The Messages We Send: The Case Against "Deploying" Machine Learning

The language we use to speak about AI is incredibly important, including on our technical terms. One of the biggest disadvantages of the discourse on machine learning and AI are the terms we've used to describe it thus far. For example, take a look at implement versus deploy: Which word best describes what you're doing with a production machine learning model? While practitioners might argue the words have the same meaning, in the context of ML, in this post, I will discuss why implementing and incorporating might be better terms than deployment. I'll also dive into how using different words can help us build a movement for responsible AI that spans disciplines and industries.