India is the best case study of digitization to nano level. The demographic divide is a wonderful subject matter for anthropologists. Using Data Science and Artificial Intelligence in such a huge population and area is noteworth.

1. AI Algorithms for Big Data
in Governance in India: Case
Studies world is looking into
Dr Neeta Awasthy
Technology Evangelist

2. Case Study I: Predictive Policing
 CCTNS (Crime and Criminal Network Tracking System) is an e-governance project
under the Digital India mission connecting 14000 police stations.
 Predictive Policing usually works in the following four ways
 Predicting places and times with an increased risk of crime,
 predicting potential future offenders,
 creation of profiles for past crimes, and
 predicting groups of individuals likely to be victims of crimes.

3. PREDICTIVE POLICING PROCESS
 Step 1. Data Collection
 Step 2. Analysis (Predictive
Model, Repeat Theory, Social Network Analysis & Regression Model)
 Step 3. Police Intervention
 Step 4. Criminal Response

4. Problems vs Benefits
 Inherent biases
 Opacity of predictive models
 Better allocation of resources
 Preventive Policing
 More holistic analysis

5. Case Study II: The Unique Identity Project
 As per official Aadhar database, 1.21 billion holders as of May 2018
 CIDR
 Seeding

6. 1. Seeding
 Ginger Platform
 Manual or Algorithmic Aadhar Seeding
 https://www.youtube.com/watch?v=3MTp5euNzM4

7. Benefits
 Reducing frauds
 facilitating financial inclusion
 providing for efficient delivery of services
 enabling political empowerment
 facilitating economic growth
 security

8. Open Questions
 Lack of data protection regulation
 Convergence
 Technological failure

9. 2. Cradle to Grave

10. Pros and Cons
 Efficient service delivery
 Convenience for the citizen
 Better fraud management
 Better information dissemination and training
 Profiling
 Lack of trust
 Knowledge gap

11. 3. Indiastack

12. Pros
 Presence-less use
 Speedy and more efficient transactions
 Reduce fraud
 One stop decentralized privacy control

13. Cons
 Complete loss of anonymity
 Potential denial of financial agency
 Predatory practices
 Doubts over the consent layer
 Regulation by code

14. Case Study III: Intelligent Transport System
 Overview:

15. Sources of Big Data in Transport :
 Vehicle Tracking System
 Passenger Information System
 Mobile Application- App collecting PI –
 SMS,
 Camera,
 wi-fi connection information,
 device ID
 and call information
 Electronic Ticketing System
 Call Data Records (CDR)

16. Potential users of Data

17. Open Questions!
 Privacy
 Exclusion

18. Pros
 For Organisations:
 Reduce project costs
 Incident management
 Promotes reliability on transports
 Reduce traffic congestion
 For Individuals:
 Improved user experience
 Targeted services
 Reduction in traffic congestion in cities
 PIS

19. Cons
 Privacy and data security
 No or inadequate Privacy Policies
 No opt-out
 Unplanned use of data
 Lack of accountability and transparency
 Data quality
 Exclusion

20. Case IV: Smart Meters
 Project Conceptualisation
 & Objective

21. DATA COLLECTED IN REAL TIME

22. Regulatory Response
 Deregulation for innovation
 Regulation for public interest
 Regulation to encourage technical measures that mitigate harm
 Regulation for interoperability

23. Pros
 Efficient Demand Side Management (DSM)
 Accurate shaping of the market and industry
 Security of supply

24. Cons
 Unintended behavioral analysis tool
 Social polarisation
 Social dumping

25. Contact me @
 drneetaa@gmail.com
 LinkedIn
 Thanks