From UPSC perspective, the following things are important :
Prelims level : Nothing Much
Mains level : Data monetisation aspects
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- There has been public furore over the delay in the release of data, for example farmer suicides, suppression of data such as on employment, bungled migration data in the Census, and controversy over the methodology used to calculate GDP growth rates.
- These data are the backbone of policy making in India.
Suggested Use of data
- These three — information obtained through the RTI Act, administrative data and data collected by the statistical machinery of government — are examples of “data as a public good”.
- But these are scarcely mentioned in a chapter so-titled in this year’s Economic Survey.
- Instead, its focus is on the expanding digital footprint of people, falling costs of data generation and storage and the growing data mining industry.
- The thrust is on how to monetise these data, for example by selling data that we share with the government in trust.
- Another worrying suggestion is consolidation of our data across various ministries.
Problems with Data usage
- Somewhere along the line, your mobile number and/or email ID got sold in the data market.
- Even as most of us delete these, others get trapped.
- A former Chief Justice of India was duped of ₹1 lakh recently as a result of a fraudulent email.
- In Mumbai, identity fraud was perpetrated by accessing personal data (address, phone number and Aadhaar).
- In phishing attacks in Rourkela, Odisha, fraudsters called bank customers asking for Aadhaar details to update their account, but used it to siphon off money.
2. Similar treatment like public services –
- The Survey treats personal data (such as date of birth, mobile numbers and addresses) the same way as data on rainfall, temperatures and road networks.
- In the examples above, the fraudsters had to get access to people’s data. The Survey is proposing that these be sold for a price.
- In early July, the Union Minister of Road Transport and Highways, Nitin Gadkari, informed Parliament that the department had earned ₹65 crore from the sale of vehicle registration and licence data.
- Imagine the consequences of your health data being sold to private health insurance companies; or your data on your earnings being sold, or data being used in the way Cambridge Analytica did.
If data can be toxic, centralising and consolidating it, as advocated by the Survey, increases its toxicity exponentially.
1.Centralising the data –
- Contrary to the widely advocated principle of decentralised/disaggregated data silos as a first line of defence by data security experts, the Survey portrays decentralisation as an obstacle.
- With decentralised data, data mining companies employ sophisticated tools to combine distinct data silos to create profiles of individuals.
- Consolidating it, for example if a unique number such as Aadhaar links them, reduces the company costs for profiling and targeting.
- Centralising it (in one data silo) means that a single data breach can compromise all aspects of your life.
2. Without Consent –
- Often they are collected and shared without our consent or knowledge, for example, CCTVs or web browsing histories.
- When our data are used by opaque algorithms to make crucial decisions about our lives, such as shortlisting for jobs, getting health insurance or whether you were speeding, we cannot question them.
Case study of Aadhar –
- Given the government’s track record on Aadhaar, these laws are unlikely to protect citizen’s rights adequately.
- Further, privacy and data protection laws will face unique implementation challenges in India.
- This is on account of low levels of tech-digital and legal literacy combined with pre-existing social inequalities which directly bear upon power relations between us (as citizens/consumers) and them (government/corporations).
- Even where such laws have been put in place, those societies/economies are grappling with the fallout of corporations whose practices can best be described as “digital kleptocracy”.
- To understand this, take the example of lending and credit scores.
- The literature documents unscrupulous use of algorithms to identify vulnerable targets such as search histories of single African American mothers in the United States that are used to sell them home or education loans which it is clear they are unlikely to be able to repay.
- Thus, digital kleptocracy is a means by which rich tech companies mine poor people’s data,in fact, steal; in most cases the person is unaware of their data being harvested and used for profit.
- What the Economic Survey advocates is not only for the government to facilitate such practices but also climb aboard this bandwagon of digital kleptocrats.