Bias and Discrimination in AI

• Understanding Bias: Acquire a thorough comprehension of bias and discrimination in a variety of contexts, such as algorithmic decision-making.

• Identifying Sources of Bias: Acquire the ability to identify the underlying causes of bias in machine learning models, including algorithmic errors and biased data.

• Bias Mitigation: Investigate effective strategies for bias mitigation, such as algorithmic fairness techniques, data cleansing, and responsible AI practices.

• The Development of Ethical Artificial Intelligence: Comprehend the ethical implications of AI and acquire the skills necessary to create and assess algorithms that are transparent, fair, and accountable.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 1

Offered by: On edX provided by UMontrealX

Duration: 4�6 hours per week approx 4 weeks

Schedule: Flexible