About this session
People decisions already involve judgment. Adding AI does not remove that judgment — it changes where it happens.
Decisions are influenced by which data is collected, how success is defined, what the system is asked to predict, which variables are used, how outputs are interpreted, and how much authority humans give the recommendation.
Participants learn to examine the complete decision process rather than asking only whether an AI tool is 'biased'.
They explore how unfairness can enter through historical data, labels, proxies, incomplete information, inappropriate comparisons, thresholds, deployment choices, and human interpretation.
They also examine automation bias: the tendency to give machine-generated outputs more credibility simply because they appear analytical or objective.
The session then builds a practical human-in-the-loop approach.
Participants clarify what AI should and should not contribute, define the evidence humans need to review, design meaningful challenge and escalation, document important decisions, communicate uncertainty, and create routes for correction when an AI-supported decision may be wrong or unfair.
The goal is not human judgment instead of AI — it is better human judgment with AI.
What your people take away
- Understand where fairness risks can enter an AI-assisted workplace decision
- Distinguish AI support from decisions that require meaningful human ownership and accountability
- Recognize data, proxy, measurement, threshold, and automation-bias risks
- Evaluate AI outputs alongside context, evidence, uncertainty, and individual circumstances
- Build challenge, review, documentation, and correction into high-impact decision processes
- Use AI to strengthen rather than weaken fair and accountable human judgment
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What each length covers
30 minutes
30 Minutes — Key Points: Understand Where AI Enters the DecisionA focused 30-minute session on three key points from Understand Where AI Enters the Decision: map the whole decision process, define what the AI is actually doing and match oversight to impact.
- Map the whole decision process
- Define what the AI is actually doing
- Match oversight to impact
Covers 3 of 4 points in Understand Where AI Enters the Decision.
60 minutes
Keep Human Judgment at the Center of AI DecisionsA focused introduction to fair AI-assisted workplace decisions. Participants map where AI enters the decision, identify common bias and automation risks, and use a practical human-review framework before relying on an AI recommendation.
- AI-supported vs. human-owned decisions
- Decision impact
- Data and proxy bias
- Automation bias
- Meaningful human review
- Fair AI Decision Quick Check
- Live Q&A
Covers 1 of 4 parts: Understand Where AI Enters the Decision.
90 minutes
Build Fairer Human-in-the-Loop DecisionsThe extended session moves from AI decision mapping into data quality, fairness, proxy variables, human oversight, contextual evidence, structured judgment, escalation, and decision documentation. Participants apply the approach to realistic workplace cases.
- AI decision architecture
- Historical data and labels
- Proxy variables
- Fairness questions
- Automation bias
- Structured human review
- Challenge and escalation
- Human-in-the-Loop Decision Planner
- Live Q&A
Covers 2 of 4 parts: Understand Where AI Enters the Decision and Find Bias & Fairness Risks Before They Scale.
3 hours
Covers 3 of 4 parts: Understand Where AI Enters the Decision, Find Bias & Fairness Risks Before They Scale and Make Human Oversight Meaningful.
Full day
Fair, Human Decisions with AI LabAn applied deep-dive covering all four modules. Participants work through realistic hiring, performance, workload, and development scenarios; identify where bias and weak assumptions could enter; challenge AI outputs; conduct structured human review; and redesign the decision process around fairness, documentation, correction, and accountability.
- Module 1 — Understand Where AI Enters the Decision
- Module 2 — Find Bias & Fairness Risks Before They Scale
- Module 3 — Make Human Oversight Meaningful
- Module 4 — Build Fair, Accountable Decision Processes
- AI Decision Process Mapping
- Data & Proxy Bias Challenge
- Fairness Definition Exercise
- Automation Bias Scenario
- Human Review Simulation
- Context & Evidence Review
- Decision Documentation Practice
- Challenge & Correction Design
- Fair AI Decision Playbook
- Live Q&A
Covers all 4 parts.
Also available as a 4-week or 8-week program, for change that lasts. Ask us about it.
What’s covered
Understand Where AI Enters the Decision
Every length- Map the whole decision processAn AI system may rank candidates, summarize feedback, flag patterns, recommend schedules, identify development needs, or produce a score.
