Generative AI Foundations: Ethical & Responsible Use of AI in IT





The rapid integration of artificial intelligence (AI) in information technology (IT) brings forth ethical responsibilities that demand critical attention. This course delves into the ethical and responsible use of AI in IT, providing IT professionals, developers, and decision-makers with the knowledge and tools needed to ensure AI models are designed and deployed ethically. Begin by exploring ethical considerations and biases and the implications of biased AI models. Then you will learn how to design an AI model while incorporating legal and compliance considerations and use anonymization to ensure privacy. You will configure content safety filters in Azure OpenAI Studio to prevent the generation of harmful content. You will discover strategies, guidelines, and legal and compliance considerations for ethical AI model development, as well as the ethical impact of AI models. Next, you will examine common AI principles and goals and dive into approaches and algorithms to help identify and reduce biases. Finally, you will investigate how ignoring sensitive features when training a predictor does not necessarily address AI model disparities.




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Generative AI Foundations: Ethical & Responsible Use of AI in IT

  • describe ethical considerations and biases in AI, with a focus on their impact on responsible AI model development
  • identify real-world implications of biased AI models, addressing their ethical and social consequences
  • design an AI model with a strong focus on legal and compliance considerations, ensuring alignment with regulatory requirements
  • anonymize and pseudonymize data to ensure the privacy and security of personally identifiable information (PII)
  • configure content safety filters in Azure OpenAI to prevent generative AI from producing harmful content
  • identify strategies to develop AI models that adhere to ethical standards, promoting fairness and ethical behavior
  • describe ethical guidelines and best practices for developing AI models that meet ethical and societal expectations
  • describe and assess the ethical impacts of AI models on individuals, society, and various stakeholders
  • identify legal and compliance considerations when developing AI models with a strong ethical focus
  • identify the goals associated with each AI principle identified in the Responsible AI Standard
  • analyze an AI model to detect and identify biases
  • reduce the correlation between sensitive features (like race) and other variables to mitigate bias affecting model predictions
  • describe how ignoring sensitive features when training a predictor doesn’t necessarily address disparities


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