Our Reading List about Machine Learning for Designers

Fabien Girardin News&References

At BBVA Data & Analytics, we continuously collaborate with design teams at BBVA to create user experiences that rely on machine learning techniques (e.g. predictive models, recommender systems). We documented that interdisciplinary practice in Experience Design in the Machine Learning Era that then led to a contribution in collaboration with Neal Lathia from Skyscanner at the AAAI 2017 Spring Symposia on Designing the User Experience of Machine Learning Systems. Our technical report entitled When User Experience Designers Partner with Data Scientists focuses both on creating experiences with learning algorithms and describes how the methods that designers and data scientists employ challenge their collaborations.

As the interest on the topic has grown internally at BBVA, I am part of a small group of data scientist and designers have joined forces over the Summer to setup a crash course on Machine Learning for Designers. The material is based on our common experiences mixed with the literature that recently emerged. Here is a brief reading list — as of September 2017 — of texts that inspire us:

Machine Learning for Designers by Patrick Hebron

A seminal ebook with technical descriptions how machine-learning techniques (e.g. natural language processing, image recognition, content personalization, and behavior prediction) can affect the design of application.

Experience Design in the Machine Learning Era by Fabien Girardin

Our hands-on experience and vision at BBVA Data & Analytics of creating experiences with learning algorithms.

When User Experience Designers Partner with Data Scientists by Fabien Girardin and Neal Lathia

A paper that presents a series of touch points and principles that partnerships between designers and data scientists can consider for productive relationships.

Machine Learning for Product Managers by Neal Lathia

A product-centric overview of machine learning with a particularly helpful list of technical terms for what products are trying to do: ranking, recommendation, classification, regression, clustering, anomaly detection.

The Step-By-Step PM Guide to Building Machine Learning Based Products by Yael Gavish

A guide on Machine Learning for product managers that particularly contains a chapter entitled  Machine Learning is Very Much a UX Problem.

Human-Centered Machine Learning By Josh Lovejoy and Jess Holbrook

Instead of viewing Machine Learning purely as a technology, what if we imagine it as a material to design with? The answer with a list of 7 steps to stay focused on the user when designing with Machine Learning.

Applications Of Machine Learning For Designers by Lassi Liikkanen

This article illustrates the power of machine learning through the applications of detection, prediction and generation. It gives six reasons why machine learning makes products and services better and introduces four design patterns relevant to such applications.

Data Jujitsu by DJ Patil

A seminal ebook with — among other things — design tricks that enlist the help of your users to take short cuts around tough problems.

The Most Crucial Design Job of the Future by Caroline Sinders

Data ethnographer? A potential future practice at the crossroad of design and data science to determine bias in algorithms and help reveal their ingredients so people can interpret the results they produce.

UX Design Innovation: Challenges for Working with Machine Learning as a Design Material by Graham Dove et al.

A paper that concludes on areas where new research and new curriculum might help our community unlock the power of design thinking to re-imagine what ML might be and might do.

What Machine Learning Will Do For Design by Eve Weinberg

A look on how machine learning became a design tool.

Rethinking Design Tools in the Age of Machine Learning by Patrick Hebron

An in-depth overview how machine learning can help simplify design tools without limiting their expressivity, without taking creative control away from the designer.

If you want to dig deeper into academic works in the domain, have a look at the proceedings of the AAAI 2017 Spring Symposia Designing the User Experience of Machine Learning Systems. For design students, we can suggest Kars Alfrink’s Design and machine learning reading list.