Practicing Responsible Generative AI has been added to your cart.
Date: Oct 8, 2026 – Oct 9, 2026   Location:
 

Practicing Responsible Generative AI

Apply responsible-AI practices that make generative AI deployable under ethical and regulatory scrutiny. Trustworthy AI and unique GenAI risks cover safety, security, resilience, explainability,...

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$2,000USD
Duration 2 days
Course Code GAI-2202
Available Formats Classroom, Virtual
Next Class: Oct 8, 2026

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Overview

Course Description

Apply responsible-AI practices that make generative AI deployable under ethical and regulatory scrutiny. Trustworthy AI and unique GenAI risks cover safety, security, resilience, explainability, fairness, validity, reliability, hallucinations, IP, bias, homogenization, environmental impact, and supply-chain risk. Model evaluation and validation cover evaluation methods, safety and resilience metrics, explainability, fairness, validity, and responsible-AI metrics. Developing GenAI responsibly and continuous monitoring cover data collection, model selection, prompt engineering for ethical outputs, content-responsibility techniques, UI design with responsibility in mind, and monitoring as an ongoing discipline. Legal and organizational implications cover GDPR, CCPA, EU AI Act, liability and accountability, responsible-AI policies, AI ethics boards, and case studies of legal challenges. Hands-on labs produce a responsible-AI scorecard, a dataset bias-and-fairness assessment, a responsible-AI policy, and an analyzed legal case study. The course is designed for data scientists, software developers, and DevOps engineers with Python and prior generative-AI experience.

Skills Gained

By the end of this course, participants will be able to:

  • Apply fairness, transparency, and accountability principles to AI models
  • Configure model behaviour to meet ethical standards across deployment
  • Evaluate AI systems for legal compliance against GDPR, CCPA, and EU AI Act
  • Develop responsible-AI implementation strategies fit for your organization
  • Apply continuous monitoring as a responsible-AI discipline

Who Can Benefit

  • Data Scientists
  • Software Developers
  • DevOps

Prerequisites

Participants should enter this course with:

  • Practical Python experience
  • GAI-1101 or equivalent

Organizational Objectives

This course assists organizations to:

  • Lower legal and compliance risk through responsible-AI policies grounded in real regulations
  • Reduce reputational risk through bias detection and responsible content design
  • Build a working responsible-AI vocabulary across data-science and engineering teams
  • Establish governance hooks (ethics boards, policies) that scale with AI deployment

Software

All attendees must have a modern web browser and an Internet connection.

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Course Details

Course Details

Module 1 - Understanding Trustworthy AI

By the end of this module, you will be able to characterize AI safety, security, and resiliency, apply explainability and interpretability principles, ensure fairness, build accountability and transparency, and recognize valid and reliable AI behaviour.

  • What makes AI Safe, Secure, and Resilient?
  • The Importance of Explainability & Interpretability
  • Ensuring Fairness in AI
  • Accountability & Transparency in AI
  • Valid & Reliable AI
  • Hands-on Lab: Author a trustworthy response to a real AI risk and balance the trustworthy-AI attributes against each other.

Module 2 - Unique and Increased Risks of Generative AI

By the end of this module, you will be able to recognize the risks unique to or amplified by generative AI — hallucinations, data privacy, environmental impact, cybersecurity, IP issues, abusive content, bias, homogenization, and supply chain.

  • Hallucinations, Dangerous Output, and Data Privacy
  • Environmental Impact, Human-AI Configuration, and Information Integrity
  • Cybersecurity, Intellectual Property, and Controlled Information
  • Abusive Content and Toxicity, Bias, and Homogenization
  • Supply Chain Risks
  • Hands-on Lab: Identify generative-AI risks in a sample system and choose between mitigate, transfer, accept, and avoid.

Module 3 - Model Evaluation and Validation

By the end of this module, you will be able to evaluate generative models against safety, resiliency, privacy, explainability, fairness, and validity metrics — and apply responsible-AI metrics tailored to generative output.

  • How to Evaluate Generative Models
  • What to Measure in a Generative Model
  • Metrics for Safety, Resiliency, and Privacy
  • Metrics for Explainability and Interpretability
  • Metrics for Fairness and Validity
  • Metrics for Responsible Generative AI
  • Hands-on Lab: Apply spotlighting to reduce prompt injections and evaluate the resulting LLM with DeepEval against fairness and validity metrics.

Module 4 - Developing Generative AI Applications Responsibly

By the end of this module, you will be able to apply responsible data collection and preprocessing, choose models and training strategies that minimize bias, prompt for ethical outputs, and design user interfaces that surface AI limitations to users.

  • Data Collection and Preprocessing Best Practices
  • Model Selection and Training Guidelines
  • Prompt Engineering for Ethical Outputs
  • Techniques for Improving Responsibility in Generated Content
  • Building User Interfaces with Responsible AI in Mind
  • Hands-on Lab: Assess one dataset for bias and fairness, then build a Responsible AI scorecard grounded in the findings.

Module 5 - Basics of Generative AI Monitoring

By the end of this module, you will be able to distinguish ongoing monitoring from offline evaluation, choose key monitoring metrics for an LLM application in production, instrument an alert and log pipeline, and verify the monitoring system end-to-end.

  • Differences Between Evaluation and Monitoring
  • Identifying Key Monitoring Metrics
  • Understanding the Monitoring Workflow
  • Alerts, Logs, and Monitoring Verification
  • Setting Up a Monitoring System
  • Hands-on Lab: Stand up a monitoring system for one GenAI application with key metrics, alert thresholds, and verification.

Module 6 - Legal and Organizational Implications of Generative AI

By the end of this module, you will be able to apply data-privacy regulations to AI systems, reason about liability and accountability for AI-generated content, develop responsible-AI policies, and recognize the role of AI ethics boards in organizational governance.

  • Data Privacy Regulations (e.g., GDPR, CCPA)
  • Liability and Accountability for AI-Generated Content
  • Developing Responsible AI Policies and Guidelines
  • Building a Culture of Ethical AI Development
  • Case Studies of Legal Challenges in Generative AI
  • The Role of AI Ethics Boards and Committees
  • Hands-on Lab: Develop a Responsible AI policy for an organization, grounded in a real legal case study.

Schedule

FAQ

Does the course schedule include a Lunchbreak?

Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does Ascendient Learning deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

What does vendor-authorized training mean?

As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.

Is the training too basic, or will you go deep into technology?

It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.

How up-to-date are your courses and support materials?

We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.

Are your instructors seasoned trainers who have deep knowledge of the training topic?

Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.

Do you provide hands-on training and exercises in an actual lab environment?

Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.

Will you customize the training for our company’s specific needs and goals?

We will work with you to identify training needs and areas of growth.  We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.

How do I get started with certification?

Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.

Will I get access to content after I complete a course?

You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.

How do I request a W9 for Ascendient Learning?

View our filing status and how to request a W9.

Reviews

You get detailed labs to guide you through the technical material giving you a hands on method of learning otherwise difficult material.

Very good couse and again we would like to see more videos on removing FRUs

The class was very vast paced however the teacher was very good at checking in on us while giving us time to complete the labs.

very good and spcecific course and above all a very good instructor. In few days I have learned a lot.

Courseware was effective but would like to have some PDF material on BPML and XPATH