My work brings me in contact with the cutting edge in science research and the developments in public policy. I find one common thread when interacting with people in these fields. The question of how can we make more use of large public data while ensuring privacy of the individuals involved. Large amounts of specific types of data can be useful to both scientists and policy makers in myriad ways. But currently there exists no policy framework one could use to take the public in confidence and simultaneously leverage data without conflict of interest. This problem is an underrated one. Steps taken towards such a policy initiative can be one of the defining intellectual exercise of the 21st century reaping huge benefits for science and policy spheres. And eventually these benefits can be put to used for many purposes. The building of this framework needs grounded knowledge of data science, computer science and public policy to make sure it is robust in every sense. I am an engineer who has built machine learning systems at scale and now I am transitioning into a data journalism and policy career. My past and current experience gives me a unique vantage point to look at this problem. It would be a honour to express myself at Data_Science Conference.

1. Building a robust framework for fair and
impactful usage of public/open data
Abhijeet Katte
Data Scientist and Product Lead
MachineHack and Analytics India Magazine
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2. The Story Of A
Crime Data In A
Town
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3. What Is Open Data?
● Data which is freely available to everyone
● Free of restrictions, copyrights and patents
● Open data can be released by both government and private institutes
● Universal participation of stakeholders
● Emphasis of re-use and re-distribution
● Interoperability
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4. Why Is Open Data Important?
● Citizen and civil society participation in administration
● A solution to information asymmetry
● Open Data + Creativity = Innovation
● Transparency for local and national governments
● Important insights
● According to a McKinsey report $3 trillion in economic value can be unlocked.
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5. Applications
● Healthcare
● Transportation
● Criminal Justice
● Elections
● Housing
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6. Problems With Open Data
● Increase in digital divide and social inequality
● Technical formats of open data
● Privacy
● Costs involved
● Personally Identifiable information and the mosaic effect
● Lack of clear policy approach
● Paywalls
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7. The Most Important Question That Remain
● What are the technical and policy approaches for maximising the benefits of open
data?
● How to minimise the risks related to microdata?
● What are the potential benefits of microdata and how it can be used ?
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8. The Two Big Elephants : Privacy and Impact
● Privacy and Open data
● Impact and Open data
● The Indian ADHAAR story and the two elephants
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9. Framework: Striking A Balance
● Between openness and privacy protection
● Between data utility and privacy protection
● Between avoiding the mosaic effect and having an impact
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10. Framework : Solve For Impact
● Incentivise re-use
● Enhance developer engagement
● Choosing great measurement techniques / SLAs
● Better data not more data (thinking of data collection methods)
● Better formats
● The API question
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11. Framework : Solve For Fairness
● Bringing in advanced machine learning
● Advanced cryptography solutions for open data
● De-identification and anonymization
● Risk-benefit analyses
● Process-oriented assessment of lifecycle (Have we done enough)
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12. Framework : Bringing them both together
Framework diagram here
Why a framework is needed and why use this framework
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13. The Power Of
Privately Open
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14. My Experience: Data Scientist, Product Guy
and a Journalist
● Experience of interacting to researchers : their perspective
● Experience of interacting to startups : their perspective
● Experience of interacting to startups : their perspective
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15. Questions