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Found 4 papers, 0 papers with code
Detecting Bias in the Presence of Spatial Autocorrelation
In spite of considerable practical importance, current algorithmic fairness literature lacks technical methods to account for underlying geographic dependency while evaluating or mitigating bias issues for spatial data.
Bias Detection
Fairness
Applications
Computers and Society
SOAR: Simultaneous Or of And Rules for Classification of Positive & Negative Classes
Wang et al. (2017) presented an or-of-and (disjunctive normal form) based classification technique that allows for classification rule mining of a single class in a binary classification; this method is also shown to perform comparably to other modern algorithms.
Towards Integrating Fairness Transparently in Industrial Applications
Numerous Machine Learning (ML) bias-related failures in recent years have led to scrutiny of how companies incorporate aspects of transparency and accountability in their ML lifecycles.
That's sick dude!: Automatic identification of word sense change across different timescales
In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books.