- Define what the AI is actually doingTerms such as 'recommendation', 'prediction', 'fit', 'risk', or 'performance' can sound more precise than they really are.
- Match oversight to impactUsing AI to organize low-impact information is different from using it in hiring, promotion, performance, compensation, scheduling, workload, or other decisions with significant consequences for a person.
- Keep decision ownership humanHuman oversight is not meaningful when somebody simply clicks approve after seeing an AI recommendation.
Find Bias & Fairness Risks Before They Scale
90 minutes and longer- Question the dataHistorical data reflects the systems, opportunities, decisions, and inequalities that produced it.
- Watch for proxy variablesA model may not use a protected characteristic directly and can still rely on variables that correlate with it or reproduce related patterns.
- Challenge the definition of 'good'Every prediction depends on what success, performance, potential, risk, fit, or suitability has been defined to mean.
- Look beyond one fairness numberFairness can involve different questions: who receives opportunities, whose errors matter, whether similar cases receive similar treatment, and whether outcomes differ systematically between groups.
Make Human Oversight Meaningful
3 hours and longer- Resist automation biasAI-generated rankings, summaries, and scores can acquire an authority they have not earned simply because they appear systematic.
- Bring back the missing contextAI systems work with the information available to them.
- Use structured human judgmentHuman discretion can correct machine limitations, but unstructured discretion can also reintroduce favoritism, inconsistency, and bias.
- Know when to stop or escalateA reviewer needs authority to challenge the system when evidence is weak, outputs conflict with known facts, consequences are significant, or the situation falls outside the tool's intended use.
Build Fair, Accountable Decision Processes
Full day only- Document the reasoningImportant people decisions become difficult to review when nobody can explain what information mattered, how AI contributed, and why the final judgment was made.
- Create routes to question & correctFair processes need a way to deal with errors.
- Monitor what happens after deploymentA system that performed acceptably during testing can behave differently as populations, jobs, policies, data, or human use change.
- Keep humans accountable for the systemResponsibility cannot be transferred to 'the algorithm'.
Tools and materials
- Human–AI Decision Architecture
- Decision Impact Map
- Data Fitness Review
- Proxy Risk Map
- Fairness Question Builder
- Automation Bias Diagnostic
- Meaningful Human Review Canvas
- Context & Evidence Checklist
- Override & Escalation Matrix
- AI-Assisted Decision Record
How booking works
- Book online. Create a free Workbliss profile, then pay with session credits, by card or by invoice.
- Pick a date. Choose a date at least 14 days ahead, or book now and set the date later.
- Choose your expert. Within 3 working days, we send you profiles of the experts available for your date.
- We deliver it. Your expert is briefed on your people and goals, then runs the session live online or at your site.
Questions about Fair, Human Decisions with AI
How long is Fair, Human Decisions with AI?
Fair, Human Decisions with AI runs as 30 minutes, 60 minutes, 90 minutes, 3 hours or full day. You choose the length when you book. A 30-minute session covers three key points from Understand Where AI Enters the Decision: map the whole decision process, define what the AI is actually doing and match oversight to impact. A 60-minute session covers Understand Where AI Enters the Decision in full, with a live Q&A. A 90-minute session adds Find Bias & Fairness Risks Before They Scale. A 3-hour session adds Make Human Oversight Meaningful. A full-day session adds Build Fair, Accountable Decision Processes. It is also available as a 4-week or 8-week program.
Can Fair, Human Decisions with AI be delivered online or in person?
Yes, both. It runs live online, so every office can join at the same time, or in person at your site, in any country.
How do we book Fair, Human Decisions with AI?
Create a free Workbliss profile with your work email, then open Fair, Human Decisions with AI to see the price for each length and book it. You can pay with session credits, by card or by invoice, and choose a date at least 14 days ahead, or set it later.
Who leads the session?
A vetted Workbliss expert, briefed on your people, culture and goals before the session. After you book, we send you profiles of the experts available for your date within 3 working days, so you can choose who leads it.
Can it be tailored to our company?
Yes. Every session is shaped around your goals, your people and the benefits you already offer, and delivered by experts in your people’s languages.