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Found 7 papers, 1 papers with code
High Significant Fault Detection in Azure Core Workload Insights
Faults or Anomalies are observed in these time-series data owing to faults observed with respect to metric name, resources region, dimensions, and its dimension value associated with the data.
Counterfactual Multi-Token Fairness in Text Classification
We have curated a resource of sensitive tokens and their corresponding perturbation tokens, even extending the support beyond traditionally used sensitive attributes like Age, Gender, Race to Nationality, Disability, and Religion.
Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets
We attempt to re-look at the data quality issues in the context of building a machine learning pipeline and build a tool that can detect, explain and remediate issues in the data, and systematically and automatically capture all the changes applied to the data.
Priority-based Post-Processing Bias Mitigation for Individual and Group Fairness
Previous post-processing bias mitigation algorithms on both group and individual fairness don't work on regression models and datasets with multi-class numerical labels.
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
Automated Test Generation to Detect Individual Discrimination in AI Models
In this paper, we present an automated technique to generate test inputs, which is geared towards finding individual discrimination.
Video Analysis of "YouTube Funnies" to Aid the Study of Human Gait and Falls - Preliminary Results and Proof of Concept
The analysis explores: computing spatiotemporal parameters of gait in a video captured from an arbitrary viewpoint; the relationship between parameters of gait from the last few steps before the obstacle and falling vs. not falling; and the predictive capacity of a multivariate model in predicting a fall in the presence of an unexpected obstacle.
