The original excitement about the influence of artificial intelligence (AI) on developed countries is shifting toward how AI might influence developing nations. The Economist recently ran a cover story extolling the potential of AI to help lower-income countries in sectors such as education, healthcare and agriculture; however, various commentators have expressed concerns that AI could cause a number of harms in the Global South.

AI Brief
  • Data reuse can address global challenges like disaster response, climate change, and public health but poses risks to privacy and equity.
  • Developing countries face power disparities in data use, requiring broader consent mechanisms, such as a "social licence".
  • Community engagement, legal reform, and roles like data stewards are essential for ethical, equitable, and impactful data reuse.

At the root of this issue runs a frequent concern with how data is collected, stored and used – and responsibly reused for purposes other than it was initially collected for. Data collected from satellite imagery and sensors can be reused to monitor deforestation, air and water quality, and the impact of climate change. Telco or social media data can be reused for disaster response to track people’s movements, identify areas that need urgent assistance and coordinate relief efforts more effectively.

Responsible reuse of public and private data can also break down silos. Access to mobile phone data has been reused to foster collaboration and innovation, like harmonising access to public transport and ride-sharing initiatives to limit travel time and reduce car use. Open contracting data was used to improve access to HIV and tuberculosis medicine in Moldova, a country that has one of the highest patient rates in Europe. But reuse carries its own risks, especially to user privacy and security.

Promoting the responsible reuse of data requires addressing power imbalances in the data ecology that disempower key stakeholders and undermine trust in data management practices. These imbalances may be particularly pernicious in the Global South. Addressing them requires broadening notions of consent beyond current individualised approaches in favour of what we term a social licence for reuse.

There are a number of imbalances in power and influence among different stakeholders in the data ecology. Larger players or those from more affluent regions have bigger budgets and more expertise, plus more computational power, to access and work with data. These imbalances take on particular significance when data is repurposed. In such cases, original data subjects frequently lack the ability to influence or even become aware of secondary uses, and data could be used in ways that harm them or that disproportionately benefit the few.

These risks are particularly pronounced in developing countries in Asia, Africa and Latin America, due in part to power imbalances between governments and companies in the Global South and North. But vast asymmetries also exist within Global South countries themselves, requiring close attention to the way data is collected, used and reused by governments that profess to speak on behalf of the people.

In theory, consent offers a mechanism to reduce power imbalances. In reality, existing consent mechanisms are limited and, in many respects, archaic, based on binary distinctions – typically presented in check-the-box forms that most websites use to ask you to register for marketing e-mails – that fail to appreciate the nuance and context-sensitive nature of data reuse. Consent today generally means individual consent, a notion that overlooks the broader needs of communities and groups.

While we understand the need to safeguard information about an individual such as, say, their health status, this information can help address or even prevent societal health crises. Individualised notions of consent fail to consider the potential public good of reusing individual data responsibly. This makes them particularly problematic in societies that have more collective orientations, where prioritising individual choices could disrupt the social fabric.

The notion of a social licence, which has its roots in the 1990s within the extractive industries, refers to the collective acceptance of an activity, such as data reuse, based on its perceived alignment with community values and interests. Social licences go beyond the priorities of individuals and help balance the risks of data misuse and missed use (for example, the risks of violating privacy vs. neglecting to use private data for public good). Social licences permit a broader notion of consent that is dynamic, multifaceted and context-sensitive.

Policymakers, citizens, health providers, think tanks, interest groups and private industry must accept the concept of a social licence before it can be established. The goal for all stakeholders is to establish widespread consensus on community norms and an acceptable balance of social risk and opportunity.

Community engagement can create a consensus-based foundation for preferences and expectations concerning data reuse. Engagement could take place via dedicated “data assemblies” or community deliberations about data reuse for particular purposes under particular conditions. The process would need to involve voices as representative as possible of the different parties involved, and include those that are traditionally marginalised or silenced.

Beyond community engagement, buy-in from the legal and policy community is needed to translate collective choices into enforceable instruments and mechanisms. This critical step requires innovative approaches to develop ways of framing, and vehicles to contain, new governance functions. A dedicated interdisciplinary research agenda across data, law, policy and the social sciences will help link the theory of social licensing and its practical implementation.

Successfully implementing social licensing will most likely require institutional innovation as well, which could include highlighting the role of data stewards or other individuals tasked with responsibly promoting data sharing. We’ve seen increasing calls for the role of Chief AI Officer (CAIO) to lead integration of AI technologies to drive innovation and achieve competitive advantage. We do believe there should be a dedicated role in all fields of data collection, public and private, to identify how data may be used in an organisation.

But we also believe this scope is too limited. Such a role can also include identifying opportunities based on other data that is available or should be. It can further include identifying opportunities based on an organisation’s own data that may be shared – not only for profit, but also for the public good.


This article is republished from The Conversation via Reuters Connect