Join us as we explore the fascinating world of AI and uncover a hidden danger lurking beneath the surface of seemingly impressive and common performance metrics, specifically Accuracy (ACC).

In this episode, we'll explore the concept of the Accuracy Barrier (ACCBAR) performance indicator and why relying solely on accuracy scores can lead to a false sense of security.

We'll examine:

The Accuracy Paradox: Discover how a 99% accuracy rate can be utterly misleading and why conventional performance indicators fall short in certain scenarios.

Accuracy Barrier (ACCBAR): Uncover this groundbreaking novel performance indicator that unveils the limitations of Accuracy -the most traditional metric- and exposes potential biases in AI models.

Real-World Implications: Learn how ACCBAR can revolutionize performance evaluation in various domains, from cybersecurity to medical diagnosis, by providing a more reliable assessment of AI systems.

Publication Bias and Confirmation Bias in Research: We'll discuss how ACCBAR can help researchers identify and address potential confirmation bias in their classifications, ensuring more robust and trustworthy AI development.

Don't miss this opportunity to gain a deeper understanding of AI performance evaluation and learn how to critically assess the true capabilities of AI systems.

Free access to the full research paper is available at: https://bit.ly/ACCBARPaper

👉 Please cite my article as follows: Canbek, G., Temizel, T. T., & Sagiroglu, S. (2022). Accuracy Barrier (ACCBAR): A novel performance indicator for binary classification. 2022 15th International Conference on Information Security and Cryptography (ISCTURKEY), 92–97. https://doi.org/10.1109/ISCTURKEY56345.2022.9931888