Ethical and Responsible AI Training and Certification

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Learning Objectives

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AI systems have the potential to provide great value, but also the potential to cause great harm. Knowing how to build or use AI systems is simply not going to be enough. You need to know how to build, use, and interact with these systems ethically and responsibly.

Cognilytica’s comprehensive, internationally recognized approach to ethical and responsible AI covers all you need to know including gaining a comprehensive understanding of boundaries for what is acceptable and not acceptable use of AI technology. You will also gain an understanding of methods, practices and processes by which AI systems can be implemented to fulfill governance, documentation, measurement, and mitigation. You will learn the necessary aspects to turn abstract ethical AI concepts into practical implementation actions and gain knowledge of necessary elements of ethical and responsible AI systems.

Certification is provided for all individuals who successfully complete all modules and exercises for this course.

Course Content

Introduction to Ethical and Responsible AI

Societal Ethical AI Principles

Responsible AI Practices

AI System Transparency

AI System Governance

Algorithmic Interpretability & Explainability

Worldwide Data and AI laws & regulations

Putting Ethical & Responsible AI into Practice

What Will I Learn?

Module 1: Introduction to Ethical and Responsible AI

Why are ethical and responsible AI systems necessary?
Ethical and responsible AI: it’s not just about “feel good”
Addressing fears about AI
Addressing concerns about AI
Addressing issues of bias and human induced error
The “Uncanny Valley”… of data
Understanding the Limits of Current Cognitive Technology
AI reality vs AI science fiction
The necessity of keeping the human in the loop
The data-centricity of AI
Risks associated with the use of AI
AI Introduces new Threat Vectors
The cost of non-compliance and the risk of being an ethical pariah
Awareness of the value of data, and the need for data privacy and appropriate sharing
Overview of Cognilytica Comprehensive Ethical & Responsible AI Framework

Module 2: Societal Ethical AI Principles

Making sure systems comply with basic human values
AI systems meant for human benefit
AI systems under human control
Data and AI systems that maintain human dignity
Maintaining human freedom of choice and agency in self-determination
Data and AI system fairness
The great potential for AI system bias
AI system representation: issues of diversity and inclusion in data
Making sure AI systems don’t run amok of the environment
Case studies of Societal AI ethics gone wrong
Examples of applications of Societal Ethical AI
Applying the Cognilytica Ethical & Responsible AI Framework for Societal Ethical AI Principles

Module 3: Responsible AI Practices

Responsible vs. Ethical: what’s the difference?
AI systems implemented to maintain a positive purpose
Misuse and Abuse of AI Systems
Compliance with rapidly evolving laws and regulations around AI and data
Maintaining AI system safety
Maintaining AI system security
Maintaining AI system trust
Putting into place a chain of command: human accountability
Data and AI privacy policies, rules, laws, and regulations
Dealing with the potential for AI-caused workforce disruption
Case studies of Responsible AI gone wrong
Examples of applications of Responsible AI
Applying the Cognilytica Ethical & Responsible AI Framework for Responsible AI Principles

Module 4: AI System Transparency

Understanding the full scope of the data and decisions that go into AI systems
AI System transparency vs. algorithmic transparency
Open systems: technology
Open systems: data
Bias Measurement & Mitigation
AI and data disclosures
AI system consent
Case studies of AI system transparency gone wrong
Examples of applications of AI system transparency
Applying the Cognilytica Ethical & Responsible AI Framework for Systemic AI Transparency Principles

Module 5: AI System Governance

Governance: it’s about people, processes, and technology
AI system risk assessment & mitigation
AI System Auditability
Contestability of algorithmic decision-making and AI system use
AI System Controls: development, training, implementation, and inference
AI System Monitoring
AI System Quality Verification
Third-party supervision, certification, and regulation
The importance of an educated and adequate trained workforce
Case studies of AI system governance gone wrong
Examples of applications of AI system governance
Applying the Cognilytica Ethical & Responsible AI Framework for AI Governance Principles

Module 6: Algorithmic Interpretability & Explainability

The AI “black box”
Algorithmic Explainability
Algorithmic Interpretability
A lower bar to meet: Understandability / Root Cause Explanations
The challenges of “Algorithmic Discrimination”
The varying levels of opacity of different AI algorithms
The need for algorithmic explainability for different AI use cases and patterns
Technology solutions for algorithmic explainability
Other approaches to algorithmic interpretability and explainability
Case studies of AI algorithmic explainability gone wrong
Examples of applications of AI algorithmic explainability
Applying the Cognilytica Ethical & Responsible AI Framework for Algorithmic Transparency Principles

