The research area of Algorithmic Fairness and Data Privacy is focused on ensuring that data-driven technologies are both equitable and protective of individual privacy. Algorithmic Fairness aims to design algorithms that do not perpetuate biases, ensuring fair outcomes across different groups. This involves developing methods to detect and mitigate biases, creating fairness metrics, and enhancing the transparency and interpretability of AI systems to build trust and prevent discriminatory practices.
Data Privacy focuses on protecting individuals’ personal information while enabling its use for analysis and decision-making. Key areas include privacy-preserving techniques like differential privacy and federated learning, data anonymization methods to prevent re-identification, and ensuring compliance with privacy regulations such as GDPR. Additionally, robust security measures are essential to safeguard data from unauthorized access and breaches, ensuring that data remains protected both at rest and in transit.
These two areas often intersect, as achieving fairness in algorithms may require access to sensitive data that must be carefully protected. Similarly, privacy-preserving techniques must be designed to avoid introducing biases. Together, algorithmic fairness and data privacy are crucial for building AI systems that are both effective and ethical, fostering public trust and ensuring that technology aligns with societal values.
Faculty
Sebastian Angel
Andreas Haeberlen
Sampath Kannan
Michael Kearns
Benjamin Pierce
