Parity Bit

Parity bits, representing the evenness or oddness of a binary sequence, are fundamental in data integrity and machine learning. Current research focuses on understanding the computational complexity of learning parity functions, particularly in noisy environments and within the context of fairness constraints, exploring the compatibility between individual and group fairness in parity-based models. These investigations utilize various approaches, including gradient-based methods for learning and online binary calibration techniques for improved prediction accuracy. The findings have implications for cryptography, fair machine learning, and time-series forecasting, impacting the design of robust and equitable algorithms.