✒️ Foreword
For leaders focused on cost, risk, and return, here’s the case: ethical AI governance isn’t a burden—it’s a smart investment. It protects against fines, reduces rework, and helps scale systems more efficiently.
What You Gain—and How to Measure It
IBM and Notre Dame’s 2024 report (IBM Institute for Business Value, 2024) outlines three types of ROI from AI ethics:
- Economic: lower legal costs, fewer compliance failures
- Reputational: stronger trust with customers, regulators, and employees
- Capabilities: shared systems and workflows that save time across AI teams .
Companies with operational ethics programs are 27% more likely to exceed revenue growth targets, according to IBM’s global survey. And 75% of executives now see AI ethics as a competitive edge.
Return on AI ethics into three categories, each with real financial implications. Economic ROI comes from avoided fines, lower legal costs, and smoother operations. Reputational ROI shows up in stakeholder confidence, stronger ESG ratings, and better staff retention—critical as scrutiny grows from both regulators and the public. And Capabilities ROI reflects the long-term payoff: governance tools, platforms, and review processes that can be reused across AI projects, reducing duplication and scaling more efficiently over time.
The Cost of Doing Nothing
The EU AI Act allows fines up to €35 million or 7% of global turnover for non-compliant AI systems. Ethics programs cut that risk early (EU AI Act, 2024).
They also save on operations. As one Fidelity VP put it, skipping a unified governance layer means “you spend more money with everyone building the same thing from scratch”.
Finally, ethical oversight supports better ESG performance—linked to a 10–15% lower cost of capital, as noted in The ROI of AI Ethics by The Digital Economist.
Ask your team:
Would we rather fix this after the fact, or build it right the first time?
💡 AI ethics, done well, reduces cost, avoids fines, and speeds delivery. That’s not overhead—it’s smart governance.
—Jakub
Curator, AIGL 📚
☀️Spotlight Resources
AI Governance Readiness Checklist by Cognitive View
🔍 Quick Summary
This resource is a streamlined, business-oriented checklist for organizations aiming to operationalize AI governance. Designed by Cognitive View, it walks through eight core readiness areas—ideal for compliance teams, legal counsel, or internal risk officers trying to map and mature their AI governance programs.
📘 What’s Covered
The checklist is organized into eight governance pillars:
- AI Discovery – Identifying all AI usage (including shadow AI and third-party tools) across the org.
- Data Governance – Ensuring data quality, privacy, consent, and secure handling practices.
- Model Development – Best practices for version control, bias testing, drift monitoring, and secure deployment.
- Governance & Risk Management – Integrates frameworks like NIST AI RMF and ISO/IEC, with guidance on AI-specific risk thresholds and role definitions.
- Regulatory Alignment – Emphasizes traceability, gap assessments, and breach response protocols.
- Ethics & Transparency – Bias testing, explainability (e.g., SHAP, LIME), and human appeal channels.
- Ongoing Culture – Promotes AI training, stakeholder feedback, and sandboxing for experimentation.
- Next Steps – Includes a maturity self-assessment, platform demo pitch, and AI auto-discovery scan.
Each section has clear checklists and actionable bullets—especially valuable for teams getting started or standardizing decentralized practices.
💡 Why It Matters
This checklist hits the sweet spot between operational detail and framework alignment. It connects risk controls to actual business processes—procurement, HR, IT—rather than staying at the principle level. For orgs seeking a lightweight tool to benchmark or guide their AI governance efforts, this is a strong option. It’s also aligned with current standards like ISO 27001, ISO 42001, and NIST AI RMF, making it easy to integrate with broader compliance initiatives.
🧩 What’s Missing
While this version is practical, it’s also a teaser—it’s a brief version of a more comprehensive tool. There’s no access to templates, assessment forms, or concrete implementation support unless you contact Cognitive View directly. Smaller organizations may also need examples, visuals, or interactive formats to operationalize these ideas without extra lift.
