… Recent scrutiny of artificial intelligence (AI)–based facial recognition software … bias and
inequity. Academic and government officials have raised concerns over racial and gender bias in …

… This paper reviews ongoing work to ensure data justice, fairness and bias mitigation in AI
systems … with bias. We also consider technical challenges in addressing issues of societal bias. …

… While many organizations seek to utilize this information in a responsible manner, biases
remain … , create significant challenges for cultivating public trust in artificial intelligence (AI). …

… it into approaches to machine learning from text would help prevent the … bias in artificial
intelligence are also primarily female, suggesting that those who are potentially affected by bias …

… Though artificial intelligence (AI) algorithms were initially proposed as a means to improve
… with bias and disparities. Therefore, we outline the various elements of potential bias in the …

… bias in AI [9]. Also known as algorithmic bias, it is what we experience when a machinelearning …
Just as this article is a reflection of the bias of its author, algorithms have authors and are …

… types of bias are negative, and there many ways to categorize or manage bias; this report 219
focuses on biases … These 220 harmful biases affect people’s lives in a variety of settings by …

Importance Clinical artificial intelligence (AI) algorithms have the potential to improve
clinical care, but fair, generalizable algorithms depend on the clinical data on which they are …

… most prominent ones that account for bias either proactively, via bias-aware data collection,
… the importance of describing and documenting bias by means of formalisms like ontologies. …

… of artificial intelligence, both nationally and internationally.The aim of this article is to describe
the phenomenon of bias in AI … For the purposes of this work, artificial intelligence will be …