Course ID: AIBE
Artificial Intelligence, Bias, and Ethics
Machines that can learn, can (and almost always do) learn to be biased. What are the ethical, social, and business ramifications of this? Participants will gain an accurate (but non-math, non-engineering) understanding of how bias seeps into AI models, even if the computer algorithms are coded in a non-biased way. They will know what transparency is ( the ability to show why a computer made certain decisions and recommendations) and how transparency may be achieved (in some cases). The will know the best ethical practices for managing bias in business uses of AI.
Hour 1 – By the end of this hour participants will be able to articulate the ways that bias gets into AI models.
Hour 2 – By the end of this hour, participants will be able to say what transparency means in the context of AI, articulate the limits of ethics panels in AI, and make a case for different approaches to implementing ethical principles.
Hour 1: Where Does Bias Come From?
Hour 2: Transparency, Ethics, and Moving Beyond Ethics Committees
Who Should Attend
Anyone interested in topics regarding AI and Ethics.
Fields of StudyBehavioral Ethics