Mojtaba Farmanbar
He is a data scientist at KNAB with over 6 years of experience in the financial services industry. He currently works for KNAB and prior to this, he worked at ING as a data scientist across various domains, including commercial, people analytics, and financial crime and fraud.
He holds a PhD in computational physics and is passionate about conducting research in Trustworthy AI and Generative AI topics.
Sessions
Uncertainty identification in machine learning is crucial for making robust decisions, enhancing model trustworthiness, and assessing risks. By quantifying and understanding uncertainty, machine learning practitioners can build more reliable and trustworthy AI systems.
Imagine having a machine learning model that predicts whether a given image contains a cat or not. While traditional machine learning approaches provide binary predictions (cat or not cat) for each image, you do not know the confidence level of the model in each prediction.
Conformal prediction (CP) is a machine learning framework for uncertainty quantification that adds a layer of confidence estimation to model predictions. Instead of just giving a binary answer, it provides a range of possible outcomes (prediction sets) along with a measure of how confident it is in each outcome. These prediction sets come with coverage guarantees for the true outcome, ensuring that they will detect at least a specified percentage of true values. Importantly, conformal prediction is agnostic to the underlying machine learning model, and it makes no assumptions about the underlying data distribution. In other words, it is a model-agnostic and distribution-free approach.
As a result, conformal prediction offers a robust framework that empowers stakeholders to make more informed decisions, particularly in high-stakes domains such as healthcare, finance, and autonomous systems