By revealing pervasive gender disparities in key positions, for instance, data can help bring the disparities to the fore and, further, support social and structural reforms. Having said that, however, this is not what I want to discuss here. Wherever the topic of technological innovation arises, what we typically find is tech advocates offering rationales for attempting to digitize every aspect of life, often at any cost. The upcoming Deep Learning Indaba conference in Nairobi is a typical example. Given that we have enough tech worshipers holding the technological future of the continent in their hands, it is important to point out the cautions that need to be taken and the lessons that need to be learned from other parts of the world.
Africa need not go through its own disastrous cautionary tales to discover the dark side of digitization and technologization of every aspect of life. Data and AI seem to provide quick solutions to complex social problems. And this is exactly where problems arise.
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Around the world, AI technologies are gradually being integrated into decision-making processes in such areas as insurance, mobile banking, health care and education services. And from all around the African continent, various startups are emerging — e. But in the race to build the latest hiring app or state-of-the-art mobile banking system, startups and companies lose sight of the people behind each data point. The rights of the individual, the long-term social impacts of these systems, and their consequences, intended or unintended, on the most vulnerable are pushed aside — if they ever enter the discussion at all.
AI, like big data , is a buzzword that gets thrown around carelessly; what it refers to is notoriously contested across various disciplines, and the term is impossible to define conclusively.
The Algorithmic Colonization of Africa
In fact, some automated systems used for hiring and policing are put forward with the explicit claim that these tools eliminate human bias. Automated systems, after all, apply the same rules to everybody. But this is one of the single-most erroneous and harmful misconceptions about automated systems. AI is a tool that we create, control, and are responsible for, and like any other tool, it embeds and reflects our inconsistencies, limitations, biases, and political and emotional desires.
How we see the world and how we choose to represent it is reflected in the algorithmic models of the world that we build.
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Therefore who builds the systems and selects the sorts and sources of data used will deeply affect the influence they may have. The results that AI systems produce reflect socially and culturally held stereotypes, not objective truths. The use of technology within the social sphere often, intentionally or accidentally, focuses on punitive practices, whether it is to predict who will commit the next crime or who would fail to pay their mortgage. Constructive and rehabilitative questions such as why people commit crimes in the first place or what can be done to rehabilitate and support those that have come out of prison are almost never asked.
Furthermore, such technologies necessarily bring cruel, discriminatory, and inhumane practices to some.
The cruel treatment of the Uighurs in China and the unfair disadvantaging of the poor are examples in this regard. Inmates locator. Judgment Files. Medical Records. Adoption Records. Probate Records.
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Henningsen F. This search accesses National Death Records. Abbott L. Whitaker, Sacramento, CA. After one dancer fends him off with a knee to the groin drawn from an incident described in an earlier biography , Fosse sidelines her from a dance number and has Reinking step in. I have felt more uncomfortable with other people on a more sinister level, but there was nothing sinister with Bob. And I think for anybody who knew Bob and really worked with him and really knew him, that was not a dynamic at all.
You knew that those two things are separate. To ears, it sounds like a serious case of creepitude, but Reinking told the story with a wistful laugh. But he was so funny during the conversation. I realized I could say no to him. I think everybody he was with was completely willing to be with him. I never perceived him as using a part as being manipulative.
By the time I got through with all of the auditions, I knew that if I got it, it was the right thing. He never played favorites. He just wanted you to be good, and if you got the part it was because you were right for it. So by the time we did start going out I was pretty sunk. She met Verdon when the show was still playing out of town in Washington, D.
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The two women—romantically and artistically entangled with the same complicated man—might have easily been rivals, but they became unlikely friends. She respected me. And I trusted her. I trusted Bob. I trusted Gwen. And I was right to. They never hurt me, and they were on my side. They were friends in the deepest sense of the word.