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Data Doesn’t Lie, But it Can Mislead: How to Ensure Integrity of AI/ML Applications
A meaningful evaluation framework is crucial to the success of any Machine Learning (ML) application, yet structural biases often creep in unknowingly. When this happens, the results may not be as reliable as they initially appear…
This talk will identify common pitfalls and illustrate them with real-world examples from nearly two decades of experience in the data science field. We will explore the hidden story behind the performance metrics, moving beyond a single F-measure or accuracy score to delve into the intricacies of the data set and its domain. We’ll discuss how to identify artificial biases in your data and offer strategies for preventing them through rigorous design of your data collection and annotation processes.
Ultimately, this talk will provide a list of practical recommendations for building ML projects on solid foundations. Because amid the current AI boom and hype, we urgently need high-quality datasets, meaningful evaluations, and robust algorithms to ensure we are not just building elaborate sandcastles with GPUs.
About the Speaker
Sofie Van Landeghem is a machine learning and natural language processing (NLP) engineer with a strong belief in the transformative power of data for industry decision-making. She holds a Master’s degree in Computer Science and a PhD in Sciences (Bioinformatics), and brings nearly two decades of experience in NLP and machine learning, with a focus on applications in the pharmaceutical and food industries.
Sofie is passionate about open source, and has worked on the maintenance of various popular Python packages, including spaCy, Typer, and FastAPI. She also runs OxyKodit, a one-woman consultancy that delivers custom NLP solutions across a range of sectors. Her work is deeply rooted in quality assurance and testing, with a focus on clean abstractions, robust architecture, and modular maintainable code.
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