Title slide: Responsible and Ethical AI Frameworks: An Introduction

If you’re a data professional and you’re not brushing up on Responsible and Ethical AI frameworks, you’re missing a critical expertise in your career. It’s not just about building smart models anymore—it’s about building trustworthy ones. With regulations tightening around AI use (think GDPR, AIDA, and more), understanding these frameworks will be key to what you do on a daily basis if it isn’t already now. But beyond the legal stuff, there’s your company’s reputation for trustability to think about. Learning how to embed fairness, transparency, and accountability into your AI use isn’t just good practice—it’s good business. Plus, it positions you as a forward-thinking pro in our field.

Global Data Summit

Today I presented a “book report” on my searches for responsible and ethical AI resources. Talks are only 20 minutes, so it was a fast coverage of frameworks, guides, and playbooks for your AI projects. The slide deck is meant to be a jumping off point for finding more resources for your AI projects. It’s full of links and references.

Key Takeaways slide:
Explainable
Trustworthy
Safety & Security
Transparent
Human-focused
Metrics and Tools
Monitoring
Managing Risk
Fairness
Key Takeaways for Ethical AI

https://speakerdeck.com/datachick/responsible-and-ethical-ai-frameworks

I wish I could have spent 20 minutes or more on each topic:

Explainable
Trustworthy
Safety & Security
Transparent
Human-focused
Metrics and Tools
Monitoring
Managing Risk
Fairness

I recently found that NASA has one, too.

NASA Ethical AI Mind Map

http://nasa.gov/nasa-artificial-intelligence-ethics/

NASA publishes their AI plan as well

ETHICAL ARTIFICIAL INTELLIGENCE 
Fair 
Human Resources, 
Union 
Diversity & Inclusion 
Leverage Higher 
Government Guidance 
Equality Laws & 
Policies 
Mitigate Bias 
Human-Centric 
and Societally 
Beneficial 
Al Embedded in 
Mission Systems - 
Remote, etc. 
Inform Humans 
when Al is Used 
Governability: 
Human/Machine 
Responsibilities 
Handling Inherently 
Government Functions 
Explainable & 
Transparent 
Trust: Theory, 
Technologies, Culture 
Data Collection 
Transparency 
Digital Forensics, 
Logs, Decision 
Records 
Predictability, 
Reliability, Consistency 
Accountable 
IJser Responsibilities 
Legal/Policy 
Maintain Al Over 
Lifetime 
Development Stan- 
dards & Responsibil- 
ities 
Al Registry/CataIog 
Governance 
Guidance, & Decisions 
Al System of Systems 
Secure & Safe 
IT Security 
Impact on People & 
Property 
Al-Specific Safeguards 
Ethical Dilemma 
Handling 
Mitigations, Graceful 
Shutdown 
Scientifically & 
Technically 
Robust 
Sister Technologies 
& Uses: IOT, "Smart," 
"Skunkworks" 
Robust to Data or 
Model Attacks 
Scientific Review 
Process 
Verification & 
Validation 
General Scientific 
Method 
Data Quality & 
Provenance 
Monitor & Mitigate 
Misuse
NASA Responsible AI Components

Calls to Action

Let me know below if you have found other useful resources.

Ready to take the next step? Your first assignment is to read up on trusted AI ethics frameworks. Your second is to bring it up in your next team meeting. Every action counts when it comes to building AI that’s not just smart, but responsible.

One response

  1. […] This week, I had the privilege of presenting at the Global Data Summit in Reykjavik — a city known for its natural wonders, but also a strong tech industry. My session, Updating Data Programs with Responsible and Ethical AI focused on the intersection of data governance and artificial intelligence, and how organizations can prepare their data governance and data management programs for the ethical challenges and opportunities AI presents. This session was an excellent opportunity to discuss AI Ethics in a global context. This was my follow-up talk after presenting Responsible and Ethical AI Frameworks: An Introduction. […]

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