Pre-Flight Checklist: The Ethics of Acceleration (Post #14 of 20)
What's the moral speed limit for innovation?
“With great powers, comes great responsibility.” ~Uncle Ben, Spider-Man (2002)
I think we all need some Uncle Ben in our lives right now, as a reminder to not lose sight of what power can do both for us, and to us, as humans.
I’m a big fan of AI capabilities, but just because we can automate everything, doesn’t mean we should automate everything.
At the very least, we should have an idea of what success looks like and what would have to be true to achieve that. Continue to ask the most simple, hardest question, “why?”
Automation amplifies everything. The good, the sloppy and the unethical.
Carl Jung warned, “The greater the power of the idea, the greater the danger of it becoming inhuman.”
I wonder what he’d say if he knew about AI.
The Ethics Gap
According to a McKinsey March 2025 State of AI1 survey, only 6% of companies say they’ve hired dedicated AI ethics specialists, and just 17% are actively working to mitigate explainability risks in their systems.
This survey only represents approximately 3% of the global workforce, and includes only the surveyed companies that said “yes” to having at least one AI tool. My rough estimation says that for the other 97% of the global workforce , that number is considerably lower than 6% and 17%, respectively.
The ethical infrastructure is not keeping pace with the speed of AI adoption.
We are seeing the warning lights flash:
Generative AI (GenAI) models are spreading misinformation faster than fact-checkers can respond.
Algorithmic bias in hiring and finance is replicating discrimination inside systems meant to remove it.
Transparency gaps are widening. Most enterprise leaders can’t explain why a model made a decision, only that it did.
In one well-documented case, researchers at Lehigh University (2024) tested large language models used in mortgage underwriting and found that AI systems recommended denying more loans and charging higher interest rates to Black applicants, even when their financial profiles were identical to white applicants.2
The most notable part though, is that the bias wasn’t actually programmed, but it was learned from historical data that quietly encoded equity in lending. That’s not just a technical failure, but an ethical one.
When a model makes a decision about someone’s job, loan, or medical treatment, its mistakes don’t just scale, they multiply.
3-Step Ethical Acceleration Framework
Simple, repeatable and enforceable before any automation goes live.
Assess. Should this be automated at all?
Human Stakes Map: Who’s helped, who’s harmed, who’s ignored?
Purpose Test: Does this reduce friction for the mission you’ve defined (see Post #1), or shift burden into a quieter group?
Reversibility Check: If the model goes wrong, can you roll back outcomes within 24 hours?
Anticipate. What specific things could go wrong?
Misuse Scenarios. List the top 3 bad outcomes (bias, leakage, unsafe decisions). For each one, define a pre-commit mitigation.
Data Morality Gate. Is the data Green/Yellow/Red (Post #5)? Any red data in scope = quarantine (Post #10).
Accountability Owner: Name the person (not team) who answers when something breaks. If you can’t name them, you’re not ready.
Act Responsibility. Guardrails before go-live.
Human-in-the-Loop by Design. No irreversible actions without human approval (Post #10 and #11).
Minimum Necessary Automation. Start with assistive mode (recommendations, drafts, flags) before autonomous mode.
Ethics Pre-Flight. A 10-line the CDQ + CAIO sign together that includes a bias test passed, data tags verified, rollback rehearsed, customer notice ready, logs on, and oversight scheduled.
Test Flight: The 48-hour “Ethical Acceleration Audit.”
Run this on any AI feature currently in pilot or about to ship.
Collect. One-pager stating the problem (Post #1), users affected and the exact decisions that AI will influence.
Score.
Reversibility (0-3): Can we unwind harm inside 24 hours?
Stakeholder Burden (0-3): Does automation offload risk onto customers or junior staff?
Data Integrity (0-3): R/Y/G verified, SSOT only, DRS>/= to90% (See Post #8)
Oversight (0-3): Named owner, review cadence, audit logs on. (>/= 10 to proceed. Otherwise, remediate and rescore).
Decide. Present score and risks to your AI Governance Council (Post #12). Go/No-Go/ Hold with Remediation.
Mission Debrief
How did it go? Where did your audit surface silent trade-offs? Speed over consent, efficiency over dignity, or convenience over care?
Your customers don’t see your internal debates. Only the outcomes. Leadership is choosing constraints that protect the people your system touches.
Power is easy. Responsibility is hard. Before we accelerate further into AI, we need to ask, “Will we still recognize ourselves when we arrive?”
The real test of intelligence isn’t what AI can do. It’s what humanity chooses for it to do.
AI Exhibits Racial Bias in Mortgage Underwriting Decisions, Leigh University 2024