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Architecting Trust: AI Safety, Ethics & Compliance

Building Robust and Regulated AI Systems for the Modern Enterprise

This program provides a 360-degree view of the AI lifecycle through the lens of safety and ethics. Participants will move beyond 'AI hype' to master the frameworks, tools, and legal requirements necessary to deploy AI that is not only powerful but also predictable and compliant.

Architecting Trust Course Cover

Core Learning Outcomes

Identify and mitigate algorithmic bias using state-of-the-art technical remediation.
Implement Explainable AI (XAI) libraries (SHAP, LIME) into production pipelines.
Navigate complex regulatory landscapes like the EU AI Act and NIST AI RMF.
Protect AI systems against adversarial attacks like prompt injection and data poisoning.
Establish cross-functional AI oversight boards and incident response protocols.
Quantify the 'Ethical ROI' and treat trust as a competitive advantage.

Curriculum Overview

Practical Simulations & Labs

The Bias Audit Lab

Clean a 'toxic' dataset to meet fairness metrics using Fairlearn.

Python + Fairlearn + AIF360

The Policy Workshop

Draft a 'Company AI Ethics Charter' for Healthcare or Finance.

Framework: NIST AI RMF

Adversarial Simulation

Attempt to 'break' a model in a sandboxed environment to find injection flaws.

Red Teaming Toolkit

Learning Path by Role

Engineers

Code bias-detection scripts and implement XAI libraries into the CI/CD pipeline.

Managers

Frameworks to assess the 'Ethics-to-Revenue' ratio and lead safe AI transitions.

Compliance

Deep understanding of the EU AI Act and NIST to ensure company passes external audits.

Frequently Asked Questions

₹21999
Professional Excellence Tier
Professional Certification
Bias Remediation Toolkit
Regulatory Compliance Maps
Incident Response Playbooks
Instructor

Celoris Designs

Celoris

Celoris Designs

Excellence in AI Safety

We specialize in operationalizing AI safety for the modern enterprise. From technical bias mitigation to boardroom governance, we bridge the trust gap.

4.95(1240+)
6-8 Weeks (Self-paced)

Prerequisites

  • Basic understanding of Machine Learning concepts
  • Familiarity with Python (for technical modules)
  • Interest in AI Governance and Corporate Compliance
  • No advanced Math PhD required
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