ASKAJAY.AI

The Responsible AI Playbook
for Founders

From principles to community — the complete framework for building AI companies that last.

Series: 4 Chapters askajay.ai/thinking

Section 1: Principle Self-Assessment Matrix

Assess your organization's current state for each of the 10 responsible AI principles. Select the traffic-light rating that best reflects your maturity, then document priority actions.

1. Human Well-being
Red
Yellow
Green
Priority Actions:
2. Fairness & Bias
Red
Yellow
Green
Priority Actions:
3. Transparency
Red
Yellow
Green
Priority Actions:
4. Privacy & Data Dignity
Red
Yellow
Green
Priority Actions:
5. Reliability & Safety
Red
Yellow
Green
Priority Actions:
6. Accountability
Red
Yellow
Green
Priority Actions:
7. Human Agency & Oversight
Red
Yellow
Green
Priority Actions:
8. Inclusivity & Accessibility
Red
Yellow
Green
Priority Actions:
9. Sustainability
Red
Yellow
Green
Priority Actions:
10. Lawfulness & Regulatory Foresight
Red
Yellow
Green
Priority Actions:
Scoring Guide: Red = No controls in place. Yellow = Partial controls, gaps remain. Green = Fully operationalized with monitoring and enforcement.

Section 2: Governance Readiness Scorecard

Based on the Five-Layer Governance Stack. For each layer, answer the three assessment questions and capture notes on gaps or improvements needed.

1
Policy
Are principles translated into testable policies?
Yes
Partial
No
Are policies version-controlled?
Yes
Partial
No
Do policies have named owners?
Yes
Partial
No
Notes:
2
Process
Are governance gates integrated into development workflows?
Yes
Partial
No
Is risk tiering applied to AI applications?
Yes
Partial
No
Are exception procedures documented?
Yes
Partial
No
Notes:
3
Tooling
Is bias testing automated in CI/CD?
Yes
Partial
No
Are model cards auto-generated?
Yes
Partial
No
Is runtime monitoring in place with alerting?
Yes
Partial
No
Notes:
4
People
Is there a named accountable individual per AI system?
Yes
Partial
No
Are decision rights and escalation paths documented?
Yes
Partial
No
Do governance roles have role-specific training?
Yes
Partial
No
Notes:
5
Assurance
Are quarterly internal audits conducted?
Yes
Partial
No
Is there an annual external review?
Yes
Partial
No
Do audit findings feed back into policy updates?
Yes
Partial
No
Notes:

Section 3: Design Ethics Checklist

Use this per-feature ethical review checklist before any AI feature ships. Complete all 12 items across the three review phases.

Empathy & Stakeholders
Identified all direct users?
Notes:
Mapped indirect stakeholders?
Notes:
Tested with vulnerable/marginalized groups?
Notes:
Conducted pre-mortem for potential harms?
Notes:
Design & Prototyping
Fairness testing on prototype outputs?
Notes:
Explainability features built in?
Notes:
Privacy controls are opt-in, not opt-out?
Notes:
Human override mechanisms for high-risk decisions?
Notes:
Testing & Launch
Adversarial testing completed?
Notes:
Diverse tester demographics confirmed?
Notes:
Crisis response protocol documented?
Notes:
Post-launch monitoring plan in place?
Notes:

Section 4: Community Engagement Planner

Map your stakeholders, plan engagement activities by quarter, and track partnership relationships.

Stakeholder Mapping Table

Stakeholder Group Relationship to AI System Engagement Method Frequency Owner
End Users Direct Co-design workshops Quarterly Product Lead

Participation Calendar

Q1
Q2
Q3
Q4

Partnership Tracker

Organization Type (Academic/NGO/CBO) Focus Area Status

Section 5: 90-Day Responsible AI Sprint

Weekly action items across 12 weeks, organized by chapter theme. Check off each item as completed and track status.

Weeks 1–3: Principles

Week 1
Define organizational red lines for AI use
Status: ________
Audit training data for known bias sources
Status: ________
Week 2
Set fairness metrics for all production models
Status: ________
Document data provenance for key datasets
Status: ________
Week 3
Complete Principle Self-Assessment Matrix
Status: ________
Prioritize top 3 principles for immediate action
Status: ________

Weeks 4–6: Governance

Week 4
Establish AI council or governance committee
Status: ________
Draft AI use policy with testable statements
Status: ________
Week 5
Classify all AI applications by risk tier
Status: ________
Design governance gates for development workflow
Status: ________
Week 6
Complete Governance Readiness Scorecard
Status: ________
Assign named owners to all governance layers
Status: ________

Weeks 7–9: Design

Week 7
Run first ethical design review on a live feature
Status: ________
Prototype explainability features for highest-risk system
Status: ________
Week 8
Test with diverse user groups (accessibility + demographics)
Status: ________
Build crisis response protocol for AI incidents
Status: ________
Week 9
Complete Design Ethics Checklist for all features
Status: ________
Integrate ethical review into product development lifecycle
Status: ________

Weeks 10–12: Community

Week 10
Identify and map all stakeholder groups
Status: ________
Plan first co-design workshop with end users
Status: ________
Week 11
Establish feedback channels (surveys, forums, advisory board)
Status: ________
Draft community advisory board charter
Status: ________
Week 12
Complete Community Engagement Planner
Status: ________
Present full Responsible AI Playbook to executive team
Status: ________

Section 6: Risk Tier Classification Worksheet

Classify each AI application by risk tier. Use the reference definitions below to assign the appropriate tier, governance requirements, and review cadence.

AI Application Name Risk Tier (1–4) EU AI Act Classification Governance Gate Required Accountable Individual Last Review Date
Risk Tier Definitions:
Tier 1 — Experimental: Low-risk, internal tools. Self-certify. Annual review.
Tier 2 — Operational: Business process AI. AI lead approval required. Quarterly review.
Tier 3 — High-Impact: Decisions affecting individuals. VP approval required. Monthly review.
Tier 4 — Critical: Safety-critical or rights-impacting. C-suite approval required. Continuous monitoring.