In a paper titled “Narratives and Counternarratives on Data Sharing in Africa,” the analysis group lays out structural challenges which includes but restricted to monetary or infrastructure challenges. Coauthors argue that failure to think about ethical issues connected with these obstacles could bring about irreparable harm.
“Currently, a significant proportion of Africa’s digital infrastructure is controlled by Western technology powers, such as Amazon, Google, Facebook, and Uber,” the paper reads. “Traditional colonial powers pursued colonial invasion through justifications such as ‘educating the uneducated.’ Data accumulation processes are accompanied by similar colonial rhetoric, such as ‘liberating the bottom billion,’ ‘helping the unbanked,’ ‘connecting the unconnected,’ and using data to ‘leapfrog poverty.’”
Power imbalances, lack of investment in developing trust, and disregard for nearby know-how and context are identified as the 3 most popular barriers to information sharing, as “entire heterogeneous geographies of people have their data accessed and shared, yet do not reap the same benefits as the data collectors and owners of data infrastructures,” according to the paper. Coauthors argue that dominant narratives about information sharing in Africa today concentrate on a lack of know-how about the worth of information and usually suffers from what coauthors refer to as deficit narratives: stories that concentrate on subjects like poverty, unemployment, or illiteracy prices.
“In recent years, the African continent as a whole has been considered a frontier opportunity for building data collection infrastructures. The enthusiasm around data sharing, and especially in machine learning or data science for development/social good settings, has ranged from tempered discussions around new research avenues to proclamations that ‘the AI invasion is coming to Africa (and it’s a good thing)‘. In this work, we echo previous discussions that this can lead to data colonialism and significant, irreparable harm to communities.”
Coauthors argue that accountable information sharing in Africa should really reject practices that lead to information colonialism and concentrate on meeting the demands of folks and nearby communities very first. They say this calls for awareness and examination of influencing troubles like legacies of colonialism and slavery. They warn that this context can contribute to information policy or practices rooted in Western-centric extractive practices that are “ill-suited for the African context.”
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The biggest datacenter in Africa is reportedly below building in South Africa. It’s portion of a surge of investment in datacenters and African telecom organizations that some have deemed a gold rush. Microsoft opened its very first datacenter in Africa in 2019. AWS opened a South Africa area final year. Google is anticipated to full building on the Equiano subsea cable later this year, and Facebook is constructing a subsea cable that is anticipated to be completed in two or 3 years. Nvidia is also ramping up operations in Africa.
An evaluation of the rise of the African cloud by Xalam Analytics identified that much less than 1% of worldwide public cloud income came from Africa in 2018.
The paper reaches its conclusions by way of interviews with African information authorities and insights from coauthors, a quantity of whom grew up in Africa or at present live on the continent. Rediet Abebe grew up in Ethiopia and cofounded Black in AI. Abebe is an assistant professor at UC Berkeley’s Electrical Engineering and Computer Sciences (EECS), the very first Black faculty member in college history.
Abeba Birhane also grew up in Ethiopia. Currently a Ph.D. student at the University of Dublin, her writing about relational ethics received a Best Paper award at the Black in AI workshop at NeurIPS in 2018. Birhane has written at length about algorithmic colonization. Sekou Remy grew up in Trinidad and Tobago but at present operates as a analysis scientist and technical lead at IBM Research Africa in Kenya. And George Obaido and Kehinde Aruleba wrote the paper in association with the University of the Witwatersrand in South Africa.
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“Data sharing practices which operate in the absence of knowledge of local norms and contexts contribute — albeit indirectly — to the erosion of trust among stakeholders in the data-sharing ecosystem,” the paper reads. “As machine learning and data science move to focus on the Global South and especially the African continent, the need to understand what challenges exist in data sharing, and how we can improve data practices become more pressing.”
Power plays a big part in information sharing in Africa. For instance, analysis cited in the paper identified that Africans are considerably underrepresented in the biomedical analysis neighborhood, even when the information comes from Africa.
“Power asymmetries, historically inherited from the colonial era, often get carried over into data practices and manifest themselves in various forms, from imbalanced authorship to uneven bargaining powers that come with funding,” the paper reads. The coauthors add that energy imbalance is also a aspect in relationships amongst project managers and information analysts information analysts and information collectors and information collectors and analysis participants.
The paper also encourages understanding attitudes about information amongst African researchers. Governments in locations like Ghana and Kenya have opened information portals, but a survey of South African researchers identified that only about one in 5 shares information with other people, and a 2018 study involving life scientists in more than a dozen sub-Saharan African nations described a quantity of disincentives to information sharing. That similar year, governments in nations like Botswana, Ethiopia, and South Africa created national information techniques. To address popular troubles, the African Union formed an AI working group in 2019.
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“Trust is the fundamental component of all relationships in a data sharing ecosystem,” the paper reads. “The future of open data management and data sharing and their contribution to the advancement of science and technology in Africa will continue to increase, despite the slow pace caused by the lack of funding, redundant policy frameworks, and limited infrastructures.”
The paper was accepted for publication at the ACM Fairness, Accountability, and Transparency (FAccT). The virtual conference starts next week. Other papers accepted for publication at FAccT consist of analysis that examines how language models do with word association and censorship and a contact for a culture modify in machine understanding by Ethical AI group at Google and University of Washington. The FAccT conference was cofounded by Timnit Gebru, the Ethical AI group lead Google fired in late 2020. The conference has a history of becoming sponsored by a quantity of Big Tech organizations with poor records of hiring Black researchers, like Facebook AI Research (FAIR), Google’s DeepMind, and Google.