Bias can emerge from artificial intelligence / machine learning (AI/ML) from many reasons (e.g., AI/ML learns historical bias, AI/ML learns from incomplete datasets). As organizations across a variety of new industries and areas implement AI/ML, it poses an ethical challenge. Organizations need to balance the ethical concerns of AI/ML fairness and anti-bias with the business benefits of AI/ML such as increased accuracy and profits. This presentation discusses evidence of AI bias in multiple contexts, some of the challenges in identifying AI bias, and some of the legal and societal implications of AI bias. Furthermore, I would discuss how algorithms exist within a decision-making framework and can/cannot induce bias in people.