{"id":684,"date":"2021-11-02T22:32:05","date_gmt":"2021-11-02T22:32:05","guid":{"rendered":"https:\/\/wpmu.mau.se\/nmict21group10\/?p=684"},"modified":"2022-05-11T12:37:09","modified_gmt":"2022-05-11T12:37:09","slug":"why-data-justice-can-be-the-starting-point-in-development","status":"publish","type":"post","link":"https:\/\/wpmu.mau.se\/nmict21group10\/2021\/11\/02\/why-data-justice-can-be-the-starting-point-in-development\/","title":{"rendered":"Why data justice can be the starting point in development"},"content":{"rendered":"
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It has become worthless to point out that algorithmic systems increasingly permeate the social sphere and day-to-day life. \u2018Smart\u2019 technologies, machine learning and Artificial Intelligence are now an integral part of how societies are organised. The way data is generated, collected and used, then, has become an increasingly prominent issue.<\/p>\n

How do we come to understand the world? What services we are able to access? Where we can go? In which way we are governed? It\u2019s likely all feature data practices that shape the terms and conditions for our participation in society (Dencik, Hintz, Redden & Trer\u00e9, p. 873).<\/p>\n

Datafication of devolopment<\/b><\/h4>\n

The widespread adoption and use of ICTs in the context of development is seen to bring opportunities<\/strong> for improving the people\u2019s lives (Qureshi, p. 202). Complex socioeconomic problems such as poverty, environmental safety, food production, security and the spread of disease are automated and packaged as mathematical and engineering problems.<\/p>\n

But, as Harcourt (2015) describes it, for data systems to be scalable to generate sufficient meaning, there is a necessary trend to reduce social identities, mobilities and practices to data<\/strong>.\u00a0<\/span><\/p>\n

The \u2018datafication of development<\/strong>\u2019 can be described in terms of a growing volume, velocity, variety and visibility of data, with greater use of new forms and streams of data in decision making (Heeks, 2018 cited by Heeks, Shekhar, p 993).<\/p>\n

A tendency to turn vast amounts of activity and human behaviour into data points<\/strong>, that risk to be managed and sorted as abstractions without a clear understanding of the embodied power relations and social effects produced by those activities (Monahan, 2008; Costanza-Chock, 2018 cited by Metcalfe, Dencik).<\/p>\n

Data-driven decision-making<\/b><\/h4>\n

With these developments, we are confronted with a significant shift<\/strong> in governance. Policymakers and researchers worldwide are taking advantage<\/strong> of the increasing availability of digital data – what the UN has termed the \u2018data revolution\u2019 (DataJustice Project). \u00a0<\/span><\/p>\n

From being data-informed to being data-driven<\/strong>, the turn to data collection and algorithmic decision-making is not simply a question of quantity. A quality shift is required. These data sources have ethical, political and practical implications for the way people are seen and treated by the state and by the private sector (Kitchin, 2016 cited by Taylor, p. 1).<\/p>\n

Although this is often welcomed as \u2018revolutionary\u2019 in its potential for enhanced efficiency, security and innovation, it sure does increase concerns with the societal<\/strong> implications<\/strong> (DataJustice Project).<\/p>\n

The dark side of the ‘revolution’<\/b><\/h4>\n

\u201cThis brave new world of solving pressing problems through machine learning has several dark sides\u201d (Qureshi, p. 201).<\/p><\/blockquote>\n

It is undeniable that data has been essential to both delivery and measurement of the Sustainable Development Goals. Unlike the predecessor Millennium Development Goals, the SDGs appear less problematic<\/strong> when it comes to implementing policies and procedures directed at achieving these goals, thanks to these new data<\/strong> (Qureshi, p. 204, 205). As mentioned, ICT use seemed to present great opportunities for advancing people\u2019s rights and representation.<\/p>\n

But what about those who do not have access<\/strong> to or use ICTs? Their data cannot be extracted nor analyzed, they remain under-represented in digital datasets and become invisible to policymakers and agencies working to implement development efforts. On the other hand, refugees\u2019 every move<\/strong> is tracked and their behaviours analysed, producing modes of mapping that are \u2018colonial\u2019 – difficult to criticize since the assumption is the data analysis\u2019 ability to predict and optimize migration and its implications (Qureshi, p. 205)<\/p>\n

The transition to greater visibility creates informational and power asymmetries in the form of surveillance<\/strong> and the potential for manipulation<\/strong> and\u00a0exploitation<\/b>\u00a0(Dencik, Hintz, & Cable 2016). Heeks and Renken (2016) refer to the disbenefits<\/strong> developmental impacts of datafication which include\u00a0<\/span><\/p>\n