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Responsible AI Algorithm Design [Released: 10/18/2024]
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 26m | 389 MB
Instructor: Isil Berkun
As AI technologies advance rapidly, embedding ethical practices into AI design practices is not just a necessity but an imperative to prevent biases and ensure trust in AI. This course equips you with the skills required to create systems that are ethically aligned, fair, and transparent, preventing biases and fostering trust in AI technology. Join instructor Isil Berkun as she shows you what it takes to master the ethical principles necessary to build and deploy AI systems responsibly. Along the way, discover critical topics such as bias mitigation, transparency, and privacy, culminating in hands-on challenges that translate ethical concepts into actionable AI development practices.
This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time-all while using a tool that you'll likely encounter in the workplace. Check out the "Using GitHub Codespaces with this course" video to learn how to get started.
Learning objectives
[list]
[*]Understand the fundamental importance of responsible AI in the development process.
[*]Mitigate the risks associated with AI deployment.
[*]Explore how AI governance and global policy shapes responsible AI practices.
[*]Discover best practices and strategies for effective AI policy implementation for AI projects.
[*]Build technical skills for designing AI systems alongside ethical considerations.
[*]Learn methods and design features for enhancing transparency and accountability of AI systems.
[*]Examine real-world examples of AI and apply the principles and methods for integrating human-centric design into AI development in a hands-on project.
[*]Integrate ethical considerations into AI development, ensuring fairness, transparency, and accountability, with a focus on theoretical frameworks and ethical guidelines rather than specific software tools.
[/list]
Exercise Files on GitHub
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