Machine Learning and Artificial Intelligence: Ethics & Fairness

Abstract

Machine learning (ML), a subfield of Artificial Intelligence (AI), is a field of computer science concerned with programs that learn. It makes use of historical data, comprised of inputs and outputs, together with mathematical functions, in order to skillfully predict outputs given new and unseen inputs in the future.

ML/AI impacts everything from Social Media to Agriculture to Healthcare to Education. While ML/AI system has the potential to improve lives, it can also be a source of harm. ML applications have discriminated against individuals on the basis of race, sex, religion, socioeconomic status, and other categories.

This discrimination results from data bias or end-user interpretation of model final re- sults. Data bias can occur in a range of areas, from human reporting and selection bias to algorithmic and interpretation bias.

By applying an ethical lens, we can work toward identifying and mitigating the harms that these technologies can cause to people

This presentation is aimed at highlighting these biases and ways to mitigate.