ABSTRACT

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.

摘要

随着大量数据集的广泛提取，使用机器学习的人工智能有望解决诸如贫困，环境安全，食品生产，安全和疾病传播等社会经济问题。这些应用需要大数据促进发展，在其中可以最有效，最有效的方式解决社会问题，贫困，粮食安全和对气候灾害的对策。通过机器学习解决紧迫问题的勇敢新世界有几个阴暗面。正在创建数据鸿沟，从而将最脆弱的人群排除在所创建的解决方案之外，同时区分那些数据晦涩难懂的算法。复杂的数学模型与计算算法共同产生得分，这些得分用于评估群众的生活。这些系统已扩展到很大的比例，通过影响信用评分，工作前景和医疗保健机会来改变生活。公平，透明，成本效益和效率的承诺催生了强大的评分算法，该算法能够造成大规模破坏，同时区分最弱势群体。出现以下问题：发展的数据化导致了哪些不公正（不公正的类型）？如何减轻数据提取，分析和商品化引起的不公？谁可以访问私有数据并对其进行处理？提取个人数据是出于谁的利益或目的？通过在数据正义，微型企业家对ICT的使用，