Focus on Responsible Data Science: Security + Privacy + Ethics


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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

Submit your project here


Upcoming Responsible Data Science Events


Responsible Data Science Professional Opportunities

Additional opportunities will be listed here as available.


Responsible Data Science Career Opportunities

Data Post Doctoral Scientist, Columbia University Data Science Institute

Staff Associate III (Research Software Engineer), Columbia University Data Science Institute

Senior Data Analytics Program Manager, Microsoft

Technical Project Manger, Columbia University Data Science Institute


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 This Success Story is a report on the results of the Northeast Big Data Innovation Hub’s 2020 Seed Fund program. 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 […]

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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 […]

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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 […]