This community is interested in understanding and affecting the interaction between computing systems and society through engineering, computer science and public policy research, education, and public engagement.
Researchers within this CoR collaborate with policymakers and technologists to improve the trustworthiness and effectiveness of interconnected digital systems such as the Internet, and to evolve new legal and regulatory structures appropriate to these systems. Our work provides global policymakers with technically grounded guidance for policies regarding cybersecurity, Internet privacy, and more. By learning from policymakers and legal scholars, we will develop a deeper understanding of the social requirements of systems. At the same time, our computer science and engineering research introduces new architectures, analytic techniques, and technical affordances to make the computing infrastructure used by the world more responsive to societal needs.
We are developing a general framework that enforces privacy transparently enabling different kinds of machine learning to be developed that are automatically privacy preserving.
We aim to better understand the features of network protocols that facilitate denial of service attacks, in order to design more robust protocols and architectures in the future and evaluate existing designs more accurately.
MIT professor and CSAIL member Gerald Jay Sussman has been awarded the IEEE Educational Activities Board Major Education Innovation Award for his “Structure and Interpretation” course, textbook, and approach.
MIT researchers and industry partners release white paper on designing accountable, traceable systems to enable respectful handling of personal financial data.
Growing from a strong foundation built at MIT CSAIL and other academic hosts, W3C will continue its mission of developing standards for an open and equitable web.
Cybersecurity experts from MIT and The United States Federal Reserve held the “Measuring Cyber Risk in the Financial Services Sector” conference to discuss efforts to quantify risk across the financial system.
Inspired by unintended negative consequences of NLP models found to reflect societal biases such as gender and racial biases, an MIT researcher created a decision-making framework to help scientists align their NLP research with social good
