By Brad Haynes | 23 Min Video

Lead the way in building trust and accountability with AI. Ethical leadership is key to navigating today’s AI challenges. This insightful webinar explores ethics’ critical role in building trust within AI systems and ensuring their responsible use.

Key topics include:

• Why Ethics is the Foundation of AI Trust: Understanding the ethical principles underpinning AI development and how they contribute to AI technologies’ reliability, fairness, and transparency.
• Frameworks and Practices for Responsible AI Governance: A deep dive into the key governance frameworks, best practices, and regulatory standards for overseeing AI systems and mitigating potential risks.
• Tools and Strategies to Implement Ethical AI Systems: Practical strategies, tools, and methodologies for incorporating ethical considerations into the design, deployment, and continuous monitoring of AI systems.

Watch the discussion on navigating the ethical landscape of AI, and discover how to ensure your AI systems are built on a foundation of trust and responsibility.

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The Ethics Foundation for AI Trust

• Establishes AI ethics as the essential bedrock for reliable, fair, and transparent AI systems.
• Emphasizes ethical leadership to build trust and accountability in AI adoption.

Governance Frameworks and Standards

• Reviews responsible AI governance models, including policies, organizational roles, and compliance standards.
• Covers regulations and best-practice frameworks to oversee AI systems and mitigate risks.

Bias, Fairness, and Equity

• Discusses identifying and reducing biases in data and algorithms, highlighting fairness as a non-negotiable goal.
• Explores tools and methodologies to evaluate fairness and maintain equitable deployment.

Transparency and Explainable AI (XAI)

• Advocates of transparent and explainable AI models, ensuring stakeholders understand decision-making processes.
• Introduces XAI techniques like LIME and SHAP for interpreting black-box AI behavior.

Privacy, Security, and Data Management

• Stresses the importance of privacy protection, secure data storage, and adherence to regulations like GDPR.
• Recommends continuous monitoring and cybersecurity measures to protect AI systems.

Accountability and Auditability

• Encourages creating audit trails, traceable decision logs, and clearly defined responsibility roles.
• Suggests routine audits and fairness assessments using frameworks like AI Fairness 360.

Tools and Practical Implementation Strategies

• Showcases real-world tools and methodologies for integrating ethics into design, deployments, and monitoring.
• Includes bias-checking tools, secure development pipelines, and XAI integrations.

Why It Matters

• Builds trust and reliability by embedding ethics throughout the AI lifecycle.
• Ensures compliance with regulations while proactively managing risk.
• Enables fair, transparent, and accountable AI systems that deliver real-world impact.

Instructor Bio:

Brad offers technical enablement and training solutions with a focus on value-based solutions and strategies. He meets the unique needs of each organization while providing a training experience to align with your specific technical requirements and business goals. Brad’s experience includes successfully aligning business objectives with key stakeholder groups, such as employees, customers, suppliers, government entities, organized labor, and the community. Brad holds certifications in Cisco CCNA, ISC2 CC, and CompTIA Security+ and Cloud Essentials, with previous certifications in CCDA, CCNA-Security, and CCNA-Voice. He is also actively engaged in advancing his knowledge in Artificial Intelligence and Cybersecurity, ensuring that he remains at the forefront of emerging technologies.