Align algorithmic bias and fairness assessment with the wider objectives of the transformation programme rather than optimising it in isolation.
Assessing Algorithmic Bias and Ensuring Fairness in Automated Decisions
Learn to design, measure and defend your organisation's approach to algorithmic bias and fairness assessment.
📍 Istanbul🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
Vendors sell algorithmic bias and fairness assessment as a product; it behaves in practice as a change programme. The distance between a working prototype of fairness assessment and a system the business can depend on is where most budgets disappear. Participants take away a working set of documents supporting the digital and data-driven work discipline, ready to be adapted internally. What blocks progress on algorithmic bias and fairness assessment is usually unclear ownership rather than unclear intent. It establishes a shared vocabulary for this area of digital and data-driven work so that teams can disagree productively about it. It is designed for mixed groups, so that fairness assessment is examined from more than one functional angle. Discussion is anchored to worked examples of algorithmic bias and fairness assessment rather than to abstract argument. Where fairness assessment is measured, it improves; where it is only discussed, it drifts. The course ends by identifying what the participant will stop doing to make this aspect of digital and data-driven work sustainable.
Expected Learning Outcomes
Define escalation and fallback for fairness assessment when the automated path fails.
Specify the data algorithmic bias and fairness assessment depends on, where it originates and who is accountable for its quality.
Structure records of fairness assessment so that they answer the questions an auditor will actually ask.
Establish version control and rollback for every component of algorithmic bias and fairness assessment that reaches production.
Draw practical lessons from failures in fairness assessment without assigning blame that suppresses reporting.
Evaluate the bias, fairness and explainability obligations attaching to algorithmic bias and fairness assessment.
Who Should Attend
Analysts producing the data on which decisions about algorithmic bias and fairness assessment rest.
Chief information officers accountable for the investment in fairness assessment.
Information security officers reviewing the exposure created by algorithmic bias and fairness assessment.
Public sector digital leads applying fairness assessment under procurement and privacy rules.
Procurement and contracting staff whose agreements set obligations around algorithmic bias and fairness assessment.
Solution architects designing how fairness assessment fits the existing estate.
Course Modules
Algorithmic bias and fairness assessment: security, privacy and regulatory obligation
2 sessions · 8 pointsSession 1Choosing a supplier for algorithmic bias and fairness assessment without being captured
- List the data sources algorithmic bias and fairness assessment consumes and confirm each has a named owner.
- Define the service level fairness assessment must meet and what happens when it is missed.
- Verify that algorithmic bias and fairness assessment still performs when input volume doubles unexpectedly.
- Identify the data already collected that bears on fairness assessment.
Session 2Who owns fairness assessment once the project team disbands
- Test algorithmic bias and fairness assessment against edge cases drawn from real historical records.
- Define the trigger that would require fairness assessment to be redesigned.
- Record the reasoning behind each architectural choice in algorithmic bias and fairness assessment.
- Design the pilot for fairness assessment so that a negative result is still useful.
Fairness assessment: monitoring, drift and operational ownership
2 sessions · 8 pointsSession 1What breaks first when fairness assessment meets real volume
- Set the review interval for algorithmic bias and fairness assessment and who attends.
- Assign responsibility for keeping documentation of fairness assessment current.
- Benchmark the organisation's algorithmic bias and fairness assessment against comparable operations.
- Decide what will be stopped to create capacity for fairness assessment.
Session 2The governance fairness assessment needs and the governance it does not
- Identify the skills the team lacks to operate algorithmic bias and fairness assessment independently.
- Build the competence framework that supports fairness assessment.
- Record the rationale for each significant choice made about algorithmic bias and fairness assessment.
- Set out the decisions in fairness assessment that require sign-off and by whom.
Fairness assessment: cost, licensing and total running expense
2 sessions · 8 pointsSession 1Reviewing fairness assessment when nothing has gone wrong
- Establish what evidence demonstrates algorithmic bias and fairness assessment is under control.
- Rehearse the briefing on fairness assessment that would follow an incident.
- Build the internal briefing that explains algorithmic bias and fairness assessment to those affected.
- Build the user briefing that explains what fairness assessment does and does not decide.
Session 2What has to be agreed before work on algorithmic bias and fairness assessment starts
- Classify the data in algorithmic bias and fairness assessment and apply access controls that match the classification.
- Decide which legacy process fairness assessment retires, and set the date.
- Estimate compute and licensing cost for algorithmic bias and fairness assessment at expected and at peak load.
- Assess the regulatory obligations fairness assessment triggers in each jurisdiction.
Fairness assessment: business case, scope and the data it depends on
2 sessions · 8 pointsSession 1Where fairness assessment touches systems nobody wants to change
- Confirm the retention and deletion rules applied to data inside algorithmic bias and fairness assessment.
- Compare the cost of fairness assessment with the cost of its absence.
- Verify six months later that changes to algorithmic bias and fairness assessment have held.
- Reduce the variation in how fairness assessment is carried out between teams.
Session 2Getting other functions to support fairness assessment
- Identify single points of dependency in algorithmic bias and fairness assessment and reduce them.
- Plan how fairness assessment is versioned and how a bad release is rolled back.
- Specify the fallback path when algorithmic bias and fairness assessment is unavailable.
- Agree who is on call for fairness assessment outside working hours.
Choose the package that suits you
Silver Package
At least 3 people
USD1,250
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Gold Package
At least 3 people
USD1,850
- 5-night stay in a 5-star hotel
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Complete your registration
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