Machine learning, pivotal in various fields like personalized medicine and self-driving cars, raises privacy concerns due to data memorization. Models adjust parameters using past data, risking overfitting and memorization. Validation datasets help detect overfitting, but not memorization. Governments regulate data usage and promote privacy-preserving techniques. Digital surveillance, powered by machine learning, necessitates robust privacy measures.
Balancing machine learning’s power with privacy concerns is crucial, especially with sensitive data. Governments must navigate this balance to safeguard citizens’ privacy in the digital era, emphasizing the importance of digital governance and regulatory frameworks.
