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Inicio 4 June 2026 06:14
Fin 4 June 2026
Bias and Discrimination in AI
537 Cursos
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Resumen
Immerse yourself in an essential yet often overlooked discussion about Bias and Discrimination in AI with our comprehensive course offered through edX. This educational journey is guided by international experts who have previously spoken at IVADO’s International School on Bias and Discrimination in AI in Montreal.
The course delves into the multifaceted realms of social and technical issues surrounding bias, discrimination, and fairness within machine learning and algorithm creation.
Focusing on critical issues such as gender, race, and socioeconomic-based bias, as well as the biases present in data-driven predictive models that influence decisions, this course offers invaluable insights for a wide audience. While it is tailored for professionals and academics possessing a foundational understanding of mathematics and programming, the insightful content is also highly beneficial for anyone involved or interested in AI for diverse applications.
The discussion on these sociotechnical subjects has been a revelation for many technical professionals, highlighting the significance of being aware of and addressing bias in AI.
The course features a total of 7:
30 hours of video content, segmented for convenience, allowing learners to progress at their own pace. Each segment concludes with comprehensive quizzes to help consolidate your understanding of the material covered.
Presented by IVADO, a leading scientific and economic hub for data science known for its role in connecting industrial, academic, and governmental partners with expertise in digital intelligence, this course also emphasizes IVADO's mission to foster the development of digital knowledge and nurture a new generation of bias-aware data scientists.
Join us on edX for this enlightening exploration into the ethical dimensions of AI and become part of the movement towards more equitable technological advances.
Categories:
Artificial Intelligence Courses, Ethics Courses.
Impartido por
Rachel Thomas, Emre Kiciman and Golnoosh Farnadi
