Generative AI in the Workplace: Policies, Ethics, and Risks
This course is part of Navigating Disruption: Generative AI in the Workplace.
This course examines the critical aspects of implementing generative AI in organizational settings, focusing on the ethical considerations, potential risks, and policy implications. Participants will gain insights into identifying AI biases in decision-making processes, understanding the challenges of algorithmic transparency, and developing proactive strategies to protect data security and user privacy. The curriculum addresses the legal landscape surrounding AI implementation, explores the dangers of deepfakes, and emphasizes the importance of explainable AI systems. As the fourth course in the "Navigating Disruption" specialization, it builds upon foundational knowledge to help professionals make informed decisions about AI adoption while prioritizing ethical considerations at every stage of implementation.
Instructors:
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What you'll learn
Identify ways generative AI algorithms can lead to biases in decision-making processes
Anticipate implications of a lack of algorithmic transparency for organizational decision-making
Plan proactively to protect data security and user privacy in AI implementations
Evaluate the trustworthiness of AI outputs for various workplace applications
Understand legal considerations and compliance requirements for AI adoption
Implement ethical frameworks at every stage of the generative AI decision-making process
Skills you'll gain
This course includes:
1.4 Hours PreRecorded video
1 assignment
Access on Mobile, Tablet, Desktop
Batch access
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There are 3 modules in this course
This course provides a comprehensive exploration of the ethical considerations, potential risks, and policy implications of implementing generative AI in workplace settings. The curriculum is structured to help professionals understand how to responsibly integrate AI technologies while mitigating biases, protecting data security, and addressing legal concerns. Students will examine real-world examples of AI implementation challenges, including algorithmic transparency issues, deepfake threats, and data privacy concerns. The course emphasizes practical approaches to ethical decision-making throughout the AI adoption process, equipping participants with the knowledge to navigate the complex landscape of workplace AI implementation while maintaining organizational integrity and trust.
Getting Started
Module 1 · 1 Hours to complete
Responsible AI Practices
Module 2 · 2 Hours to complete
Future Hurdles
Module 3 · 1 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Navigating Disruption: Generative AI in the Workplace
Instructor
4 Courses
Leading Scholar in Political Communication and Public Opinion Research
Josh Pasek serves as Associate Professor of Communication & Media and Political Science at the University of Michigan, where he has established himself as an expert in political communication, public opinion, and survey methodology. His research focuses on how new media and psychological processes shape political attitudes and behaviors, as well as improving techniques for measuring public opinion. As a Faculty Associate in the Center for Political Studies and Core Faculty for the Michigan Institute for Data Science, he explores topics such as the impact of political information on public opinion, the changing political information environment due to social media, and methodological issues in survey research. His work has been published in top journals including Public Opinion Quarterly, Political Communication, and Communication Research. Pasek has developed two widely-used R packages for survey analysis: anesrake for producing survey weights and weights for analyzing weighted survey data. His academic journey includes a Ph.D. from Stanford University and previous positions at the University of Pennsylvania and University of Vienna before joining the University of Michigan in 2011.
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Frequently asked questions
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