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Could the use of personal data decrease inequality?

Posted By Felistus Mbole, Tuesday, August 20, 2019

Felistus Mbole a member of our Emerging Fellows program inspects the role of personal data in decreasing inequality through her new blog post. The views expressed are those of the author and not necessarily those of the APF or its other members.

The one thing that defines the digital revolution we are in today is the enormous volume of personal data that is generated and collected each day. Data generation is growing at an exponential rate. This is supported by the ever-increasing computational power particularly in mobile devices. The use of smart devices is increasingly becoming part of our everyday lives. Through them, we are leaving a digital trail in almost everything we do. According to the Next Generation Data Analytics report, the big data market is expected to grow from USD 28 Billion in 2019 to USD 66 Billion by 2025. The trend is clearly upward. What does the continued generation and use of personal mean for economic inequality? Can the benefits of big data be made more inclusive?

The key drivers of the big data era are the growing number of mobile devices and related applications, and organisational shift from analogue to digital technologies. According to the World Bank, today more households in developing countries own a mobile phone than have access to clean water or electricity. Furthermore, close to 70 percent of the bottom wealth quintile in these countries own mobile phones. Businesses and governments are becoming smarter each day. They are developing algorithms which enable them to analyse big data and make predictions with a much greater level of precision than would be the case with huge national surveys. This is making decision-making easier.

Governments now have access to a mass of large-scale data sets, and new data sources on previously ‘unknown’ populations. They are using big data to cost-effectively make predictions that enable them to provide better services to their citizens. For instance, healthcare professionals can use big data to calculate someone’s chance of suffering from a given disease and thus provide timely or preventive treatment. Big data has been used to increase financial inclusion, improve education, respond to epidemics, and mitigate the impact of natural disasters. Businesses on the other hand are using data freely collected from individuals to provide services and products that are more targeted at their clients. Using algorithms, they can more accurately anticipate behaviour. They are driving our future behaviour. This form of surveillance capitalism is making data companies much more profitable and driving the inequality between them and the rest of society.

The role of technology companies in making connectivity work for everyone in future is likely to remain. Yet the reality is that business decisions on investments are driven by the need to optimise returns. Thus, despite the dividends highlighted here, a digital divide between the rich and the poorer in society who cannot afford the latest technology is likely to persist. The poor and the digitally excluded have less or incomplete data which makes them excluded from services whose design is informed by machine learning. Additionally, the algorithms can be discriminative and biased. For instance, health insurance services algorithms use historical data which could have biases. Credit scoring algorithms use residential location and type of work which could further entrench one’s economic situation. These could sustain the prevailing global inequalities.

The economy of the future will be digital. Based on the current trajectory, big data and machine learning is likely to increase. As the revolution of big data and artificial intelligence takes root, there will be loss of jobs. The poor in society who do not have the requisite digital skills to engage in this big data economy are likely to be disadvantaged and excluded economically. This could increase global inequality. The digital divide between the richer and the poor could be closed by addressing the non-digital or analogue elements behind it. Adapting the skills of workers to the digital economy, the nationalisation of data, and effective regulation of business to ensure digital inclusion would help address this digitally driven inequality.

© Felistus Mbole, 2019

Tags:  data  inequality  technology 

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