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Algorithmic Biases in Machine Learning

As emerging technologies continue to be implemented in society, algorithms are becoming more and more prominent in our daily lives. These algorithms have the power to make decisions based on machine learning, and its reliance on data gives these machines the potential to implement biases in its decision making processes. This project questions how these systems should be designed so that they are ethically implemented in the world, and highlights the concept of fairness in terms of who develops these technologies.

Heena Chudasama

Product Design

Information Design

The implementation of artificial intelligence in society may involve unintended consequences that may be undesirable and dangerous to the public. Algorithmic biases are errors based on data that create unfair outcomes that favour a certain group of users over others. These biases include, but are not limited to gender, race, sex, education and skill level.

This project explores the ins and outs of artificial intelligence technologies, including modern day applications, the potential for harm, ethical frameworks, as well as resources and toolkits that are beneficial to the designers and developers of these technologies.