Algorithmic Ethics Editorial Team
Researching ethical AI frameworks and bias mitigation strategies for Vancouver tech teams
We research, test, and document practical approaches to responsible AI development. No hype. Just honest guidance.
Why We Do This
We're here because responsible AI development doesn't have to be complicated. Most teams we talk to want to do the right thing—they're just not sure where to start, or they're overwhelmed by frameworks that don't match their reality.
We started this site in 2023 because we noticed the same questions coming up over and over. How do you actually detect bias in your data? What does fairness mean for a recommendation system? How do you get stakeholders to care about ethics work when deadlines are tight? These aren't theoretical problems—they're what development teams face every week.
So we research these challenges. We look at technical documentation, case studies, and we talk with practitioners who've solved these problems. We test frameworks, check the details carefully, and write about what actually works. We're transparent about trade-offs. We update articles when new tools emerge. And we explain everything in plain language, not jargon.
How We Work
Our process focuses on accuracy, clarity, and real-world usefulness
Listen and Research
We start by understanding what teams are actually struggling with. We review technical documentation, case studies, and talk with developers, data scientists, and product leads who've tackled these problems in production.
Test and Validate
We don't just summarize what we read. We work through frameworks ourselves, test tools where we can, and verify that the strategies we're documenting actually work in practice. We check for gaps, limitations, and trade-offs.
Write Clearly
We write in plain language. No corporate jargon. No buzzwords. We explain the why behind each approach, who it's useful for, and what limitations to keep in mind. Every article includes concrete examples and actionable next steps.
Review and Update
We check every detail for accuracy. As new frameworks, tools, and best practices emerge, we revisit articles and keep them current. Our goal is for you to trust that what you're reading reflects the state of the field right now.
What We Cover
Topics we write about regularly
Detecting Bias in Data
How to identify bias in training data, what bias actually means in different contexts, and tools that help you spot problems early. We focus on practical detection methods that don't require a PhD in statistics.
Building Fair Systems
What fairness means for recommendation systems, classification models, and ranking algorithms. We document strategies for building systems that work well across different groups, and what trade-offs you might face.
Stakeholder Alignment
How to get buy-in for ethics work when time and resources are limited. We share approaches that have worked for teams trying to shift culture toward responsibility without grinding projects to a halt.
Monitoring and Drift
How to track fairness and bias after models go live. We document approaches for detecting when models start behaving unfairly in production, and how to respond when problems emerge.
Frameworks and Standards
We review ethical AI frameworks from organizations, governments, and researchers. We explain what they're useful for, who they're designed for, and how they compare. We're honest about what's mature and what's still evolving.
Tools and Techniques
Open-source tools, testing frameworks, and methods teams can use to implement ethical AI practices. We focus on tools that are actually usable in real projects, not research prototypes.
Our Editorial Approach
Practical Over Theoretical
We prioritize approaches that teams can actually implement. We acknowledge research and theory, but we focus on what works in real development environments with real constraints.
Honest About Limitations
No framework is perfect. No tool solves every problem. We tell you what works, what doesn't, and where the gaps are. We explain trade-offs so you can make informed decisions.
Clear Language Always
We avoid jargon and buzzwords. If a technical term is necessary, we explain it. Our goal is to make this information accessible to developers, data scientists, product teams, and leaders who don't have a background in ethics or AI research.
Regularly Updated
This field moves fast. New tools, frameworks, and practices emerge regularly. We revisit articles to keep them current and reflect what's happening now, not what was true a year ago.
Our Library
We've published guides and articles covering the most pressing ethical AI questions facing development teams
In-depth guides published
Total content pages available
Year we started this work
Read Our Work
Start with one of these guides
Detecting Bias in Your Training Data
A practical guide to identifying bias before it affects your models. We cover what to look for, tools that help, and questions to ask about your data.
Read articleBuilding Fair Recommendation Systems
What fairness means in recommendations, why it matters, and how to design systems that work well for different groups. Includes specific strategies and trade-offs.
Read articleGetting Stakeholder Buy-In for Ethics Work
How to make the case for ethical AI practices when resources are limited. Real approaches from teams that've successfully shifted culture toward responsibility.
Read articleMonitoring Models for Drift and Fairness Issues
How to track fairness after models go live and detect when they start behaving unfairly. We document approaches, metrics, and response strategies.
Read articleGet in Touch
Have a question about ethical AI, bias mitigation, or responsible development? We'd like to hear from you. Reach out with feedback, article ideas, or just to chat about the challenges you're facing.
Send us a message