Certified Professional in Privacy Ethics in AI
-- viewing now**Artificial Intelligence (AI)** is transforming industries, but it also raises significant **privacy** concerns. To address these concerns, the Certified Professional in Privacy Ethics in AI (CPPE-AI) program was created.
2,644+
Students enrolled
GBP £ 149
GBP £ 215
Save 44% with our special offer
About this course
Designed for professionals and organizations, the CPPE-AI program equips learners with the knowledge and skills to develop and implement AI systems that respect individuals' **privacy** rights.
Through a comprehensive curriculum, learners will explore topics such as **data protection**, **algorithmic bias**, and **transparency** in AI decision-making.
By earning the CPPE-AI certification, learners will demonstrate their ability to navigate the complex landscape of AI and **privacy** ethics.
Join the growing community of professionals committed to responsible AI development. Explore the CPPE-AI program today and take the first step towards shaping a more **privacy**-conscious AI future.
100% online
Learn from anywhere
Shareable certificate
Add to your LinkedIn profile
2 months to complete
at 2-3 hours a week
Start anytime
No waiting period
Course details
•
Data Protection by Design (PbD) - This unit focuses on incorporating privacy into the design and development of AI systems, ensuring that data protection is a fundamental aspect of the system's architecture. •
Artificial Intelligence and Machine Learning Ethics - This unit explores the ethical implications of AI and ML, including issues related to bias, transparency, and accountability, and provides guidance on developing ethical AI systems. •
Human Rights and AI - This unit examines the relationship between human rights and AI, including the right to privacy, freedom of expression, and non-discrimination, and provides guidance on how to ensure that AI systems respect and protect human rights. •
AI and Bias - This unit delves into the issue of bias in AI systems, including how bias can be introduced into systems and how to detect and mitigate bias, and provides guidance on developing fair and transparent AI systems. •
Data Governance and Management - This unit provides guidance on data governance and management, including data quality, data security, and data sharing, and is essential for ensuring that AI systems are used in a responsible and ethical manner. •
Explainability and Transparency in AI - This unit focuses on the importance of explainability and transparency in AI systems, including how to develop models that are transparent and explainable, and provides guidance on how to ensure that AI systems are accountable and trustworthy. •
AI and the Internet of Things (IoT) - This unit explores the intersection of AI and IoT, including the potential risks and benefits of AI in IoT systems, and provides guidance on how to ensure that AI systems in IoT are secure and private. •
AI and Cybersecurity - This unit examines the relationship between AI and cybersecurity, including how AI can be used to enhance cybersecurity and how AI systems can be vulnerable to cyber threats, and provides guidance on how to ensure that AI systems are secure and resilient. •
AI and Mental Health - This unit explores the potential impact of AI on mental health, including the potential benefits and risks of AI in mental health applications, and provides guidance on how to ensure that AI systems are designed and used in a way that promotes mental well-being. •
AI and the Workforce - This unit examines the impact of AI on the workforce, including the potential benefits and risks of AI in the workplace, and provides guidance on how to ensure that AI systems are designed and used in a way that supports workers and promotes social responsibility.
Data Protection by Design (PbD) - This unit focuses on incorporating privacy into the design and development of AI systems, ensuring that data protection is a fundamental aspect of the system's architecture. •
Artificial Intelligence and Machine Learning Ethics - This unit explores the ethical implications of AI and ML, including issues related to bias, transparency, and accountability, and provides guidance on developing ethical AI systems. •
Human Rights and AI - This unit examines the relationship between human rights and AI, including the right to privacy, freedom of expression, and non-discrimination, and provides guidance on how to ensure that AI systems respect and protect human rights. •
AI and Bias - This unit delves into the issue of bias in AI systems, including how bias can be introduced into systems and how to detect and mitigate bias, and provides guidance on developing fair and transparent AI systems. •
Data Governance and Management - This unit provides guidance on data governance and management, including data quality, data security, and data sharing, and is essential for ensuring that AI systems are used in a responsible and ethical manner. •
Explainability and Transparency in AI - This unit focuses on the importance of explainability and transparency in AI systems, including how to develop models that are transparent and explainable, and provides guidance on how to ensure that AI systems are accountable and trustworthy. •
AI and the Internet of Things (IoT) - This unit explores the intersection of AI and IoT, including the potential risks and benefits of AI in IoT systems, and provides guidance on how to ensure that AI systems in IoT are secure and private. •
AI and Cybersecurity - This unit examines the relationship between AI and cybersecurity, including how AI can be used to enhance cybersecurity and how AI systems can be vulnerable to cyber threats, and provides guidance on how to ensure that AI systems are secure and resilient. •
AI and Mental Health - This unit explores the potential impact of AI on mental health, including the potential benefits and risks of AI in mental health applications, and provides guidance on how to ensure that AI systems are designed and used in a way that promotes mental well-being. •
AI and the Workforce - This unit examines the impact of AI on the workforce, including the potential benefits and risks of AI in the workplace, and provides guidance on how to ensure that AI systems are designed and used in a way that supports workers and promotes social responsibility.
Career path
Entry requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
Why people choose us for their career
Loading reviews...
Frequently Asked Questions
Debug: False
Course fee
MOST POPULAR
Fast Track
GBP £149
Complete in 1 month
Accelerated Learning Path
- 3-4 hours per week
- Early certificate delivery
- Open enrollment - start anytime
Standard Mode
GBP £99
Complete in 2 months
Flexible Learning Pace
- 2-3 hours per week
- Regular certificate delivery
- Open enrollment - start anytime
What's included in both plans:
- Full course access
- Digital certificate
- Course materials
All-Inclusive Pricing • No hidden fees or additional costs
Get course information
Earn a career certificate
CERTIFIED PROFESSIONAL IN PRIVACY ETHICS IN AI
is awarded to
Learner Name
who has completed a programme at
London School of Planning and Management (LSPM)
Awarded on
05 May 2025
Blockchain Id: s-1-a-2-m-3-p-4-l-5-e
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
