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Found 9 papers, 4 papers with code
Towards Fair and Explainable AI using a Human-Centered AI Approach
The rise of machine learning (ML) is accompanied by several high-profile cases that have stressed the need for fairness, accountability, explainability and trust in ML systems.
D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias
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.
Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions
Lastly, we offer a list of combinations of interventions that perform best for different fairness and utility metrics to aid the design of fair ML pipelines.
Fluent: An AI Augmented Writing Tool for People who Stutter
On hovering over any such word, Fluent presents a set of alternative words which have similar meaning but are easier to speak.
WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings
In this work, we present WordBias, an interactive visual tool designed to explore biases against intersectional groups encoded in static word embeddings.
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation
The similarity score between feature rankings provided by the annotator and the local model explanation is used to assign a weight to each corresponding committee model.
Measuring Social Biases of Crowd Workers using Counterfactual Queries
Social biases based on gender, race, etc.
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience
We conducted an empirical study comparing the model learning outcomes, feedback content and experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without the explanation).
Does Speech enhancement of publicly available data help build robust Speech Recognition Systems?
Thereafter, we tried to investigate if we can use publicly available noisy data to train robust ASR systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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