When we deploy machine learning models, what are the known scenarios in which the technology does not work? In this talk, we will go over the many potential blindspots in ML deployments, and how, as a fundamentally narrow and limited technology, we need to be careful to communicate, evaluate for and directly address these risks in a way that protects users and reinforces developer accountability.

As algorithmic systems become an ever more integral aspect of much of the social sphere, their application for “social good” has also increased. Western-made AI is deployed throughout the African continent with great enthusiasm and little regulation or critical engagement, in a manner that resembles past colonial conquests. This talk explores some of the critical and ethical questions that need to be raised with the “digitization” of the African continent and the use of AI for “social good”.

Since the turn of the millennium, the interdisciplinary field of information & communication technologies and development (ICTD) has explored how digital technologies could contribute to international socio-economic development. The associated research community includes both techno-utopians who imagine that just about any problem can be solved with the right application of technology, as well as extreme skeptics wary of any attempts at intervention. Debates continue, but in this talk, I will attempt to summarize some of the expressed consensus in ICTD -- things that not everyone necessarily believes, but will at least pay lip service to. I will also discuss what I call technology's "Law of Amplification," which reconciles some of the differing opinions in ICTD and also offers guidance for how machine learning can have real-world impact.

Data accessibility, including the ability to make data shareable, is important for knowledge creation, scientific learning, and progress. In many locations, including Africa, data is a central engine for economic growth and there have been recent calls to increase data access and sharability by organizations including the UN, AU, and many others. Discussions around data inaccessibility, however, revolve around lack of resources, human capital, and advanced technologies, hiding the complex and dynamic data ecosystem and intricate challenges faced in sharing data across the continent. In this piece, we shed light on these overlooked issues around data inaccessibility and sharing within the continent using a storytelling perspective. Using the perspectives of multiple stakeholders, we surface tensions and trade-offs that are that are inadequately captured in current discussions around data accessibility in the continent.

While applications of AI is now becoming more common in fields like retail and marketing, application of AI in solving problems related to developing countries is still an emerging topic. Specially, AI applications in resource-poor settings remains relatively nascent. There is a huge scope of AI being used in such settings. For example, researchers have started exploring AI applications to reduce poverty and deliver a broad range of critical public services. However, despite many promising use cases, there are many dataset related challenges that one has to overcome in such projects. These challenges often take the form of missing data, incorrectly collected data and improperly labeled variables, among other factors. As a result, we can often end up using data that is not representative of the problem we are trying to solve. In this case study, we explore the challenges of using such an open dataset from India, to predict an important health outcome. We highlight how the use of AI without proper understanding of reporting metrics can lead to erroneous conclusions

Many policymakers, academics and governments have advocated for exchangeable property rights over information as it presents a market solution to what could be considered a market failure. Particularly in jurisdictions such as Africa, Asia or South America, where weaker legal protections and fleeting regulatory enforcement leaves data subjects vulnerable or exploited regardless of the outcome. We argue that whether we could achieve this personal data economy in which individuals have ownership rights akin to property rights over their data should be approached with caution as a solution to ensuring individuals have agency over their data across different legal landscapes.

We present an objection to the use of property rights, a market solution, due to the noxious nature of personal data - which is founded on Satz and Sandell's objection to markets. Ultimately, our rights over personal data and privacy are borne out of our basic human rights and are a precondition for the self-development, personal fulfilment and the free enjoyment of other fundamental human rights - and putting it up for sale risks corrupting its essence and value.

In September and October 2019, a three judge bench in Kenya heard a case protesting digital ID. The case arose after the state rolled out a mandatory digital ID system, known as Huduma Namba that would be a prerequisite for access to government services. Being a constitutional case, arguments on human rights such as privacy were expected. However, the state framed its case of digital ID as inevitable technological development and those opposed to the advancement as being fearful of technology. To understand whether there were human rights implications with the digital ID, parties had to explain about the technology choices taken, the data practices and parties involved in the project. Hence the court (and public at large) spent a significant amount of time getting to understand multi-modal deduplication. While judgement on the case is yet to be delivered, the case brings to fore the societal implications of technology. It also calls for dialogue between technologists and sociologists in design and execution of projects aimed at low and middle income countries.