Frontier

This frontier project establishes the Center for Trustworthy Machine Learning (CTML), a large-scale, multi-institution, multi-disciplinary effort whose goal is to develop scientific understanding of the risks inherent to machine learning, and to develop the tools, metrics, and methods to manage and mitigate them. The center is led by a cross-disciplinary team developing unified theory, algorithms and empirical methods within complex and ever-evolving ML approaches, application domains, and environments.

This frontier project establishes the Center for Trustworthy Machine Learning (CTML), a large-scale, multi-institution, multi-disciplinary effort whose goal is to develop scientific understanding of the risks inherent to machine learning, and to develop the tools, metrics, and methods to manage and mitigate them. The center is led by a cross-disciplinary team developing unified theory, algorithms and empirical methods within complex and ever-evolving ML approaches, application domains, and environments.

This frontier project establishes the Center for Trustworthy Machine Learning (CTML), a large-scale, multi-institution, multi-disciplinary effort whose goal is to develop scientific understanding of the risks inherent to machine learning, and to develop the tools, metrics, and methods to manage and mitigate them. The center is led by a cross-disciplinary team developing unified theory, algorithms and empirical methods within complex and ever-evolving ML approaches, application domains, and environments.

Natural language privacy policies have become a de facto standard to address expectations of notice and choice on the Web. Yet, there is ample evidence that users generally do not read these policies and that those who occasionally do struggle to understand what they read. Initiatives aimed at addressing this problem through the development of machine implementable standards or other solutions that require website operators to adhere to more stringent requirements have run into obstacles, with many website operators showing reluctance to commit to anything more than what they currently do.

The Center for Encrypted Functionalities (CORE) tackles the deep and far-reaching problem of general-purpose "program obfuscation," which aims to enhance cybersecurity by making an arbitrary computer program unintelligible while preserving its functionality.