Tag Archives: Agathe

Geospatial Disparities: A Case Study on Real Estate Prices in Paris

Our paper, Geospatial Disparities: A Case Study on Real Estate Prices in Paris, and Agathe Fernandes Machado, François Hu, Philipp Ratz and Ewen Gallic, is now online on ArXiv,

Driven by an increasing prevalence of trackers, ever more IoT sensors, and the declining cost of computing power, geospatial information has come to play a pivotal role in contemporary predictive models. While enhancing prognostic performance, geospatial data also has the potential to perpetuate many historical socio-economic patterns, raising concerns about a resurgence of biases and exclusionary practices, with their disproportionate impacts on society. Addressing this, our paper emphasizes the crucial need to identify and rectify such biases and calibration errors in predictive models, particularly as algorithms become more intricate and less interpretable. The increasing granularity of geospatial information further introduces ethical concerns, as choosing different geographical scales may exacerbate disparities akin to redlining and exclusionary zoning. To address these issues, we propose a toolkit for identifying and mitigating biases arising from geospatial data. Extending classical fairness definitions, we incorporate an ordinal regression case with spatial attributes, deviating from the binary classification focus. This extension allows us to gauge disparities stemming from data aggregation levels and advocates for a less interfering correction approach. Illustrating our methodology using a Parisian real estate dataset, we showcase practical applications and scrutinize the implications of choosing geographical aggregation levels for fairness and calibration measures.

Insurance Data Science, IDSC’24 (in Stockholm)

This year, the Insurance Data Science Conference will take place in Stockholm. The abstract booklet with all accepted speakers at IDSC 2024 is now available. Agathe will present some recent joint work on calibration (inspired by arXiv:2402.07790 – as well as more recent work)

I will also attend, to present some work on fairness, with multiple sensitive attributes (based on arXiv.2309.06627 and another article that should be finalized by then about our equipy python package),

WIM (Workshop in Insurance Mathematics) is back

After four year without it, the WIM is back. Last time, it was in February 2020. Tomorrow, Agathe and Olivier will present posters at the Workshop in Insurance Mathematics, a Concordia University.

Both posters will be on fairness and discrimination. Olivier Côté will present a poster on “Fairness in insurance enigma: exploring the maze of regulation

Agathe will present a poster on Equipy, “A Python Package for Sequential Fairness using Optimal Transport with Applications in Insurance

From Uncertainty to Precision: Enhancing Binary Classifier Performance through Calibration

Our paper From Uncertainty to Precision: Enhancing Binary Classifier Performance through Calibration, written with Agathe Fernandes Machadoa, Emmanuel Flachaire, Ewen Gallic and François Hu is now online on ArXiv,

The assessment of binary classifier performance traditionally centers on discriminative ability using metrics, such as accuracy. However, these metrics often disregard the model’s inherent uncertainty, especially when dealing with sensitive decision-making domains, such as finance or healthcare. Given that model-predicted scores are commonly seen as event probabilities, calibration is crucial for accurate interpretation. In our study, we analyze the sensitivity of various calibration measures to score distortions and introduce a refined metric, the Local Calibration Score. Comparing recalibration methods, we advocate for local regressions, emphasizing their dual role as effective recalibration tools and facilitators of smoother visualizations. We apply these findings in a real-world scenario using Random Forest classifier and regressor to predict credit default while simultaneously measuring calibration during performance optimization.

The whole is greater than the sum of the parts

Good news: ou paper, A Sequentially Fair Mechanism for Multiple Sensitive Attributes, written with Philipp Ratz and François Hu will be presented in February in Vancouver, at the 38th Annual AAAI Conference on Artificial Intelligence. For a shorter version, there was a review on montrealethics.ai of the paper last week (as mentioned previously).

Also, last week also, the team launched the  equipy python package, with codes used in the paper,

pip install equipy

EquiPy is a Python package implementing sequential fairness on the predicted outputs of Machine Learning models, when dealing with multiple sensitive attributes. This post-processing method progressively achieve fairness accross a set of sensitive features by leveraging multi-marginal Wasserstein barycenters, which extends the standard notion of Strong Demographic Parity to the case with multiple sensitive characteristics. This approach seamlessly extends to approximate fairness, enveloping a framework accommodating the trade-off between performance and unfairness.

(from the left to the right, Agathe, who just joint the PhD program, Suzie, MSc student at ENSAE, with us since May or June, Philipp, PhD student, François, postdoctoral fellow – and Dante, also postdoctoral fellow, in stochastic processes). According to Aristotle (or probably slightly misquoted),

the whole is greater than the sum of the parts

I couldn’t agree more !