The course "Responsible AI and Ethics" explores the ethical, social, and technical aspects of artificial intelligence (AI) and machine learning (ML). It focuses on understanding bias in both human and machine systems and provides strategies for mitigating risks. By examining key issues such as fairness, accountability, and the regulatory landscape, learners will gain essential knowledge to navigate the ethical challenges in AI. Through case studies and real-world examples, students will explore the complexities of AI implementations, assessing their impact on society and industries.
Responsible AI and Ethics
Responsible AI and Ethics
This course is part of AI Strategy and Project Management Specialization
Instructor: Ian McCulloh
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
16 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand the sources and trade-offs of bias in both human and AI systems, and learn strategies for mitigating these biases in AI implementations.
Explore ethical frameworks for responsible AI, focusing on transparency, fairness, and accountability, and gain knowledge of laws surrounding AI.
Analyze real-world AI case studies to identify strengths and weaknesses in AI adoption, and understand the considerations for managing AI projects.
Skills you'll gain
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Recently updated!
December 2024
Assessments
9 assignments
Taught in English
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This course is part of the AI Strategy and Project Management Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
In this course, you will explore the ethical, social, and technical aspects of Artificial Intelligence (AI) and Machine Learning (ML), focusing on sources of bias, risk mitigation strategies, and the regulatory landscape. You'll examine the trade-offs between human and machine biases, AI team dynamics, and emerging labor trends. The key topics of this course include responsible AI use, legal frameworks, and the impact of evaluation methods on team performance. you will gain practical insights into building fairer, more effective AI systems through case studies and discussions.
What's included
1 reading1 plugin
This module introduces you to the concept of bias in Artificial Intelligence. While there has been much publicity and attention on the topic of machine bias, it often ignores human bias. In this module, you will compare human and machine bias to enable a more fair assessment of risk in AI systems. Specific attention will be paid to Machine Learning bias, algorithm bias, human bias, measurement bias, and algorithmic drift.
What's included
7 videos5 readings3 assignments1 plugin
This module introduces you to the complex topic of responsible AI. The common “risk-based approach” will be contrasted with the more ethical “human baseline approach.” You will also cover fiscal/performance responsibility, international regulations, privacy, and legal considerations.
What's included
8 videos3 readings3 assignments3 plugins
This AI case studies module offers you practical insights into AI's transformative power across various applications. You will explore successful integrations and lessons from AI's challenges, focusing on decision-making, implementation, and outcomes. Real-world examples will help you understand critical success factors and avoid potential pitfalls in AI adoption.
What's included
6 videos6 readings3 assignments
Instructor
Offered by
Recommended if you're interested in Machine Learning
Northeastern University
Duke University
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