Using Data Responsibly

This chapter has detailed a handful of examples of the many innovative applications of big data and AI being used to inform sustainable development and humanitarian work globally (see table 3.1 in particular), illustrating the value of this technology for development actors.

The pervasive nature of big data and the rapidly evolving capabilities of AI hold tremendous promise for social impact and can drive transformation across many domains, ranging from health, to food security, to jobs, and action on climate. Scope therefore exists to expand use of this technology beyond current applications, leveraging big data and AI in new ways that help us achieve the 2030 Agenda. National and international development actors should prioritize operational integration of these digital innovations into policy and practice. Doing so will allow them to craft more agile and responsive programming, to support anticipatory approaches to managing risk, and to find new ways to mea sure social impact. However, mainstream, scaled adoption by policy makers and communities themselves still faces systemic barriers and pervasive inertia.

Given their broad applicability, big data and AI necessitate new forms of interinstitutional relationships to leverage data and computational resources, human talent, and decision-making capacity. The capabilities of a diverse set of stakeholders can enable the integration of data innovation into ongoing policy processes rather than one-time policy decisions.

Moreover, as adoption of big data and AI increases and the technology evolves, so do the potential risks and issues that need to be resolved. Many question the suitable application of this technology, including malicious use, and highlight the risk of unintended consequences in this rapidly evolving field, where policy makers may struggle to keep pace. Although both the supply of and demand for data are expanding at "warp speed," the data ecosystem, as we know it, is still embryonic – with many advanced potential applications still more theory than practice. As new capabilities and data sources are applied for good – whether to create smarter public services, better early warning systems, or more effective responses to crises – development actors must pause to consider the potential for harm that may arise, for example, from inadequate privacy protection.

To date, no standards exist for the anonymization and sharing of insights from big data in priority industries such as financial services, e-commerce, and mobile telecommunications – although the latter has done work to develop such standards. At the same time, as noted, nonuse of these new capabilities and data sources represents at least as great a risk of harm to the public as that potentially arising from inadequate privacy protections. New frameworks are needed that go beyond privacy and ensure accountability and responsible use and reuse of data for the public good. Principles such as responsibility, accuracy, auditability, and fairness should be core concepts that guide the development of algorithms and AI. The "society-in-the-loop" algorithm concept, for example, proposes to embed the "general will" into an algorithmic social contract in which citizens oversee algorithmic decision-making that affects them.

Developing countries may have the most to gain from the use of new data sources and tools. However, without thoughtful application and critical complements they may also stand to lose the most. To reap the societal benefits of AI – including expected improvements to productivity and innovation – countries must have access to the data, tools, and human expertise necessary to support their application, as well as viable plans to address the likely displacement of workers. The availability of data is to a large degree a by-product of digitization, an area in which developing countries lag far behind. There can be no mass digitization without universal and affordable access to broadband. According to International Telecommunication Union (ITU) statistics, some 3.8 billion people, or just over half the world's population, were still lacking access to the internet in 2017.

The way forward must be inclusive. For the big data and AI revolution to benefit the most vulnerable people, current AI research roadmaps must increase attention to methodologies that can work in data-scarce environments, that can be adapted quickly and with few examples – as in crisis scenarios – and that can work with incomplete or missing data (such as "one-shot learning"). Need is also urgent for bridging gender inequalities in big data. More effort must be made to train younger generations, women and men, to ensure gender equality and the inclusiveness of ethnic groups in shaping AI.

As the field of data science accelerates, countries must create robust big data and AI strategies to prevent growing inequalities in access and use of these technologies. In digital advertising, for example, where many of these capabilities were incubated, big data and AI continue to demonstrate their ability to concentrate wealth – and data – in the hands of the few and widen inequalities.

Just as misuse of AI may lead to harm, nonuse of AI may allow preventable harms to occur. The challenge is that misuse of these new tools is already rife online and real harm is being done, while the opportunity cost of failure to use them responsibly is mounting. Clearly, although achievement of the 2030 Agenda and the modernization of humanitarian practices necessitates responsible use of these new tools, it urgently requires a new, rights-centric effort by all stakeholders to ensure innovations meet community needs and no one is left behind. Undoubtedly, assessing the ethical impact of AI in addition to privacy protection measures can mitigate harm, maximize benefit, and lead to use of the new technologies as a force for good.