Welcome to "Responsible AI – Principles and Ethical Considerations"! Dive deep into the very essence of Responsible AI with us. Uncover the significance of key principles shaping technology's future. From ethical considerations to fairness, transparency, and accountability, we discuss these principles with real-world examples, putting them into the context of data science.
Responsible AI - Principles and Ethical Considerations
This course is part of Leadership Strategies for AI and Generative AI Specialization
Instructors: Fractal Analytics
Included with
(14 reviews)
Recommended experience
What you'll learn
Discuss responsible AI principles and their significance in technology, including ethical considerations, fairness, transparency, and accountability.
Apply techniques to identify, address, and mitigate bias in AI algorithms and data, promoting fairness and inclusivity in AI systems.
Interpret and explain AI decisions, balancing accuracy and explainability to foster trust and accountability in AI systems.
Discuss accountability, ethical AI governance, privacy considerations, security measures in the development & deployment of responsible AI systems.
Details to know
Add to your LinkedIn profile
12 assignments
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 5 modules in this course
In this module, you will learn about AI and the challenges it brings in different domains. You will be able to understand the need of Responsible AI and 6 principles of Responsible AI.
What's included
10 videos4 readings3 assignments2 discussion prompts1 plugin
In this module, you'll learn the concept of fairness within AI and gain insights into the different forms of biases that can infiltrate the machine learning pipeline. You will also learn about effective techniques for bias mitigation and measurement.
What's included
8 videos3 assignments1 discussion prompt
In this module, you will explore the concept of transparency in AI, gaining a deep understanding of its importance. You'll also discover how transparency in data and models plays a crucial role in achieving explainability, ultimately leading to transparent and explainable business decisions.
What's included
5 videos2 assignments
In this module, you'll learn the core concept of accountability in AI and its significance. Explore the concept of drift, including its various types, and delve into the diverse techniques for detecting drift in AI systems.
What's included
3 videos2 readings2 assignments1 discussion prompt
In this module, you'll learn the crucial need for data privacy in AI. Explore Privacy by Design, its foundational elements, and how it safeguards privacy in AI systems. Understand AI security and the concept of differential privacy for robust and private AI applications.
What's included
4 videos2 readings2 assignments
Instructors
Offered by
Recommended if you're interested in Data Analysis
Why people choose Coursera for their career
Learner reviews
Showing 3 of 14
14 reviews
- 5 stars
64.28%
- 4 stars
14.28%
- 3 stars
14.28%
- 2 stars
7.14%
- 1 star
0%
New to Data Analysis? Start here.
Open new doors with Coursera Plus
Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.