Hello! Thank you for your interest in our paper:
Effect of Information Presentation on Fairness Perceptions of Machine Learning Predictors.
This is a collaborative project between Aalborg University,
the University of Melbourne, and the University of Oulu. My name is Niels van Berkel.
The uptake of artificial intelligence has raised concerns regarding fairness and
transparency. Furthermore, the HCI community has called for more end-user involvement in
AI development, as opposed to solely relying on developer input to determine what is fair.
However, it is quite clear that existing tools, such as Google’s 'What-If Tool',
are aimed at AI experts with extensive technical knowledge. Given the growing
interest of non-expert involvement, we set out to study the effect of information presentation
on fairness perceptions. We evaluate the effect of two common visualisation techniques:
Text-based visualisation. And scatterplots.
A second between-factor in our study is the display of outcome variables - also
known as ground truth - which we either include or exclude.
This adds up to a total of four conditions. Shown here.
We evaluated these conditions across two within-subject factors.
First, our study contained real world data from two scenarios:
a lending dataset and a recidivism dataset. Second, we selected three types of predictors:
Demographic, such as age or race. Domain-specific, such as the size of a loan
And finally, a Validation type, in which we ourselves created artificial variables,
such as the handedness, to assess and validate participant input.
We ran an online study with 80 participants. Following
the introduction of some background information, participants assessed a total of ten predictors,
indicating whether they think it should be included in the given scenario,
by indicating a yes or a no, and how fair this would be, as ranging from 0 to 100.
We also collected their basic demographic information.
Now on to some of the study’s results. We constructed a binomial
linear mixed effects model, able to explain 54% of the variance in prediction assessment
without relying on participant demographics or any other typically unknown data point.
Main effects were found for: Predictor type,
highlighting that participants were able to successfully exclude the validation variables.
For Visualisation technique, with text-based visualisation leading to more included predictors.
And Scenario, with recidivism leading to more included predictors than lending.
Please also see the paper for details on the interaction effects.
We also found that participants’ education and sex had a significant impact on
perceived fairness. The higher the educational attainment, the lower the perceived fairness.
We further find that women rated the presented predictors as significantly less fair than men.
Our study aims to understand the effect of data presentation on people’s fairness perceptions.
A better understanding of the public’s perception of fairness
is critical in supporting an informed debate and to provide a voice to non-machine learning experts
in the design of future AI applications.
Despite the underlying data being identical,
the text-based visualisation resulted in an increased likelihood of predictors being included.
Based on prior work, we hypothesise that the visual information in the scatterplot
more clearly communicated the differences that existed between groups.
We highlight five recommendations for future work.
First, to limit information complexity and to make use of graphics over text.
Second, to tailor information presentation to the target audience
and their technical level of understanding. Third, to validate results across multiple
scenarios or datasets - especially in the case of a methodological contribution such as ours.
Fourth, to include verifiable predictors to assess participant understanding.
And finally, to instruct participants with task-relevant information to ensure
successful task completion.
Again, I really want to thank you for your attention.
We publicly release our application’s source code for future use.
And do feel free to reach out for any questions or comments that you may have.