Case study of digitization in india
Download as PPTX, PDF0 likes254 views
The document discusses the application of AI algorithms for big data governance in India through various case studies, including predictive policing, the Unique Identity Project (Aadhar), intelligent transport systems, and smart meters. Each case highlights the benefits and challenges, emphasizing aspects such as resource allocation, privacy concerns, and regulatory implications. Overall, it explores the impact of technology on efficiency and governance while raising questions about data protection and ethical considerations.
Case study of digitization in india
- 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