Also, while strong on structure, it’s light on escalation paths (e.g. what to do when bias is found, or breaches occur during retraining).
“AI Act Governance: Best Practices for Implementing the EU AI Act”
🔍 Quick Summary
“AI Act Governance: Best Practices for Implementing the EU AI Act” is a practical guide developed by the International Association for Privacy Professionals (IAPP) and law firm Wilson Sonsini. It targets privacy professionals, legal counsels, and governance leads preparing to comply with the EU AI Act. Unlike many high-level explainers, this resource zooms in on operational readiness—especially for high-risk systems and general-purpose AI (GPAI). Think of it as a roadmap for building the internal scaffolding needed to survive regulatory scrutiny.
📘 What’s Covered
This 14-page white paper is broken into four best practice pillars:
- Corporate Governance – Recommends designating a board-level AI officer, aligning AI and privacy risk functions, and documenting accountability chains.
- Compliance Frameworks – Emphasizes the need to build structured AI governance programs with risk classification, impact assessment workflows, and audit readiness.
- AI Risk Management – Outlines how to integrate AI risk into enterprise-wide ERM systems. Notably, it links NIST AI RMF and ISO/IEC 42001 as the backbones of technical and organizational control mapping.
- Documentation & Transparency – Stresses the need for system cards, logs, audit trails, and internal justification notes—especially around transparency and human oversight.
The final sections touch on:
- GPAI-specific obligations (transparency, copyright disclosures, systemic risk logs)
- Vendor accountability and procurement practices
- Enforcement trends and anticipated regulator behavior
💡 Why It Matters
As companies move from theoretical readiness to operational implementation, the need for aligned, defensible governance frameworks is immediate. This paper helps organizations do three things well:
- Prioritize which AI use cases need the most attention (especially under high-risk classification).
- Map overlapping requirements between the AI Act, GDPR, and NIST/ISO standards.
- Build a compliance posture that anticipates audits—not just avoids fines.
It’s also one of the few resources that gives GPAI models the focused attention they deserve. That’s critical, as most legal teams are still catching up to the new categories and systemic risk obligations.
🧩 What’s Missing
- No visual assets – A simple governance maturity model or compliance checklist would make this much more usable across teams.
- Little SME support – Smaller organizations may struggle to apply these best practices without templates or tools.
- Scant real-world case studies – The examples are helpful but limited. A deeper dive into how specific industries (like health or finance) are applying these practices would add depth.
- Limited interaction with conformity assessment bodies – While mentioned, there’s little concrete advice on managing notified bodies or third-party evaluators under the Act.
🌙 After Hours
Sometimes you need a break from prompt engineering and just watch a guy build a kiln in the woods.
The Silent YouTube King
Primitive Technology is a no-talking, no-music, no-nonsense YouTube channel where one guy builds everything — huts, furnaces, tools — from scratch using just what’s around him. It’s like ASMR for your overtaxed brain.
Read the Wiki or watch the channel
🔥 Why it works
He doesn’t explain anything. He just does it. And yet, you learn more about systems, iteration, and constraints than in most tech keynotes. The dude reverse-engineered iron smelting with zero Wi-
Got a favorite low-tech obsession that clears your head? Hit reply — I could use a few more.
Lost Blogs of the 2000s
Lucy Pham’s Abandoned Blogs board on Are.na is a quiet archive of forgotten blogspot pages, old Tumblrs, and weird personal sites that haven’t been touched in over a decade. It’s like flipping through a digital attic.
📓 Why it hits
There’s something oddly moving about half-written posts, broken links, and last-updated dates from 2009. You get glimpses of lives — teens posting playlists, students ranting about exams, someone documenting a failed startup — then… silence.
🌐 Digital ghosts, governance edition
What happens to content with no user, no login, no policy? Who owns it? Who remembers it? It’s all very “right to be forgotten” meets “right to be stumbled upon.”
Got a favorite corner of the internet that’s quietly fading? Send it — I’ll keep the lights on.
