AI fairness is tasked with evaluating and mitigating bias in algorithms that may discriminate towards protected groups. This paper examines if bias exists in AI algorithms used in disaster management and in what manner. We consider the 2017 Hurricane Harvey when flood victims in Houston resorted to social media to request for rescue. We evaluate a Random Forest regression model trained to predict Twitter rescue request rates from social-environmental data using three fairness criteria (independence, separation, and sufficiency). The Social Vulnerability Index (SVI), its four sub-indices, and four variables representing digital divide were considered sensitive attributes. The Random Forest regression model extracted seven significant predictors of rescue request rates, and from high to low importance they were percent of renter occupied housing units, percent of roads in flood zone, percent of flood zone area, percent of wetland cover, percent of herbaceous, forested and shrub cover, mean elevation, and percent of households with no computer or device. Partial Dependence plots of rescue request rates against each of the seven predictors show the non-linear nature of their relationships. Results of the fairness evaluation of the Random Forest model using the three criteria show no obvious biases for the nine sensitive attributes, except that a minor imperfect sufficiency was found with the SVI Housing and Transportation sub-index. Future AI modeling in disaster research could apply the same methodology used in this paper to evaluate fairness and help reduce unfair resource allocation and other social and geographical disparities.
D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias
With the rise of AI, algorithms have become better at learning underlying patterns from the training data including ingrained social biases based on gender, race, etc. Deployment of such algorithms to domains such as hiring, healthcare, law enforcement, etc. has raised serious concerns about fairness, accountability, trust and interpretability in machine learning algorithms. To alleviate this problem, we propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases from tabular datasets. It uses a graphical causal model to represent causal relationships among different features in the dataset and as a medium to inject domain knowledge. A user can detect the presence of bias against a group, say females, or a subgroup, say black females, by identifying unfair causal relationships in the causal network and using an array of fairness metrics. Thereafter, the user can mitigate bias by refining the causal model and acting on the unfair causal edges. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset based on the current causal model while ensuring a minimal change from the original dataset. Users can visually assess the impact of their interactions on different fairness metrics, utility metrics, data distortion, and the underlying data distribution. Once satisfied, they can download the debiased dataset and use it for any downstream application for fairer predictions. We evaluate D-BIAS by conducting experiments on 3 datasets and also a formal user study. We found that D-BIAS helps reduce bias significantly compared to the baseline debiasing approach across different fairness metrics while incurring little data distortion and a small loss in utility. Moreover, our human-in-the-loop based approach significantly outperforms an automated approach on trust, interpretability and accountability.
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- Award ID(s):
- 1900706
- NSF-PAR ID:
- 10430780
- Date Published:
- Journal Name:
- IEEE Transactions on Visualization and Computer Graphics
- ISSN:
- 1077-2626
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TVCG.2022.3209484
