The initial excitement concerning the impact of artificial intelligence (AI) on developed countries is shifting to how AI could impact developing countries. A recent cover story highlighted the potential of AI Help low-income countries in sectors reminiscent of education, healthcare and agriculture; However, various commentators have expressed concerns that AI may lead to this a series of damages in the worldwide south.
The root of this problem often lies in concerns about how data is collected, stored and used – and the way it’s used responsibly reused for purposes aside from those for which they were originally collected. Data collected from satellite images and sensors may be reused to watch deforestation, air and water quality, etc Effects of climate change. Telecommunications or social media data may be reused for Disaster relief to trace people's movements, discover areas in urgent need of assistance, and coordinate relief efforts more effectively.
Responsible reuse of private and non-private data may break down silos. Access to cell phone data has been repurposed to advertise collaboration and innovation, for instance by harmonizing access to public transport and ride-sharing to shorten travel time and reduce automobile use. Open contract data was used to enhance access to HIV and tuberculosis drugs in Moldova, a rustic with one among the very best patient rates in Europe. However, reuse carries its own risks, particularly for user privacy and security.
Power imbalances
Promoting responsible data reuse requires addressing power imbalances in the info ecology that weaken key stakeholders and undermine trust in data governance practices. These imbalances may be particularly damaging in the worldwide south. To address them, notions of consent must expand beyond current individualized approaches in favor of what we call social license to reuse.
There are various power and influence imbalances between different stakeholders in the info ecology. Larger players or those from wealthier regions have larger budgets and more expertise and computing power to access and work with data. These imbalances turn into particularly essential when reusing data. In such cases, the unique data subjects often lack the flexibility to influence and even concentrate on secondary uses, and the info might be utilized in ways in which harm them or disproportionately profit a couple of.
These risks are particularly pronounced in developing countries in Asia, Africa and Latin America, partially resulting from power imbalances between governments and corporations within the Global South and North. But even inside the countries of the Global South themselves, there are major asymmetries that require particular attention to the way in which data is collected, used and reused by governments that purport to talk on behalf of the people.
The need for social license
In theory, consent provides a mechanism for reducing power imbalances. In reality, existing consent mechanisms are limited and in some ways outdated. They are based on binary distinctions – typically represented in the shape of checkboxes that the majority web sites use to ask you to enroll in marketing emails – and which don’t have in mind the nuances and context-sensitive nature of knowledge reuse. Consent now generally means the assent of the person, a term that ignores the broader needs of communities and groups.
Although we’re aware of the necessity to protect details about an individual, reminiscent of their health status, this information may also help manage and even prevent societal health crises. Individualized notions of consent don’t have in mind the potential public good of responsible reuse of individual data. This makes them particularly problematic in societies with more collective orientations, where prioritizing individual decisions could disrupt the social fabric.
The concept of social license, which has its roots within the Nineties Raw materials industryrefers back to the collective acceptance of an activity, reminiscent of B. the reuse of knowledge based on its perceived fit with community values and interests. Social licenses transcend individual priorities and help balance the risks of knowledge misuse (e.g., the risks of invasion of privacy versus neglecting the usage of private data for the general public good). Social licenses enable a more inclusive notion of consent that’s dynamic, diverse, and context-sensitive.
Policymakers, residents, healthcare providers, think tanks, advocacy groups and the private sector must accept the concept of social license before it may well be established. The goal of all involved is to achieve a broad consensus on community norms and a suitable balance between social risks and opportunities.
Community engagement can create a consensus-based basis for preferences and expectations regarding data reuse. Engagement could happen through dedicated “data assemblies” or collaborative consultations on the reuse of knowledge for specific purposes under specific conditions. The process would want to incorporate voices which can be as representative as possible of the varied parties involved, and likewise include those that have been traditionally marginalized or silenced.
Institutional innovation
Beyond community engagement, the consent of the legal and policy community is required to translate collective decisions into enforceable tools and mechanisms. This critical step requires revolutionary approaches to developing ways to design and contain recent governance functions. A dedicated interdisciplinary research agenda in data, law, policy and social sciences will help link social licensing theory and its practical implementation.
Successful implementation of social licensing will probably also require institutional innovations, which could include emphasizing the role of knowledge stewards or others tasked with responsibly promoting data sharing. We have seen increasing calls for the role of the Chief AI Officer (CAIO) to steer the mixing of AI technologies to drive innovation and achieve competitive advantage. We imagine that in all areas of knowledge collection, whether public or private, there needs to be a dedicated role in determining how data could also be used inside a company.
But we also imagine that this scope is simply too limited. Such a task can also include identifying opportunities based on other data that’s or needs to be available. Additionally, it may well also include identifying opportunities based on a company's own data that may be shared – not only for profit, but in addition for the general public good.
