If you’re a user experience or a product designer, you will be very familiar with the concept of human centred design —a problem solving approach that involves people in every step of the process. It is woven into everything we do as designers and we often champion it in our lines of work. However, the design landscape is always changing and some could say even faster than ever, especially with emerging technologies like machine learning and AI.

Over the last couple of years I had the opportunity to work as a product designer exclusively on machine learning driven features and products. Over time I noticed that there are multiple significant considerations and tradeoffs that need to be taken into account. Not being aware of these considerations leaves a lot of room to damage the experience of your products and sometimes even the overall perception of the brand.

That’s why I decided to capture what I learned in an article series — ‘Machine Centred Design’, where I will be covering core skills and best practices when designing machine learning driven features and products. The article series was written with user experience and product designers in mind, but it’s a useful resource for anyone working in product or tech.

It’s worth noting that this space is relatively new and I’m still learning, so there might be some things that I have not captured. Also, there are multiple companies that have dedicated teams researching this, so I will be linking out to their resources where appropriate. Lastly, no prerequisite knowledge about machine learning is needed, but having a general understanding would help to understand a few concepts.

The series will consist of three parts:

Part 1 — Will give an overview of core skills needed when designing machine learning driven products and features. It will cover understanding machine learning, working with data scientists and translating algorithm outputs to create experiences for users.

Part 2 — Will dive deeper into collaboration with data science and where I will explain why it matters, how it differs from other stakeholders and expand further on the actual process.

Part 3 — The final part of the series will dive deeper into understanding algorithm outputs and translating them to create experiences for users. Covering concepts like explicit vs. implicit data capture, precision vs. recall and many more.

I’m excited to be sharing this article series with you and explore the intersect between user experience and AI. Follow me for updates and make sure to like the article if you found it interesting. The next part in the series is coming out very shortly.