Self-Paced Course
Data & AI Ethics
Explore the impact of bias and other ethical issues in the age of data & AI, and the importance of responsible data collection and stewardship.
Course Description
Data and AI Ethics are topics that most data leaders and professionals don’t learn in school, or on the job.
But as the volume of data grows and the impact of ML & AI algorithms continues to increase, understanding the ethical implications of our work – and how to prevent & mitigate ethical lapses – is more important than ever.
We’ll start this course by defining AI and Data ethics before moving onto what it means to be an ethical steward of data.
From there, we’ll dive into the types of bias that can be present in your data and how it can propagate into analyses and algorithms in a way that can not only raise ethical questions, but also negatively impact your company’s bottom line.
Next we’ll dive into the world of modern AI models, and the unique risks and ethical concerns posed by powerful generative AI tools. We’ll use case studies to highlight real life controversies, discuss how to mitigate the risk of ethical lapses, and use thought exercises to help you develop the skills to anticipate, identify, and mitigate the risk of ethical lapses in your day-to-day work.
If you’re looking for a unique and highly engaging way to learn about data and AI ethics, this is the course for you.
COURSE CURRICULUM:
- Course Structure & Outline
- DOWNLOAD: Course Resources
- Setting Expectations
- What is Data Ethics?
- Common Ethical Issues
- Ethics vs. Law
- QUIZ: Data Ethics 101
- Becoming a Data Steward
- Consent
- Security
- Privacy & Confidentiality
- Legal Implications
- Common Case Studies
- QUIZ: Ethical Data Collection & Stewardship
- What is Bias?
- Bias in Models & Analytical Outputs
- CASE STUDY: Automated Applicant Streaming
- Proxy Variables
- Combatting Data Bias
- QUIZ: Bias in Data
- The Model Training Process
- Bias, Transparency & Moral Judgement
- Biased Data vs. Biased Algorithms
- CASE STUDY: IBM Facial Recognition
- Transparency
- Black Box Algorithms
- Combatting Algorithmic Bias
- QUIZ: Algorithmic Bias
- AI Ethical Issues
- AI at Scale
- CASE STUDY: Autonomous Vehicles
- Offloading Moral Judgement
- Long-Term Societal Harm
- Lack of Transparency
- Bias in Generative AI
- Hallucinations
- Copyrights & Intellectual Property
- Fraud, Deepfakes & Propaganda
- Liability
- Mitigating AI Ethical Risks
- Legal Gray Areas
- QUIZ: Ethics in the Age of AI
- Course Feedback Survey
- Share the love!
- Next Steps
WHO SHOULD TAKE THIS COURSE?
- Data leaders looking to build a deeper understanding of the ethical concerns prevalent in modern data products and projects
- Individual contributors who want to learn more about how they can identify and mitigate potential ethical issues in their day-to-day work
- Anyone looking for a thought-provoking and highly engaging course on ethics in the age of AI
WHAT ARE THE COURSE REQUIREMENTS?
- This is an entry-level course (no prerequisites)
- Some experience working with data is helpful, but not required
Start learning for FREE, no credit card required!
Every subscription includes access to the following course materials
- Interactive Project files
- Downloadable e-books
- Graded quizzes and assessments
- 1-on-1 Expert support
- 100% satisfaction guarantee
- Verified credentials & accredited badges
Ready to become a
data rockstar?
Start learning for free, no credit card required!