The objective of this focus area is to develop recommendations and best practices for Responsible Data Science (RDS) by design, with security, privacy and ethical use of data and data science methods. This includes:
1. Enabling the development and dissemination of responsible data science best practices, such as building and deploying of integrative data equity systems, incorporating ethical and legal norms in all stages of the data science life cycle, with security, privacy and ethics
2. Engaging IEEE standards development activities for the TIPPSS framework – Trust, Identity, Privacy, Protection, Safety and Security – and its use in clinical Internet of Things, connected healthcare, and smart and connected communities
3. Collaborating with cybersecurity researchers and domain scientists to determine their needs and challenges regarding trustworthy data, and data driven cybersecurity, privacy, and ethics
Current Projects on Responsible Data Science
Trustworthy Data Working Group (External)
Connected Healthcare Cybersecurity Workshop Series
Cybersecurity Risk Initiative (2019)
Framework for Integrative Data Equity Systems (External)
Data Sharing and Cyberinfrastructure Working Group
Upcoming Responsible Data Science Events
Trusted CI Webinar Series
Data Sharing and Cyberinfrastructure Working Group Meeting
2021 NSF Cybersecurity Summit
Workshop on Data for AI in Network Systems
Trusted CI Webinar Series
Big Data Summit 2021
Trusted CI Webinar Series
Responsible Data Science Career Opportunities
The Data Science Institute (DSI) at Columbia University invites applications for the position of a Data Post-Doctoral Scientist. The post-doc will work on new methods for scalable and privacy-respecting digital identity systems. These systems will provide digital identities suitable for low-infrastructure environments, used to facilitate access to resources such as medical care, education and food assistance. These highly secure systems will provide unprecedented new levels of resistance to identity theft.
Data Science Institute at Columbia University is a world-leading institute in research and education in theory and practice of the emerging field of data science. To drive both innovative approaches and best practices for management of an ambitious Trustworthy AI research agenda, the Institute invites candidates for the Technical Project Manager position.
The Data Science Institute is looking for a Staff Associate III (Research Software Engineer) eager to work in an academic environment towards this goal, at the cutting edge of probabilistic programming, causal inference, program synthesis and machine learning. Specifically, this role is to develop and engineer algorithms, languages and systems for causal probabilistic programming.
Microsoft is seeking an experienced Data Analyst Program Manager to join their team and deliver world-class analysis and visualization solutions. In this role, the candidate will collaborate with Microsoft’s societal programs, public policy, and product teams to understand their goals and deliver impactful analysis, insights, and PowerBI visualizations. Candidate experience in any of the following, preferred: cybersecurity, threat intelligence, identity systems, fraud, or abuse.
Responsible Data Science Resources
EE DataPort Open-Source Datasets – Security
The Business Case for Security, Cybersecurity & Infrastructure Security Agency
Responsible Data Science Success Stories
How to Innovate AI Procurement?
Guest post by Mona Sloane, Ph.D., New York University Artificial intelligence (AI) systems are increasingly deployed in the public sector. As these technologies can harm citizens and pose risk to society, existing public procurement processes and standards are in urgent need of revision and innovation. This issue is particularly pressing in the context of recession-induced […]
Researchers from NYU Tandon release 3-D data tracking human interactions outside of coronavirus hotspots
Study to set groundwork to build machine learning models that rapidly analyze how a virus spreads In April when New York City was under a strict lockdown, a team of 16 student researchers from New York University’s Tandon School of Engineering commenced a National Science Foundation Rapid Response Research (RAPID) grant-funded project to observe potential […]
Water Data and Software Services to Support Discovery, Reproducibility, and Collaboration in the Water-Resources Domain and Beyond
Guest post by Emily Clark, Project Manager, CUAHSI The mission of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) is to enable interdisciplinary collaboration in the water sciences, provide critical cyberinfrastructure, and promote water science education at all levels. CUAHSI’s services can be especially useful in supporting the Northeast Big Data […]