Building AI Products Responsibly

1 certified

Responsible AI is not a compliance checkbox. It's a set of design decisions baked into your product from day one. This course teaches concrete patterns: impact assessment, transparency that builds trust, bias detection that works, and the engineering practices that make all of it sustainable.

01
Start here: what this course is about
Welcome to Building AI Products Responsibly
Read first. ~3 minutes.
02
Start here: principles from Google AI
AI Principles - Google AI↗
Google's published AI principles. The foundational commitments behind their products. Read this first.
03
Microsoft's responsible AI framework
Responsible AI Principles and Approach - Microsoft↗
Microsoft's principles: fairness, reliability, privacy, inclusiveness, transparency, accountability. The operating principles.
04
Anthropic's Responsible Scaling Policy
Anthropic's Responsible Scaling Policy↗
How Anthropic gates model capabilities behind safety thresholds. The most concrete responsible-deployment framework in industry.
05
Impact assessment: the EIA tool
UNESCO Ethical Impact Assessment for AI↗
UNESCO's framework for assessing AI products against ethical principles. Use it before launch.
06
Choosing an ethics framework
A Review of Ethical AI Frameworks in Product Development↗
Systematic academic review of 62 frameworks. Which fits your product? Trade-offs explained.
07
Building user trust in AI products
How to Build AI Products That Users Trust↗
Mind the Product's practical guide. Concrete trust-building patterns from PMs shipping AI products.
08
Transparency in AI development
A Framework for AI Development Transparency - Anthropic↗
Anthropic on what transparency in AI means in practice. Beyond marketing copy: real disclosure requirements.
09
Detecting and mitigating bias
Mitigating Bias in AI Algorithms (academic-leaning guide)↗
Survey of bias detection and mitigation techniques. Balanced datasets, adversarial methods, fairness metrics.
10
Engineering patterns for responsible ML
Responsible Design Patterns for Machine Learning Pipelines↗
Engineering-level patterns: data lineage, monitoring, retraining, kill switches. The implementation layer.
11
State of the art: minimum viable ethics
Minimum Viable Ethics: From Institutionalizing AI Governance to Product Impact↗
What is the smallest ethics framework that still works? Practical guidance for fast-moving teams shipping AI.
12
Closing: what to take with you
Closing: Responsible Is Not Slower
Wrap-up. ~3 minutes.