Human bias is a significant challenge for almost all decision-making models. Over the past decade, data scientists have adamantly argued that AI is the optimal solution to problems caused by human bias. Unfortunately, as machine learning platforms became more widespread, that outlook proved to be outlandishly optimistic.
The viability of any artificial intelligence solution is based on the quality of its inputs. Data scientists have discovered that machine learning solutions are subject to their own biases, which can compromise the integrity of their data and outputs. How can these biases influence AI models and what measures can data scientists take to prevent them?
Author: Ryan Kh