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Program overview
Online
On-site
Hybrid
Responsible AI & AI Safety Foundations
A practical, low-barrier introduction to Responsible AI, AI ethics, governance, safety principles, and emerging risks without technical prerequisites.
Duration:
3 days
Level:
Beginner
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1500+ users onboarded
Who will Benefit from this Training?
- Students and early-career practitioners
- Product Managers and Business Analysts
- HR, Operations, and Policy teams
- Junior Data Scientists
- Anyone new to Responsible AI and AI safety
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Training Objectives
- Understand ethical risks and harmful failure modes of AI systems.
- Identify common sources of bias in data, labeling, and product design.
- Apply fairness, transparency, explainability, and safety principles in practical scenarios.
- Explain major governance and regulatory frameworks in simple business language.
- Recognize unsafe LLM outputs and implement beginner-friendly guardrails.
- Participate confidently in AI governance and Responsible AI discussions.
Build a high-performing, job-ready tech team.
Personalise your team’s upskilling roadmap and design a befitting, hands-on training program with Uptut
Key training modules
Comprehensive, hands-on modules designed to take you from basics to advanced concepts
- Module 1: Introduction to Responsible AI and Ethical Foundations
- What Responsible AI means in real-world systems
- How AI systems make decisions (non-technical explanation)
- Real-world AI failures and ethical breakdowns
- Sources of risk across data, design, and deployment
- Hands-on: Evaluate an AI decision for fairness and bias
- Module 2: Understanding Bias, Fairness and Inclusivity
- Types of bias in AI systems
- Historical, sampling, labeling, and representation bias
- Fairness concepts explained without mathematics
- Designing inclusive AI products
- Hands-on: Identify bias sources in a dataset example
- Module 3: Safe Use of Large Language Models
- How LLMs generate responses
- Common LLM risks including hallucinations and harmful outputs
- Overtrust and misuse of generative AI
- Introduction to prompt safety and red-teaming
- Hands-on: Detect unsafe outputs and rewrite safer prompts
- Module 4: Transparency, Explainability and Accountability
- What transparency means in AI products
- Explainability vs interpretability with simple examples
- Model documentation basics
- Accountability structures inside organizations
- Hands-on: Create a simple Model Card for a fictional AI system
- Module 5: Global Frameworks and Regulations (Beginner Edition)
- Why AI governance frameworks exist
- NIST AI RMF explained for non-technical teams
- EU AI Act risk tiers simplified
- OECD and emerging regional AI guidelines
- Module 6: Designing Safe and Responsible AI Products
- Safety-by-design principles
- Non-technical guardrails for AI systems
- Human oversight and escalation thresholds
- Monitoring misuse and harmful interactions
- Hands-on: Redesign an AI feature with safety controls
- Module 7: Capstone – Responsible AI Review
- Ethical risk identification
- Fairness and bias assessment
- Lightweight governance documentation
- Safety improvement recommendations
Hands-on Experience with Tools
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Training Delivery Format
Flexible, comprehensive training designed to fit your schedule and learning preferences
Opt-in Certifications
AWS, Scrum.org, DASA & more
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100% Live
on-site/online training
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Hands-on
Labs and capstone projects
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Lifetime Access
to training material and sessions
Skill-Gap Assessment
Analysing skill gap and assessing business requirements to craft a unique program
1
Personalisation
Customising curriculum and projects to prepare your team for challenges within your industry
2
Implementation
Supplementing training with consulting support to ensure implementation in real projects
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Why Responsible AI for your business?
- Reduce product and reputational risk: Prevent biased or unsafe AI behavior before it reaches users.
- Improve customer trust: Build transparency and accountability into AI-powered features.
- Enable compliance readiness: Translate NIST and EU AI Act concepts into practical actions.
- Safer LLM adoption: Recognize hallucinations, harmful content, and overtrust, and apply guardrails.
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Discover the power of digital transformation with train-to-deliver programs from Uptut's experts. Backed by 50,000+ professionals across the world's leading tech innovators.
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Frequently Asked Questions
2. Will my team get any practical experience with this training?
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With our focus on experiential learning, we have made the training as hands-on as possible with assignments, quizzes and capstone projects, and a lab where trainees will learn by doing tasks live.
3. What is your mode of delivery - online or on-site?
We conduct both online and on-site training sessions. You can choose any according to the convenience of your team.
4. Will trainees get certified?
Yes, all trainees will get certificates issued by Uptut under the guidance of industry experts.
5. What do we do if we need further support after the training?
We have an incredible team of mentors that are available for consultations in case your team needs further assistance. Our experienced team of mentors is ready to guide your team and resolve their queries to utilize the training in the best possible way. Just book a consultation to get support.