When businesses fail to develop a strong awareness about biases in AI it can land them in serious trouble. Biases can have a negative effect on society as well as on individual well-being, they can reveal weaknesses in design, and be counterproductive to the goal the AI was initially designed to achieve. And they can be as detrimental for the businesses. Taking biases seriously means actively incorporating ethics into your business and taking proactive steps to address the problem. This webinar is a first step in that direction.
In this webinar, we will explore algorithmic bias, starting from its definition and consequences all the way to its solutions. What do we mean when we talk about “biases in AI”? Why is it important to reduce and eliminate them both for our societal and individual well-being and for the success of the technology and the business? We will define the categories of algorithmic bias illustrating them with use-cases.
It is crucial to understand the core problems around algorithmic bias, but how do we move on to solving them? We will examine the stages of innovation and the bias-related questions we should ask at each stage. Often answering these questions require two types of work to be done: technical and ethical. We will point to these solutions and explain our model for effective and successful problem solving through integrating ethics analysis into the innovation
Speakers:
Cansu Canca, Ph.D. — Founder & Director of AI Ethics Lab
Laura Haaber Ihle, Ph.D. — Associate at AI Ethics Lab & Harvard University
Moderator:
Julia Zacharias — VP Delivery & Customer Success at Applause
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