Good morning, my name is Brianna Richardson and I will be presenting on behalf of my team
at Spotify on our recent work in creating a practitioner-oriented rubric for creating
and evaluating fair machine learning toolkits.
The motivations for our work were three-fold: Firstly, There currently exist real and consequential
harms that emerge from the unintended biases that exists in our machine learning algorithms.
To combat these harms, there exists a diverse array of tools and resources that have been
made available to machine learning practitioners in the Fair AI research space.
Nonetheless, there is an evident lack of application of these resources in practice.
Our objective for this project was to identify the features that would bridge the gap and
make Fair AI tools work in practice. Therefore, we decided to build a rubric that could be
used as both a guide and an evaluation tool for the creation of Fair AI tooling. To build
this rubric, we took the recommendations of fairness experts in literature and practitioners
in the workplace.
In addition to a complete literature review on the solution space of fair AI, we wanted
to also collect feedback from machine learning practitioners. So we conducted a study with
20 participants, all of whom were individuals who were affiliated to machine learning projects
in some capacity. Our participant pool was mixed with both product and research oriented
practitioners from a diverse array of domains.
The study was done virtually using the video conferencing platform, Google Meet, and took
about an hour to complete.
After collecting demographic information, interviewers introduced participants to the
study task. Participants were told that they would be the project manager for a new product
that predicts whether or not an individual makes over 50k.
They were told that their team had produced three different models and that they would
be using a fairness toolkit to evaluate the three models.
Participants had the opportunity to decide their willingness to deploy each of the models
before and after viewing fairness analysis. Each participant was shown one of two fairness
toolkits: UChicago’s Aequitas or Google’s Fairness Indicators.
To start, participants were shown common performance metrics for each model in the form of confusion
matrices. Using this information alone, participants shared their willingness to deploy each of
these models. Next, participants were shown completed analyses
in Google Colab. The fairness portion of this analysis can be seen in the videos shown here.
On the left, you can see what the fairness analysis done in Aequitas and on the right,
you can see the fairness analysis done in Google’s Fairness Indicators. After spending
time with the analyses sections, participants were then asked again about their willingness
to deploy each model.
Our results were extensive, just to name a few: we found that the impact of fairness
tooling was clear: fairness toolkits were significantly influential on user’s willingness
to deploy the models.
Furthermore, there was a clear need for diverse resources to assist in the use of toolkits.
Practitioners stated a need for fairness collaborations and colleagues, access to source code for
fairness tools, company-led training courses, etc.
Our main contribution was a comprehensive rubric that lists the fairness and usability
needs as requested by practitioners when it comes to fairness toolkits. This rubric can
be used to identify critical components for any Fair ML toolkit.
Two of these rubric components can be seen here.
Firstly, many of the interviewed practitioners found the tools to be inapplicable to their
own machine learning algorithms. Either the task of classification or the use of tabulated
data were irrelevant for them. Since many tools are very limited in the predictive tasks
or the allowable data types, there is a dire need for diversification of these toolkits.
Another major complaint was the lack of guidance or recommendations for when and how outcomes
were unfair. Many practitioners voiced that the experience of deciphering unfairness was
exhaustive and requested as much support as possible within the toolkit for determining
where and how bias may sneak into their models.
To see more about these rubric components in more detail, please check out our paper
in the conference’s proceedings.
The results depict a dire need for improvement when it comes to the diverse problem sets
that can be handled by Fair ML, the usability of Fair ML, and the additional resources provided
to support the use of the fair ML. However, the responsibility for normalizing the use
of fair ML in practice does not solely lie in the hands of fairness experts. Institutions
should be active supporters of fair AI pursuits. They should provide resources for educating
practitioners and should define their own fairness expectations.
Next steps should focus on prototyping this improved fair ML.
Thanks for attending our talk. Please feel free to reach out to me at any point. I look
forwarding to hearing your questions and comments.