Data quality is critical in powering artificial intelligence (AI)-driven decisions. Christopher Teixeira, MITRE principal data scientist, speaking on the Robots & Red Tape podcast, emphasized the importance of continuously evolving models to ensure ethical and effective AI applications.
He explained that effective AI problem-solving starts with defining the issue and identifying relevant data, highlighting that success depends not only on data volume, but understanding its limitations and context.
"It's not necessarily just being able to take every single data source, throw it into a machine learning model and see what comes out," said Teixeira. "It's being able to understand what some of those biases are that might occur, what are some of the inconsistencies in the data and knowing it's not a bad thing. You understand what it is, what it represents, and how you might be able to take advantage."
Common challenges in data science include incomplete data, the time required to collect it, and the associated costs. Teixeira explained these limitations demand a thoughtful approach to both data selection and analysis.
"Trying to make sure you incorporate the challenges with incomplete data, the potential biases in how the data was collected, or being able to correctly represent the system or the problem space you're trying to model, can provide a lot of challenges with the data you might have available to work with," said Teixeira. "No dataset is ever considered perfect for being able to answer the problem you're looking at. It's all about making sure there's some transparency in what is collected and how it's used."
Another key concern is the tendency to over-trust AI outputs. Teixeira warned against blindly accepting model results without scrutiny, stressing the importance of maintaining a critical lens and clearly communicating any uncertainty.
"At the end of the day, I try to take a step back and think about what the model is supposed to be doing, what decisions is it influencing, what are those consequences," said Teixeira. "If a model can help free up some resources, whether it's people, time, or money spent, you have to argue those are really great things. However, what are the consequences of having something go poorly?"