Our early insights suggest that the purpose of the steward, or the problem it is intended to solve, will define which governance stewardship framework to adopt. The Data Economy Lab, a collaborative effort between Aapti Institute and Omidyar Network, has studied over 100 existing models of data stewardship and are in the process of drawing out lessons about how to approach more operational questions for designing models across sectors. Each of these models have their own logic, rules and governance systems defined by their own peculiar context. Data trusts can be used for multiple purposes, from unlocking the value of data to solving societal problems, to helping groups use data for collective empowerment. A trust entails a legally defined board of trustees that exercises delegated consent on behalf of the people/community that are part of the trust and is committed to taking decisions in their best interest. they aim to empower collectives to draw more value from their data. The cooperative model can be used to generate societal value or help groups evolve mechanisms of participative governance and negotiate better on issues of data rights.ĭata trusts have a similar function to cooperatives i.e. Similarly, Driver’s Seat, a driver-owned cooperative, collects and sells mobility data from drivers to city governments and research institutes to create value for society and people. This model aims to empower individuals and help them extract value from the data they produce.ĭata cooperatives, such as the MiData platform, which shares medical data for research, allow members to collectively govern the use of their data. is a personal data store that aggregates individual data from multiple sources and allows subjects to share it with the companies they choose. For instance, personal data stores enable individuals to seek benefits by sharing their data data cooperatives can ensure that groups make data-related decisions in the interest of the collective data trusts revolve around a legally entrusted group that makes decisions on behalf of data principals/subjects.Įxamples of data stewardship models are emerging globally. There are several ways in which data can be stewarded with greater participation from the people whose data it is – data subjects. One possible mechanism to tilt authority in the favour of migrant workers, in this case, and those of vulnerable communities more broadly, is through data stewardship – a set of governance, legal and technological instruments to enhance data subjects or individual and community’s agency and decision-making over data governance, while harnessing data for societal impact. This social and data injustice needs to be addressed to enable workers to bargain better with the State and the private sector. This points to the fact that data is fundamentally about power, its collection is used for the exertion of control, surveillance and its absence is used for exclusion. On the other hand, migrant workers’ vulnerabilities are accentuated when relevant data about them is unavailable during emergencies. On one hand, migrant workers are in unequal and data-extractive relationships with the State, regularly parting with personal details, including biometric identification, in return for services like subsidised food grains or the right to work. This absence of reliable information renders the migrant’s experience invisible and points to an imbalance of power in the data economy. However, it lacked proper consent protocols, did not offer migrants control over their data and was unable to deploy data to provide better care to the workers. For example, the India Observatory dashboard run by a civil society organisation, the Foundation for Ecological Security (FES), presented data on movements of migrants in real-time as well as information on critical facilities and relief measures. There have been varying efforts to consolidate data for migrants across the country during the health emergency. Faced with the reality of no work or income, many took arduous journeys on foot in the absence of trains or buses. In a shocking admission in late April this year, the Government of India acknowledged in Parliament that no data was available on the number of migrant deaths due to the harsh lockdown the country imposed in response to the COVID-19 pandemic, which forced millions of migrant workers to move back to their homes at very short notice. It can help rethink how the value of data is defined and distributed, and create mechanisms for people to negotiate on their data rights. Data stewardship is a relatively new, but increasingly popular concept that represents both a fundamental rehaul of existing top-down decisions on data, as well as more functional practices on how this can be done.
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