With the widespread extraction of very large datasets, artificial intelligence using machine learning hold the promise to address socio-economic problems such as poverty, environmental safety, food production, security and the spread of disease. These applications entail Big Data for Development in which social problems, poverty, food security and responses to climate disasters can be solved in the most efficient and effective manner. This brave new world of solving pressing problems through machine learning has several dark sides. A data divide is being created that leaves the most vulnerable populations out of the solutions being created while discriminating against those whose data is churned by obscure algorithms. Complex mathematical models together with computing algorithms produce scores that are used to evaluate the lives of the masses. These systems have scaled to enormous proportions, changing lives by affecting credit scores, job prospects and access to healthcare. The promise of fairness, transparency, cost-effectiveness and efficiency gives rise to powerful scoring algorithms that have the power to create mass devastation while discriminating against the most vulnerable. Questions arise as to: What injustices (types of injustice) are created by datafication of development? how can the injustices caused by the extraction, analysis and commoditization of data be alleviated? Who has access to and what is being done with private data? And for whose benefit or purpose is personal data being extracted? Such questions are explored through the contributions in on data justice, the use of ICTs by micro-Entrepreneurs, mobile money and financial inclusion offered through papers in this issue.
ASJC Scopus subject areas
- Public Administration
- Computer Science Applications