Learn how artificial intelligence impacts our human rights and what can be done to enhance the ethical development and application of algorithms and machine learning.
The United Nations have multiple times reiterated that human rights apply online and offline alike. Events that happen online can severely impact our lives offline. With the rapid advancement of technology, human rights professionals need to understand and participate in shaping the tools and technology that impact our daily lives. Artificial intelligence has multiple and severe implications for human rights: Predictive policing, discriminatory algorithms, hate speech and freedom of expression on social media are just a few examples where artificial intelligence plays a significant role.
The University of Montreal is now offering a course on Bias and Discrimination in Artificial Intelligence that is open to everyone with internet access. Technical human rights professionals will find the course exceptionally useful but everyone who is using a computer for human rights work from social media managers to human rights recruiters will benefit from this course.
In the introductory part of the course, you will learn the basic terminology of fairness, bias, discrimination as well as machine learning and artificial intelligence. You will explore the negative impact machine learning and discriminatory algorithmic decision making can have on our lives and what mitigation strategies exist. Beyond providing a basic understanding of the subject matter, the course examines specific examples where artificial intelligence/machine learning has impacted the right to association, religion, and expression, as well as freedom of movement and the right to life, liberty and security of a person.
The intermediate level course takes roughly four weeks to complete. After finishing the course, you will be able to explain how bias and discrimination manifests through artificial intelligence, how human rights are impacted by discriminatory AI, what strategies exist to mitigate bias in machine learning and what can be done to enhance the ethical development and evaluation of algorithms.