Module 7: Worldwide Data and AI laws & regulations

The evolving landscape of AI and data laws and regulations
Laws and regulations for emerging tech
Introduction to current global laws and regulations pertaining to data and AI
Current state of worldwide data and AI laws and regulations
Facial Recognition and Computer Vision-Relevant Laws and Regulations
AI-Relevant Data Privacy Laws and Regulations
Autonomous Vehicle-Relevant Laws and Regulations
Laws and Regulations Pertaining to AI Ethics, Bias, and Fairness
Lethal Autonomous Weapons Systems (LAWS) and AI-enhanced Weapons
Chatbot and Conversational Systems Laws and Regulations
Laws and Regulations Regarding Use of AI for Predictions or Decision Support
Laws and Regulations Regarding Malicious AI and AI-Enabled Forgery and Fakery
Other General AI Regulations
Specific examples of AI and data laws and regulations in use and enforcement
Planning for continued evolution of AI and data laws in the context of the Cognilytica Ethical & Responsible AI Framework

Module 8: Putting Ethical & Responsible AI into Practice

Ethical and Responsible AI: it’s something you DO, not just a statement.
Organizational change management for ethical and responsible AI practices
The AI governance team
Organizational compliance teams
Ethical AI teams, boards, and centers of excellence (CoE)
Case studies in ethical & responsible AI teams gone wrong
Putting ethical and responsible AI practices into place in data science, data engineering, and ML development teams
Ethical and Responsible AI components of Best Practices Methodologies (CPMAI)
Technology solutions for implementing ethical and responsible AI practices
ML Ops facilitating practical ethical & responsible AI implementation
Applying ethical & responsible AI at scale
Roadblocks to ethical & responsible AI implementation
KPIs and success metrics for ethical & responsible AI
Responding to AI and data crises
Mitigating ethical and responsible AI and data challenges
Go forth and Do: Moving Forward with Ethical & Responsible AI

Who is this Training For?

C-Level Executives
Business executives
Directors
Vice Presidents
Technical Managers
Non Technical Managers
Project Managers
Product Managers
Information Security Managers
Data Scientist
AI / Machine Learning (ML) Engineer

Learning Paths

Supported Learning Paths

Cognilytica: Ethical & Responsible AI Certification
Edison DSF: Data Science Managers (DSP01 - DSP03)
Edison DSF: Data Science Professionals (DSP04 -DSP09)
Edison DSF: Data Engineering & Management Professionals (DSP10-DSP16)
Edison DSF: Operations & Technical Support (DSP17-DSP20)
Edison DSF: Data & User Support (DSP20-DSP22)

Learning Levels

Edison DSF: Level 2-3
Category: Ethical & Responsible AI

Course Schedule

Course Schedule & Delivery

Course delivered online with a live, virtual instructor

Course is sixteen (16) hours of live instruction plus exercises to be completed by trainees on their schedule between online classes.
Course is delivered over four (4) consecutive weeks, two (2) days per week, two (2) hours per day.
Next course is scheduled for Q1 2022: please inquire about availability
Private instructor courses offered for groups of 20 or more: please inquire about availability
Access to recorded videos is provided for thirty (30) days after conclusion of class
Additional terms and conditions apply

Course Pricing

Pricing, Group Discounts, Enrollment Details

- $1,995 USD per trainee
- Certification & materials included in price
- Group enrollment discounts:
  - 10% discount for 5 or more trainees
  - 15% discount for 10 or more trainees
  - 20% discount for 15 or more trainees

Group enrollment discount applied at time of registration of all trainees in group

What people are saying

"Cognilytica’s CPMAI training has had a direct and immediate impact on our team. The course gave an excellent overview of the fundamentals of AI as well as provided a common framework for our teams to communicate and collaborate in the management of AI projects. I'm a believer and champion of the value of this training. I am already seeing the value and impact of the knowledge and insight gained by my direct reports. I recommend this course for anyone who is looking to train or upskill their workforce around AI project management efforts."

“Cognilytica’s CPMAI project management training and certification is a very effective program for executives and managers. Having taken this training early on in our AI journey, it provided me with a solid framework needed to successfully guide and provide meaningful oversight to our AI and ML projects. Ron and Kathleen clearly illustrated their strong fundamentals in understanding AI & machine learning with a big emphasis on how to do AI projects right. This training and certification is a must for anyone looking to run and manage AI projects of any size.”

"The CPMAI methodology and Cognilytica’s training helped break down the fundamentals of AI including various algorithms, why deep learning has become so popular, and why data is at the heart of AI. I highly recommend this training for anyone looking to learn how to do AI right, and who wants to learn the data and AI centric CPMAI methodology."

"The CPMAI methodology and Cognilytica's AI & ML training verbal and visual aspect of the training were very informative and definitely kept my attention. The exercises helped to re-emphasize what we learned in the class, rendering them very useful."

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