The Museum of Digital Society: A Living Archive of Digital Inclusion
This is one of the narratives of the communities impacted from data injustice which are published in the research report on data justice by Digital Empowerment Foundation
The injustices that algorithms of platform and gig-economy apps cause has been documented previously. In India, the workers in the gig-economy are counted as “clients,” depriving them of many protections labour laws provide. In such an unorganised sector, Shaik Salauddin of the Indian Federation Of App Based Transport Workers (IFAT) is one of the leaders organising and unionising people working in ride-hailing and delivery apps. We speak to him in detail about the algorithms that cause injustices.
In December 2019, the Indian Parliament passed the controversial Citizenship Amendment Bill, along with the government’s commitment to enforce a National Register of Citizenship. As Booker Prize winning author and activist Arundahti Roy put it, “Coupled with the Citizenship Amendment Bill, the National Register of Citizenship is India’s version of Germany’s 1935 Nuremberg Laws, by which German citizenship was restricted to only those who had been granted citizenship papers—legacy papers—by the government of the Third Reich. The amendment against Muslims is the first such amendment.” Noting the use of an automated tool to decide the lineage of people in Assam, we spoke to Abdul Kalam Azad , a researcher from Assam, now at Vrije Universiteit Amsterdam, who had looked into detail the issues and exclusions created by the NRC in Assam. Learning of exclusions of Trans People from the same list, (already facing an undemocratic law like the Trans Act), we spoke to two activists from the Trans Community, Sai Bourothu, who had worked with the Queer Incarceration Project and the Automated Decision Research team of The Campaign to Stop Killer Robots, and Karthik Bittu, a professor of Neuroscience at Ashoka University, Delhi and an activist who had worked with the Telangana Hijra, Intersex and Transgender Samiti.
Another exclusion we noted in our primary research was the homeless in any of the data enumerations. We spoke to Jatin Sharma and Gufran, who is part of the Homeless Shelter in Yamuna Ghat on these exclusions and how it leads to the homeless people being denied basic healthcare and life-saving TB treatment.
Four researchers, activists and civil society leaders who had done considerable work on data related exclusions, surveillance, and identification software such as the Aadhar offered their perspectives on the debates, conversations and potential reimaginings of data injustices. Srinivas Kodali, independent activist and researcher; Nikhil Dey, of the Mazdoor Kisan Shakti Sangathan; Apar Gupta, lawyer and director of the Internet Freedom Foundation, and Rakshita Swamy, an NLU professor who also heads the Social Accountability Forum for Action and Research were the people who provided their insights.
Citizenship and other Registers of Data Injustices: A Case from Assam
Interviews on the National Registry of Citizenship brought out the nuances of building an AI-powered system to determine the citizenship status of a population with a muddled history of colonialism and anti-immigrant sentiment. Wipro11 deployed a Document Segregation and Meta Data Entry (DOCSMEN) software to digitise legacy data development of 39 million applicants in 2014. 1.9 million were excluded from the final list. The interview also pointed out that the 4 million people who did not have an Aadhar card 12 India’s UID, were promised an Aadhar card after the NRC process, but continue to be excluded from all the entitlements and
schemes linked to Aadhar. The government has already collected the biometric data, yet none of them knows what it is used for, nor can they reapply for a different Aadhar card as their application is “under process” for years. The interview also highlighted how the software-generated “family tree” system that verifies one’s legacy data violated the basic human rights of hundreds of thousands of people who were involved in this process, either excluded from or included in the list.
An example was pointed out by a respondent who belongs to the Bengali Muslim community of Assam, seen largely scrutinised and victimised in the NRC Project. The Muslim immigrant community of Assam was brought into the State by the colonial administration as labourers to increase the revenue in 1826. They were brought from East Bengal – which later became East Pakistan and then Bangladesh 13 . The inclusion in the NRC list was based on something called the ‘legacy document’. One needs to mention an ancestor who was included in an NRC done in 1951 or in the voter’s list of 1966 to be in the NRC list. The legacy document should have the name and address of the ancestor, the precise address they were residing in and the precise details of everyone who is part of that family from that particular ancestors’ generation. Our respondent explained the enormity of the data one had to present and how the ‘family tree’ algorithm excluded several in this process. One family tree will have hundreds of people if they are basing it on their grandfather, including cousins and nephews. And each of these hundred people had to keep the matching spellings, including the spelling of the address, otherwise, the algorithm would exclude them from the list. Mild variations lead to exclusion and the grievance redressal process was reportedly even more vicious. Hundreds of these extended family members had to appear together before the tribunal to prove that they all belong to the same family. Our respondent pointed out that their plight is further complicated by the fact that the literacy rate of these regions- mostly floating islands, is as low as single digits.
Another important aspect pointed out in this is how the legacy codes given by the NRC Seva Kendra (service centres) led to the exclusion of several families. The applicants who were unsure about the address and other details of the “legacy source person” could go to the Seva Kendra to get a legacy code by providing their names and their legacy person’s name. The code contains all
the data about that particular person. However, if two families have the matching names of two of their ancestors, both the families would end up using the same codes for the legacy document. In the case of Assam NRC, many families had to fight each other in the tribunal to prove that the disputed ancestor was theirs. Our respondent recollected how, often, one of the families ended up losing the dispute and was excluded from the list.
According to the same respondent, the entire process of NRC citizenship contestation in the Assam State of India is built on a set of biased data: the D-voter list (the doubtful voter’s list), the Assam NRC of 1951 and the ‘reference cases’ registered by the border police. Firstly, the 1951 Assam NRC was partial and several people were excluded from the list. The river islands of Assam that disappeared during the floods were only partially covered in the first NRC. These islands are largely populated by Bengali Muslim immigrants. Secondly, there were multiple people with the same names and ancestral names. If one of them happened to be in the reference case list or the D-voters list, all of them ended up getting excluded. The border police, deployed widely in Muslim dominant districts, has the right to search and collect the fingerprints of any ‘doubtful’ person.
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