While digital social protection systems have been claimed to bring efficacy in user identification and entitlement assignation, their data justice implications have been questioned. In particular, the delivery of subsidies based on biometric identification has been found to magnify exclusions, imply informational asymmetries, and reproduce policy structures that negatively affect recipients. In this paper, we use a data justice lens to study Rythu Bharosa, a social welfare scheme targeting farmers in the Andhra Pradesh state of India. While coverage of the scheme in terms of number of recipients is reportedly high, our fieldwork revealed three forms of data justice to be monitored for intended recipients. A first form is design-related, as mismatches of recipients with their registered biometric credentials and bank account details are associated to denial of subsidies. A second form is informational, as users who do not receive subsidies are often not informed of the reason why it is so, or of the grievance redressal processes available to them. To these dimensions our data add a structural one, centred on the conditionality of subsidy to approval by landowners, which forces tenant farmers to request a type of landowner consent that reproduces existing patterns of class and caste subordination. Identifying such data justice issues, the paper adds to problematisations of digital social welfare systems, contributing a structural dimension to studies of data justice in digital social protection.
翻译:虽然数字社会保护系统据称提高了用户身份和权利分配的效率,但其数据公正的影响却受到质疑,特别是,基于生物鉴别技术的补贴的发放被认为放大了排斥,意味着信息不对称,并复制了对接受者有负面影响的政策结构;在本文件中,我们用数据公正透镜研究印度安得拉邦针对农民的社会福利计划Rythu Bharosa;虽然据报告该计划的覆盖面在接受者人数方面很高,但我们的实地工作揭示了三种数据公正形式,需要监测预定接受者。第一种形式与设计有关,因为接受者与其登记的生物鉴别证书和银行账户细节的不匹配与拒绝补贴有关。第二种形式是信息,因为没有获得补贴的用户往往没有被告知其原因,也没有被告知他们可以利用的冤情纠正程序。我们的数据增加了一个结构层面,其核心是补贴对土地所有者的批准条件,迫使租户要求一种土地所有者同意,这种同意复制现有的阶级和种姓从属模式。第二个形式是与设计有关的,因为接受者与拒绝补贴有关。第二个形式是信息形式,因为没有获得补贴的用户往往没有被告知补贴的原因,或者没有被告知他们可以利用冤情纠正程序。关于数字保护的社会问题。