Last month Giovanni Astante (who worked on the topic during his research stay at NERDS) presented our work (w. Roberta Sinatra) on “Investigating the Robustness of a Danish Voting Advice Algorithm” (VAA) at the European Conference on Algorithmic Fairness.

Now the paper is finally out in the Proceedings of Machine Learning Research!
You can find a copy here –> https://proceedings.mlr.press/v350/astante26a.html

We look at a popular VAA (called ‘Kandidattesten’), developed by the large media house Politiken, and investigated the robustness of its matching algorithm.

In past Danish elections 62% of eligible voters used some form of voting assistance algorithms (‘Valgtest’ in danish) one month prior to the election and 45% reported they followed the recommendation of a VAA when voting. This means these voting tools can have a large influence on election results.

Through a freedom of information request we got access to the algorithm used to match users and political candidates. The system functions by users filling out a questionnaire which is compared to a database of answers from candidates. Users are then presented candidates with highest agreement.

These systems have been studied in the past (mostly how humans interact with them), but so far nobody has tested their robustness. We study how minor modifications to the algorithm’s weights can change the outcome of which political candidates are recommended. In addition, we study how dropping questions, i.e. calculating match similarity based on fewer questions (than the original 20), affects which candidates are recommended.

Our results demonstrate that even minor tweaks to the algorithm can produce entirely different outcomes, potentially affecting outcomes of political elections. A marginally changed matching algorithm can recommend entirely different political candidates, from different political parties.

Based on our findings we provide recommendations to VAA developers and policy makers, on how to monitor and evaluate the robustness of these systems.

Do you work with human mobility data?
Then this publication could be important for you to read, and to reflect on!

A tale of ten cities: data bias in human mobility is pervasive and highly location specific by K. den Nijs, E. Omodei, & V. Sekara published in EPJ Data Science.

In this joint work w. Katinka den Nijs and Elisa Omodei we study ‘data production’ biases in human mobility data. Data production bias is a form of bias which deals not only with whether individuals are represented in datasets, but also how they are represented. Especially in terms of how much data they produce. Put differently, we don’t only care about if people are represented in data, we also care about how they are represented.

We study a GPS mobility dataset, collected from anonymized smartphones for ten major US cities, and find that data points are as, or more, unequally distributed than wealth. Imagine!
Their data production is more unequally distributed than wealth in society!

To understand the interplay between demographic factors and data production bias we build machine learning models to predict the number of data points we can expect to be produced by the composition of demographic groups living in census tracts.  We build one model per city and, generally, find strong effects of wealth, ethnicity, and education on data production.

While we find that bias is a ubiquitous phenomenon, occurring in all ten cities, we also find that each city suffers from its own manifestation of it, and that location-specific models are required to model bias for each city. Our work raises serious questions about general approaches to debiasing human mobility data and highlights the urgent need for further research on the topic.

In summary, be careful with biases in human mobility data (or any other digitally collected datasets). Data production biases can have significant downstream effects, when data is used to model everything from epidemics, behavior, etc.

TLDR; Representativeness does not mean we should only strive for individuals to merely be ‘present’ in data. As our results show, how people are represented, or misrepresented, via the quantity and quality of data the produce is of equal importance. Further, ‘debiasing’ data is no easy task, as it heavily depends on geographical context.