interpretatie VDAB & begrijpbaar voor de burger • Bruikbaar voor interne en externe communicatie • Kwalitatief meten tijdens het ontwikkelings- process • Kwantitief meten van ‘fairness’ in data & model • Korte termijn acties voor “Kans op Werk” • Lange termijn aanbevelingen voor nieuwe AI use cases • Workshops & co-creatie met het Core team met medewerkers uit: • Arbeidsmarktbemiddeling • Ondersteunende diensten • I&T • Evalueren van Trust bij AI use cases, data & modellen door bevraging van medewerkers • Evaluatie van gebruikte data & model “Kans op Werk” • Interpretatie van de metingen • KT acties voor “Kans op Werk” • LT aanbevelingen voor nieuwe AI use cases WAT? HOE?
  12. 12. CONTRETE OUTCOMES USE CASE : KANS OP WERK Meten “TRUST” one-pager & detail document gevalideerd door het Core team Principes BegrijpenPrincipes Begrijpen Kwalitatieve evaluatie van “Kans op Werk”, AI process en bias d.m.v. interviews en vragenlijsten Meten Kwantitatieve analyse “Kans of Werk” data en model MetenMeten
  13. 13. Principles & playbook Discover with impact Output from the ethical AI practice Interpreted principles – this can be used for oversight & communication Assessment results & results template MVP v2 to assess data & model in qualitative and quantitative way One pager: Transparency One pager: Benefit AI 4 Good summary Full playbook + Communication pilot plan, AI communication template, channel & frequency described Full playbook, describing the responsible AI organization
  14. 14. AI 4 GOOD JOURNEY
  15. 15. > Creation of ambassadors > Evangelize on critical thinking from the idea and aks the right questions when developping AI systems > Internal communication about the ethical dev and use of AI > Digital ethicist role and governance board Initiatives AI 4 GOOD AMBITION > Continuous monitoring of bias in data and models > Ethical Governance board with Veto right and empowers Ethics by design > External commnucation about ethical development and the use of AI Ambition
  16. 16. Putting AI 4 Good into practice Exploit Experiment Explore Experimental bias Design issues Execution issues Data bias Model bias Communication failures Operational bias Conception flaws Use case is known (from business) vs. use case is not known
  17. 17. Explore Exploit Experiment Define expected outcome, potential value and impact of AI use case (ensure this is aligned with VDAB mission & vision) 1 Define the sensitive variables used in the experiment / AI use case and verify Governance is needed. 2 Perform qualitative assessment (identify conception flaws or experimental bias) 3 Select fairness metrics & define fairness for the use case Create & share internal communication material Share lessons learned, and incorporate changes in Playbook, ensure collaboration & diversity across teams 1 Perform qualitative & quantitative assessments (These can run in parallel & at different stages of the project) 2 Receive & review assessment results & provide feedback 34 5 1 Create & share external communication material Share lessons learned, and incorporate changes in Playbook, ensure collaboration& diversity across teams Monitor using the quantitative assessment & perform qualitative assessment checks (execution issues, communication failures & operational bias) Receive & review assessment results & provide feedback 2 3 4 The AI 4 Good organization - overall actions Based on the initial playbook, we have created a light version of the key steps for execution during the AI lifecycle = Integrate feedback improvements when required
  18. 18. Ethics oversight defined roles Role Brief description Executive Committee Is responsible to underwrite the advice of the Digital Ethicist which has been overruled by the Director Innovation & Architecture Director Innovation & Architecture Has oversight of all AI initiatives in VDAB, knowledgeable of their status and ethics assessment results. Has the authority to overrule advice from the Digital Ethicist. Digital Ethicist / Informational Ethicist An ethics expert that provides ethical guidance to employees or teams defining AI use cases and developing or maintaining AI systems. Is responsible to evaluate the ethics assessment results at each gate of the AI delivery lifecycle and provides an advice to proceed or to address ethical issues. GDPR expert Ensures that AI system development and data used in that process is compliant with the GDPR regulation. Legal Expert Well-versed in the legal dimensions relevant to the types of applications, technologies, policies, and projects that fall within the committee’s purview. Legal experts are crucial to ensuring legal compliance, as well as for identifying areas where current legal guidance is absent, inadequate or ambiguous. Data Architect / Data Expert Ensures that the data that is being collected can be collected as per the ethical guardrails and is collected in a secure way. Aligns with the Digital Ethicist on project specific data needs and purpose to ensure this can be collected in an ethical and secure manner. Analytics & AI team lead Has oversight of the AI delivery lifecycle and manages the AI portfolio. Responsible to Project Manager Responsible to plan the project activities, allocate resources, ensure the methodology is applied, organize project team meetings, report project progress to sponsor, ensure the necessary deliverables are in place to proceed through the AI delivery lifecycle. Specifically for Ethical AI, the Project Manager needs to ensure that the Qualitative and Quantitative Algorithmic Assessment results (deliverables) are produced in the various steps of the project. Data Scientist Works within the Analytics and AI team and part of a project team developing AI systems. Is responsible to execute the Machine Learning lifecycle: data preparation model creation, model evaluation, model refinement and model deployment. As part of that lifecycle, the data scientist is responsible to run the Algorithmic Assessment steps on the input data, model output and mode itself. The Data Scientist needs to discuss and review the results with a Business Expert. He is also responsible to peer review the assessment results of colleague data scientists working on in a different project team.
  19. 19. PURPOSE OF JOBNET To make it easier for CITIZENS (and EMPLOYERS) to find suitable match of JOBS (or CANDIDATES), through relevant recommendations reflecting the current market situation driven by jobseekers’ and employers’ interests.
  20. 20. SOURCES OF UNFAIRNESS IN AI SYSTEMS What we will not assessWhat we will assess Training Data The dataset for training the algorithm may have unintentional bias due to unbalanced sample representativeness. Engineered Features Engineered features (embeddings) that are used to convert raw data into algorithm compatible data may be biased. The modelling process may lead to incorporation of data bias which will then be propagated to future predictions. Model Model Outcome The outcomes generated by the model might amplify the bias due to training data or model. A B C D
  21. 21. Making a success of the AI 4 Good organization, we need to ensure we have the right teams CommunicationProject team* Analytics & AI team Oversight get data protection & ethics right Collaboration build the right reflexes Measuring data driven ethics Communication promote a culture to speak up Data Protection & Ethics team
  22. 22. You need a sustainable operating model Ethical board structure DPO – digital ethicist Privacy & ethics Director labor market management Business & Operations Director Innovation Innovation & analytics University ethics & AI expert External, research (NEW ideas) Director Strategy & Policies HR / Values & Strategy CEO / or Executive Committee ?
  23. 23. • Transparency with external parties ( blackbox effect) • Enable and keep innovation capacity • Governance model => keep it light • Data Governance is key • Open data • Guarantee neutrality • Explain vs justify vs responsibilise • Ethics by design integrated with Privacy and Security by design • Communication internal en external ( reputation impact)
  24. 24. VINCENT BUEKENHOUT DIGITAL ETHICIST & DATA PROTECTION OFFICER VINCENT.BUEKENHOUT@VDAB.BE 0497 39 43 88 Tapping the ethical forces of change

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