From the course: AI Accountability: Build Responsible and Transparent Systems (2022)
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Strategies for developers
From the course: AI Accountability: Build Responsible and Transparent Systems (2022)
Strategies for developers
- [Instructor] So you're getting ready for your big AI project. You want to do your best work, and if possible, avoid potential headaches. So I've got a few suggestions for several different stages of your project. The first stage, of course, is preparing for your project. Think about what the project's goals are. I can't tell you how many data projects I've seen that have gotten sunk by lack of clarity, or consensus about the goals. Are you looking for overall classification accuracy? Are you weighting cases, because maybe some have more impact? Are you going for speed in doing the entire project, or speed of execution? Are you looking for interpretability? What are your goals? And make sure you're on the same page as your stakeholders, so you don't have to back up, or lose time on lost effort. Also, think about what data is available to you for this project, and also what data is legally usable. So it's going to be…
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