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Found 7 papers, 3 papers with code
Quantifying Feature Contributions to Overall Disparity Using Information Theory
If we have access to the decision-making model, one potential approach (inspired from intervention-based approaches in explainability literature) is to vary each individual feature (while keeping the others fixed) and use the resulting change in disparity to quantify its contribution.
Can Information Flows Suggest Targets for Interventions in Neural Circuits?
Motivated by neuroscientific and clinical applications, we empirically examine whether observational measures of information flow can suggest interventions.
Analysis and Control of a Planar Quadrotor
We perform stability analysis of the open loop system and develop a PD controller for its position control.
Memory Guided Road Detection
In self driving car applications, there is a requirement to predict the location of the lane given an input RGB front facing image.
Fairness Under Feature Exemptions: Counterfactual and Observational Measures
While quantifying disparity is essential, sometimes the needs of an occupation may require the use of certain features that are critical in a way that any disparity that can be explained by them might need to be exempted.
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models
Overall, for large changes, $s \gg \sqrt{n}$, we need only $\mathrm{SNR}= O(1)$ whereas a na\"ive test based on community recovery with $O(s)$ errors requires $\mathrm{SNR}= \Theta(\log n)$.
Testing Changes in Communities for the Stochastic Block Model
Overall, for large changes, $s \gg \sqrt{n}$, we need only $\mathrm{SNR}= O(1)$ whereas a na\"ive test based on community recovery with $O(s)$ errors requires $\mathrm{SNR}= \Theta(\log n)$.
