Updated Fairness README.md in Introduction

The provided video link for the Fairness sub-section is the one under Privacy and Security.

1-Introduction/3-fairness/README.md

@@ -78,7 +78,7 @@ AI systems should be safe and respect people’s privacy. People have less trust
 ### Transparency
 AI systems should be understandable. A crucial part of transparency is explaining the behavior of AI systems and their components. Improving the understanding of AI systems requires that stakeholders comprehend how and why they function so that they can identify potential performance issues, safety and privacy concerns, biases, exclusionary practices, or unintended outcomes. We also believe that those who use AI systems should be honest and forthcoming about when, why, and how they choose to deploy them. As well as the limitations of the systems they use. For example, if a bank uses an AI system to support its consumer lending decisions, it is important to examine the outcomes and understand which data influences the system’s recommendations. Governments are starting to regulate AI across industries, so data scientists and organizations must explain if an AI system meets regulatory requirements, especially when there is an undesirable outcome. 
 
-> [🎥 Click the here for a video: transparency in AI](https://www.microsoft.com/videoplayer/embed/RE4voJF)
+> [🎥 Click the here for a video: transparency in AI](https://www.microsoft.com/videoplayer/embed/RE4voJF #)
 
 - Because AI systems are so complex, it is hard to understand how they work and interpret the results. 
 - This lack of understanding affects the way these systems are managed, operationalized, and documented.