This third and final course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the critical human factors in developing AI-based products. The course begins with an introduction to human-centered design and the unique elements of user experience design for AI products. Participants will then learn about the role of data privacy in AI systems, the challenges of designing ethical AI, and approaches to identify sources of bias and mitigate fairness issues. The course concludes with a comparison of human intelligence and artificial intelligence, and a discussion of the ways that AI can be used to both automate as well as assist human decision-making.
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Programa de cursos integrados AI Product ManagementUniversidade DukeInformações sobre o curso
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Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 3 no
Nível iniciante
No prior experience in AI or programming required
Aprox. 17 horas para completar
Inglês
Legendas: Inglês
O que você vai aprender
Identify and mitigate privacy and ethical risks in AI projects
Apply human-centered design practices to design successful AI product experiences
Build AI systems that augment human intelligence and inspire model trust in users
Habilidades que você terá
- Machine Learning
- Privacy
- Design Thinking
- Ethics
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 3 no
Nível iniciante
No prior experience in AI or programming required
Aprox. 17 horas para completar
Inglês
Legendas: Inglês
oferecido por
Universidade Duke
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
Programa - O que você aprenderá com este curso
5 horas para concluir
Design of AI Product Experiences
In this module we will discuss approaches and tools to perform human-centered design, which is critical to designing successful AI products. We will then walk through the key challenges involved in the user experience design of AI products and how to resolve them.
5 horas para concluir
12 vídeos (Total 68 mín.), 5 leituras, 1 teste
3 horas para concluir
Data Privacy and AI
In this module we will focus on data privacy as it relates to AI products. We will first cover best practices in ensuring user privacy and the relevant U.S. and international privacy laws to be aware of. We will then discuss how AI creates unique challenges in ensuring privacy and some of the methods and tools which can be employed to protect the privacy of user data.
3 horas para concluir
8 vídeos (Total 42 mín.), 3 leituras, 1 teste
3 horas para concluir
Ethics in AI
In this module we will discuss the three main goals of ethical AI: fairness, accountability and transparency. We will identify common sources of bias in modeling projects and discuss approaches to detecting and mitigating bias, including organizational, process, and technical components.
3 horas para concluir
6 vídeos (Total 41 mín.), 3 leituras, 1 teste
6 horas para concluir
Human and Societal Considerations
In this module we will begin with differentiating between human intelligence and artificial intelligence, and then examine ways that they can compliment each other. We will conclude the course by learning about approaches to encourage adoption and inspire trust among users in your model.
6 horas para concluir
7 vídeos (Total 39 mín.), 2 leituras, 2 testes
Sobre Programa de cursos integrados AI Product Management
Organizations in every industry are accelerating their use of artificial intelligence and machine learning to create innovative new products and systems. This requires professionals across a range of functions, not just strictly within the data science and data engineering teams, to understand when and how AI can be applied, to speak the language of data and analytics, and to be capable of working in cross-functional teams on machine learning projects.
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