Real-world projects from industry experts
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Ethical AI
Course
Learn to apply ethics of artificial intelligence principles to minimize bias, while maximizing fairness and explainability, ensuring an ethical future for all.
Estimated time
1 month
At 5 hours/week
Enroll by
June 7, 2023
Get access to classroom immediately on enrollment
Skills acquired
AI Fairness, Model Bias Analysis, Ethical AI, AI Governance
What you will learn
Ethical AI
Estimated 1 month to completeThis AI ethics course provides learners with the job-ready digital skills they need to approach and apply ethical AI.
Prerequisite knowledge
AI & machine learning fundamentals.
Introduction to Ethical AI
- Articulate the context and motivation for ethical AI.
- Create an ethical AI perspective to understand the strengths and weaknesses of our thinking.
AI Ethics for Organizations
- Articulate the impact of bias and fairness on decision-making with AI.
- Identify and develop organizational ethical AI pipelines, guidelines, and frameworks.
- Articulate components of ethical governance initiatives.
Identifying Bias Towards Fairness
- Identify types of data and machine learning (ML) bias and where they are introduced.
- Identify harms in AI solutions.
- Define AI fairness problem statements and priorities.
- Apply methodologies for identifying data and AI models bias.
- Apply metrics for measuring AI bias and fairness.
Mitigating Bias Towards Fairness
- Identify bias and fairness with AI lifecycle phases and negative feedback loops.
- Assess the strengths and weaknesses of bias and fairness mitigation strategies and metrics.
- Implement mitigation strategies to improve fairness in AI models and solutions.
- Articulate considerations towards designing and building data and models with enhanced fairness.
Transparency, Trust, and Explainability
- Articulate elements of legal programs towards data privacy, AI security, and transparency.
- Articulate compliance metrics for responsible data governance.
- Articulate what explainability is in AI/ML and apply solutions.
- Communicate trust to customers and users of AI/ML systems using compliance and creating industry standard documentation.
- Identify ethical AI auditing mechanisms.
Course Project: AI Ethics for Personalized Budget Prediction
Personalization is a central aspect of many core AI systems. In this project, learners will be tasked with designing an AI model for budget prediction and applying ethical AI considerations. They will use data exploration and bias and fairness measurement skills to complete the first phase of the project around harm quantification. They will then apply bias mitigation skills towards remediating the harm, and construct a model card articulating the ethical implications, quantitative analysis, and business consequences of the use case.
All our courses include
Real-time support
On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.
Quizzes
Check your understanding of concepts learned in the course by answering simple and auto-graded quizzes. Easily go back to the lessons to brush up on concepts anytime you get an answer wrong.
Flexible learning program
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Course offerings
Class content
- Real-world projects
- Project reviews
- Project feedback from experienced reviewers
Student services
- Student community
- Real-time support
Succeed with personalized services.
We provide services customized for your needs at every step of your learning journey to ensure your success.
- Experienced Project Reviewers
- Real-Time Support
Get timely feedback on your projects.
- Personalized feedback
- Unlimited submissions and feedback loops
- Practical tips and industry best practices
- Additional suggested resources to improve
1,400+ project reviewers
2.7M projects reviewed
88/100 reviewer rating
1.1 hours avg project review turnaround time
Learn with the best.
Learn with the best.
Ria Cheruvu
AI Ethics Lead Architect at IntelRia Cheruvu is Intel’s NEX AI Ethics Lead Architect, leading trustworthy AI. She is an emerging industry speaker and has a master’s in data science from Harvard University. Ria previously served as a teaching fellow for Harvard's 2021 data science graduate curriculum and lead instructor for Eduonix's ML deployment course.
Ethical AI
Get started today
Learn
Learn ethical AI skills that enhance fairness, limit bias, and avoid unforeseen consequences.
Average Time
On average, successful students take 1 month to complete this program.
Benefits include
- Real-world projects from industry experts
- Real-time support
Program details
Do I need to apply? What are the admission criteria?
No. This course accepts all applicants regardless of experience and specific background.
What are the prerequisites for enrollment?
A well-prepared learner should have experience with AI systems, machine learning models, basic data parsing and visualization, creating efficient scripts, and the AI lifecycle.
Learners who are not comfortable with these skills are welcome to take our AI Programming with Python or Deep Learning Nanodegree program to get up to speed.
How is this course structured?
This Ethical AI course consists of content and curriculum to support one project. We estimate that students can complete the program in 1 month.
The project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
How long is this course?
Access to this course runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our programs.
Can I switch my start date? Can I get a refund?
Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.
What software and versions will I need in this course?
To run demo material or exercises locally, learners will need a computer running a 64-bit operating system with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.7 or a higher Python version and supporting packages. Most modern Windows, OS X, and Linux laptops or desktops will work well.