Monday, May 7, 2018
Combatting Bias in Artificial Intelligence: Fairness and Equity
Harini Suresh (MIT Data Drive Inference Group) & Natalie Lao (CSAIL)
Dinner provided for attendees!
AI and algorithmic-based technologies are rapidly growing and becoming commonplace. They have the potential to bring incredible gains in efficiency, while providing the capabilities to perform a range of complex functions. However, a emerging issue has been the presence of bias and lack of fairness in AI and machine learning. As one article, puts it: Biases such as “confirmation bias” (when a person accepts a result because it confirms a previous belief) or “availability bias” (placing greater emphasis on information relevant to the individual than equally valuable information of less familiarity) can render the interpretation of machine learning data pointless. When these types of human mistakes become baked-in parts of an AI — meaning our bias is responsible for the selection of a training rule that shapes the creation of a machine learning model– we’re not creating artificial intelligence: we’re just obfuscating our own flawed observations inside of a black box."
Come discuss this topic and bring questions ! We're at the cusp of a technological shift and ensuring that fairness, justice, and equity are embedded within is crucial to ensure inclusion of all.
Here are several articles that may be of interest:
Study finds gender and skin-type bias in commercial artificial-intelligence systems - link
Ethical Implications Of Bias In Machine Learning - link
Forget Killer Robots—Bias Is the Real AI Danger - link
We look forward to an engaging discussion !