DeepAI
Log In Sign Up

Bias, Fairness, and Accountability with AI and ML Algorithms

05/13/2021
by   Nengfeng Zhou, et al.
7

The advent of AI and ML algorithms has led to opportunities as well as challenges. In this paper, we provide an overview of bias and fairness issues that arise with the use of ML algorithms. We describe the types and sources of data bias, and discuss the nature of algorithmic unfairness. This is followed by a review of fairness metrics in the literature, discussion of their limitations, and a description of de-biasing (or mitigation) techniques in the model life cycle.

READ FULL TEXT

page 3

page 7

page 11

12/10/2021

A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions

In a world of daily emerging scientific inquisition and discovery, the p...
02/15/2022

Fairness Amidst Non-IID Graph Data: A Literature Review

Fairness in machine learning (ML), the process to understand and correct...
02/03/2021

BeFair: Addressing Fairness in the Banking Sector

Algorithmic bias mitigation has been one of the most difficult conundrum...
12/21/2022

A Seven-Layer Model for Standardising AI Fairness Assessment

Problem statement: Standardisation of AI fairness rules and benchmarks i...
07/22/2022

Algorithmic Fairness in Business Analytics: Directions for Research and Practice

The extensive adoption of business analytics (BA) has brought financial ...
10/24/2022

Simultaneous Improvement of ML Model Fairness and Performance by Identifying Bias in Data

Machine learning models built on datasets containing discriminative inst...
05/15/2020

Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics

Recent research on algorithmic fairness has highlighted that the problem...