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AI DevCamp Notes: Responsible AI (Week 2)
My journey at AlDevCamp (GDG London) continues with excitement. We learn something new each week, and witnessing my growth week by week makes me proud.
Let’s see what I learned in week 2 about Responsible AI!
Ensuring AI systems are fair, interpretable, private, and secure is crucial for building trust and reliability. Responsible AI not only fosters ethical considerations but also enhances user experience and safety. This week, we delved into the best practices that help achieve these goals, exploring ways to develop more robust and trustworthy AI solutions.
Evaluating Data and Models
First of all, there are some important criteria we need to keep in mind when evaluating our data and models. These criteria are crucial for ensuring that our AI systems are fair, interpretable, private, and secure.
By keeping these criteria in mind, let’s dive deeper into this week’s topics.
Understanding and Examining Data
Before processing your data, it’s crucial to closely examine your raw data. This helps you understand what your data contains and identify potential issues early on. For example, if you’re working with customer reviews, reading a sample of the data to check for inappropriate or irrelevant entries is a good start. The quality of your data directly impacts your model’s performance.
Data Examination Process
No dataset or model is perfect. Recognizing the limitations of your dataset and your model is essential to avoid incorrect results. For instance, if your dataset only contains information from specific regions, it’s important to know that your model might not perform well globally. This awareness helps you interpret your model more accurately and manage user expectations.
