Ethical Risk Management for Data and AI

As data-driven technologies and AI become central to modern business, the potential for unintended ethical lapses grows, making proactive risk management a critical professional skill. Simply intending to be ethical is not enough; you need a structured approach to identify vulnerabilities before they cause harm. This course provides the framework needed to detect ethical gaps and implement practical mitigation strategies to ensure technology serves both users and society. Through written explanations and practical examples, you will learn to: - Understand the core principles and terminology of ethical risk management in technology. - Identify common ethical pitfalls in data collection and algorithmic decision-making. - Apply structured assessment frameworks to detect biases and systemic vulnerabilities. - Implement mitigation strategies to protect users and maintain organizational integrity. - Integrate ethical safeguards into modern workflows, including generative AI and large language model patterns. - Improve development lifecycles by embedding ethics into every stage of the technical process. The course begins with foundational definitions of ethics in a technical context before progressing to practical assessment techniques and lifecycle integration. This text-based program is designed for beginners and requires no prior experience in ethics or data science. Start building a framework for responsible innovation today.