You may submit an essay of between 900 and 1,200 words on a topic broadly relate

You may submit an essay of between 900 and 1,200 words on a topic broadly related to the societal impacts of data science, including but not limited to privacy or equity and social justice. Your essay should address a concrete problem, assess the current state of affairs, and suggest one or more paths forward. Examples of suitable topics include but are no means limited to, i) bias in facial recognition, ii) the impact of automation on social services for the poor, and iii) the role of technology in combatting climate change. Please note that you should pick a specific topic and not just write about the general impact of social media or tech companies on society as a whole.<
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You are welcome to argue any angle on your topic, provided that you can offer sound sources and argumentation. For example, you may argue that bias in facial recognition is a major problem in need of solution, or you may argue that no substantial problem exists.<
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Your essay should satisfy the following criteria.<
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Sources: The essay should cite and elaborate on at least five news articles, scholarly journal articles, or other primary sources. Opinion articles do not count. Blog posts are generally unsuitable, but posts by established experts may be permissible; ask me if you’re not sure. You should use a consistent citation convention. If you’re not sure which one to use, APA and MLA are both good choices. Up to one source may be a documentary film, while the remainder must be written. You are free to use any sources to which you have access. If you are having trouble getting started, you may email me for useful starting places.<
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Argument: The essay should have a thesis statement, which is supported by the discussion of the sources. Several examples of successful thesis statements: Taken together, these articles suggest that algorithmic surveillance of marginalized groups is only increasing with time. Although studies have revealed significant biases in Google Search, more recent work suggests that these biases may be less important in practice than once believed. While many proposals exist for how algorithms and computation can be used to combat climate change, these proposals often overlook the role of human interpretation in implementing computer-generating guidance.