The Impact of Differential Feature Under-reporting on Algorithmic Fairness in Predictive Risk Modeling

Core Concepts

Differential feature under-reporting, where administrative data records are more complete for individuals who have more greatly relied on public services, can introduce significant bias and unfairness in predictive risk models used in the public sector.

Abstract

The content examines the impact of differential feature under-reporting on algorithmic fairness in predictive risk modeling. Key insights:

Differential feature under-reporting occurs when administrative data records are more complete for individuals who have more greatly relied on public services, leading to biased data.
This form of data bias remains understudied from a technical viewpoint, unlike other types of feature mismeasurement like additive noise or missing indicators.
The authors introduce a statistical model of data collection with differential feature under-reporting and provide theoretical results characterizing its impact on disparities in selection rates across groups.
Standard missing data methods generally fail to mitigate unfairness in this setting. The authors propose new methods based on augmented loss estimation and optimal prediction imputation that are tailored to the under-reporting setting.
Empirical results on semi-synthetic and real-world data show that under-reporting typically exacerbates disparities, and the proposed solution methods can help mitigate this.
The authors also examine the impact of under-reporting on regression parameter estimates, demonstrating attenuation bias and shifting weight across features.

Stats

Differential feature under-reporting leads to attenuation bias in the parameter estimate for the mismeasured feature.
The magnitude of the parameter estimate for the mismeasured feature decreases as the under-reporting rate increases.
For fully observed features, the parameter estimates can increase or decrease depending on the signs of the true parameters and the covariances between features.

Quotes

"By relying on data that is only collected on families using public resources, the AFST unfairly targets low-income families for child welfare scrutiny."
"Problematically, we generally lack indicators on who is privately or publicly funded and for which services."

Key Insights Distilled From

The Impact of Differential Feature Under-reporting on Algorithmic Fairness

by Nil-Jana Akp... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2401.08788.pdf

Deeper Inquiries

How can we extend the proposed methods to handle cases where the under-reporting rates vary across multiple features or are unknown

To extend the proposed methods to handle cases where the under-reporting rates vary across multiple features or are unknown, we can implement a more sophisticated estimation procedure. One approach could involve incorporating a probabilistic model that accounts for varying under-reporting rates across features. This model could leverage techniques from Bayesian inference to estimate the under-reporting rates for each feature. By treating the under-reporting rates as latent variables, we can use techniques like Markov Chain Monte Carlo (MCMC) sampling to infer the most likely values for these rates based on the observed data.
Additionally, for cases where the under-reporting rates are unknown, we can adopt a data-driven approach to estimate these rates. This could involve using unsupervised learning techniques such as clustering or anomaly detection to identify patterns in the data that suggest under-reporting. By analyzing the distribution of feature values and their relationships with the target variable, we can infer the likelihood of under-reporting for each feature.
By incorporating these advanced estimation techniques, we can enhance the robustness and flexibility of the proposed methods to handle varying and unknown under-reporting rates across multiple features.

What are the implications of differential feature under-reporting in other machine learning applications beyond predictive risk modeling, such as recommender systems or computer vision

The implications of differential feature under-reporting extend beyond predictive risk modeling to various other machine learning applications. In recommender systems, under-reporting of user preferences or behaviors can lead to biased recommendations, impacting user experience and potentially reinforcing existing disparities. For example, if certain user groups are more likely to under-report their preferences, the recommendations provided to them may not accurately reflect their interests, leading to a lack of diversity in the content they are exposed to.
In computer vision applications, under-reporting of certain visual features or attributes can introduce biases in image classification or object detection tasks. For instance, if certain demographic groups are under-represented in the training data due to under-reporting, the model may struggle to accurately classify or detect objects related to those groups, leading to disparities in performance across different demographic categories.
Overall, differential feature under-reporting can introduce biases and unfairness in a wide range of machine learning applications, impacting the accuracy, equity, and reliability of the models deployed in these domains.

Could differential feature under-reporting be leveraged strategically to achieve more equitable outcomes, or is it inherently a source of unfairness that should be mitigated

While it may be tempting to consider leveraging differential feature under-reporting strategically to achieve more equitable outcomes, it is important to recognize that under-reporting is inherently a source of unfairness that should be mitigated rather than exploited.
Strategically leveraging under-reporting to manipulate outcomes would only serve to perpetuate existing biases and inequalities in the data, leading to unfair and potentially harmful decisions. By intentionally exploiting under-reporting, we would be perpetuating systemic injustices and reinforcing disparities in algorithmic decision-making processes.
Therefore, it is crucial to address and mitigate differential feature under-reporting through transparent and ethical data collection practices, robust modeling techniques, and fairness-aware algorithms. By actively working to reduce under-reporting and its impact on algorithmic fairness, we can strive towards more equitable and unbiased machine learning systems.