Artificial Intelligence (AI) and Machine Learning (ML) systems are
increasingly being used across all sectors and societies. Alongside this
growth, model fairness has been gaining awareness over the past years.
This field aims to assess how fair the model is when treating
pre-existing biases in data. Fairness in machine learning is an exciting
and vibrant area of research and discussion among academics,
practitioners, and the broader public. The goal is to understand and
prevent unjust or prejudicial treatment of people related to race,
income, sexual orientation, religion, gender, and other characteristics
historically associated with discrimination and marginalization, when
and where they manifest in algorithmic systems or algorithmically aided
decision-making. AI systems are enabling new experiences and abilities
for people around the globe. Beyond recommending books and television
shows, AI systems can be used for more critical tasks, such as
predicting the presence and severity of a medical condition, matching
people to jobs and partners, or identifying if a person is crossing the
street. Such computerized assistive or decision-making systems have the
potential to be fairer and more inclusive at a broader scale than
decision-making processes based on ad hoc rules or human judgments. The
risk is that any unfairness in such systems can also have a wide-scale
impact. Thus, as the impact of AI increases across sectors and
societies, it is critical to work towards systems that are fair and
inclusive for all.

Simi Manchanda
https://www.linkedin.com/in/sweetee-simi-m-0189861b/

The Data Standard
https://datastandard.io/
https://www.linkedin.com/company/the-data-standard/
https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q