  Most Fairness in AI research focuses on exposing biases in AI systems. A
broader lens on fairness reveals that AI can serve a greater aspiration:
rooting out societal inequities from their source. Specifically, we focus on
inequities in health information, and aim to reduce bias in that domain using
AI. The AI algorithms under the hood of search engines and social media, many
of which are based on recommender systems, have an outsized impact on the
quality of medical and health information online. Therefore, embedding bias
detection and reduction into these recommender systems serving up medical and
health content online could have an outsized positive impact on patient
outcomes and wellbeing.
  In this position paper, we offer the following contributions: (1) we propose
a novel framework of Fairness via AI, inspired by insights from medical
education, sociology and antiracism; (2) we define a new term, bisinformation,
which is related to, but distinct from, misinformation, and encourage
researchers to study it; (3) we propose using AI to study, detect and mitigate
biased, harmful, and/or false health information that disproportionately hurts
minority groups in society; and (4) we suggest several pillars and pose several
open problems in order to seed inquiry in this new space. While part (3) of
this work specifically focuses on the health domain, the fundamental computer
science advances and contributions stemming from research efforts in bias
reduction and Fairness via AI have broad implications in all areas of society.

Artificial Intelligence;Computers and Society;Information Retrieval;Machine Learning