Ethics & Governance for Quality Management 

This course equips quality professionals with a critical understanding of ethical considerations and governance principles surrounding AI in quality control processes. You'll explore potential biases in data and algorithms, fairness and explainability in AI decisions, and strategies for responsible AI implementation within your quality management practices. 

• Explain the ethical implications of using AI in quality control tasks, including potential biases in data, algorithms, and decision-making.

• Identify different types of bias that can creep into AI systems (e.g., algorithmic bias, data bias), and understand their potential impact on fairness and quality control outcomes.

• Analyze the importance of explainability in AI models for quality control, and explore techniques for making AI decisions more transparent and interpretable.

• Discuss the ethical considerations surrounding data privacy and security in the context of AI-powered quality control systems.

• Understand the principles of AI governance, including frameworks and regulations for responsible AI development and deployment in quality management.

• Identify potential risks associated with AI in quality control and develop mitigation strategies to ensure responsible and trustworthy AI adoption.

• Analyze real-world case studies of ethical dilemmas arising from AI use in quality control across different industries.

• Develop a plan for integrating ethical considerations and governance principles into your company's quality control practices when deploying AI solutions.

1. Ethics of Quality Control:

• The Ethics of Quality Control: Introducing the ethical considerations surrounding AI adoption in quality control, exploring potential biases in data, algorithms, and decision-making processes.

• Understanding and Mitigating Bias in AI: Demystifying different types of bias in AI systems (algorithmic bias, data bias) and their impact on fairness and quality control outcomes.

• Explainable AI for Quality Control: Highlighting the importance of explainability in AI models for quality control and exploring techniques for making AI decisions more transparent and interpretable to stakeholders.

• Case Study 1: Algorithmic Bias in Loan Approval Systems: Analyzing a real-world scenario of algorithmic bias in loan approval systems and its potential impact on fairness, prompting discussions on potential risks in quality control applications.

• Data Privacy and Security in AI-Powered Quality Control: Exploring ethical considerations surrounding data privacy and security in the context of collecting, storing, and using data for AI-powered quality control systems.

• The Governance Landscape: Frameworks and Regulations for Responsible AI: Understanding the principles of AI governance, including frameworks (e.g., Algorithmic Justice League Principles) and regulations (e.g., GDPR, CCPA) for responsible AI development and deployment in quality management.

• Managing AI Risks in Quality Control: Identifying potential risks associated with AI in quality control, such as job displacement, explainability limitations, and safety concerns.

• Developing a Risk Mitigation Strategy: Exploring strategies to mitigate identified risks associated with AI in quality control, promoting responsible and trustworthy AI adoption.

• Case Study 2: Explainability Challenges in Facial Recognition for Security: Analyzing a real-world scenario of explainability challenges in facial recognition systems and its implications for responsible AI use in quality control tasks.

• Hands-on Session 1: Utilizing a fairness assessment tool, participants evaluate a sample dataset (simulated or anonymized) relevant to their quality control processes for potential bias.

• Basic understanding of AI concepts and technologies

• Familiarity with industries and their operations

• Knowledge of project management and risk assessment principles is beneficial but not required