Bias and Discrimination in AI
This course delves deeply into AI bias, equipping students with the knowledge they need to design responsible and ethical AI systems.
machine learning,artificial intelligence,network bridging,economics,algorithms,predictive modeling,data science
Description for 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
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