DeepAI AI Chat
Log In Sign Up

Harnessing value from data science in business: ensuring explainability and fairness of solutions

by   Krzysztof Chomiak, et al.

The paper introduces concepts of fairness and explainability (XAI) in artificial intelligence, oriented to solve a sophisticated business problems. For fairness, the authors discuss the bias-inducing specifics, as well as relevant mitigation methods, concluding with a set of recipes for introducing fairness in data-driven organizations. Additionally, for XAI, the authors audit specific algorithms paired with demonstrational business use-cases, discuss a plethora of techniques of explanations quality quantification and provide an overview of future research avenues.


page 28

page 34

page 41


Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models

Motivations for methods in explainable artificial intelligence (XAI) oft...

Fairness and Robustness of Contrasting Explanations

Fairness and explainability are two important and closely related requir...

Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning

There has recently been a surge of work in explanatory artificial intell...

Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations

While machine learning models have achieved unprecedented success in rea...

Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions

Ensuring trustworthiness in machine learning (ML) models is a multi-dime...

Algorithmic Fairness in Business Analytics: Directions for Research and Practice

The extensive adoption of business analytics (BA) has brought financial ...

An Approach to Ensure Fairness in News Articles

Recommender systems, information retrieval, and other information access...