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Artificial Intelligence: An Intro & Ethics Breakdown

  • 02 Mar 2023
  • 10:00 AM - 11:00 AM
  • zoom

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Part 1: Intro to AI: What is it? How can you use it? 

Part 2: Ethics in AI: Is AI good for me? Can it be trusted?

During part 1 we will cover off the fundamentals of AI. Since you can read about it anywhere, we will also drill into what it takes for you to create your own system, and the challenges many face with trying to understand when to identify where an AI system is being used and how it is helping/hurting you.

In part 2 we must talk about the implications of AI. We will touch on the countless books, Netflix series, and other well documented materials that explore how AI is often created by a narrow group of people. After this session you will understand why you need to ask questions about systems that use AI and consider the harm it may be causing you or others. In addition, you will have the tools to ask yourself or others creating their own AI to ensure what they create is equitable.

Key Takeaways;

  • There is a broad range of what can be considered AI. You can define it however you want, depending on the day and how you’re talking about it.
  • Why creating usable “AI” can be hard.
  • Opportunities everyone has to explore AI.
  • Questions you need to ask about the tools and systems you use every day to ensure they are not causing you and society harm.
  • Why the use of AI may or may not be good for us as a society.
  • Steps you can take to ensure any agents or systems you create will be ethical.

Meet your presenter 

Jennifer LaPlante is the Executive Director of DeepSense, an organization focused on economic growth through industry and academic partnerships in the ocean economy. Her work is rooted in advancing technology adoption and developing the talent pool. She is a member of the Government of Canada’s AI Public Awareness Working Group and the co-organizer of the Halifax Chapter of Women in Machine Learning and Data Science (WiMLDS), an organization that aims to support and promote women and gender minorities who are working, studying or are interested in the fields of machine learning and data science.


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