Hidden Stories — an investigation of the stories that are being told about the refugee crisis

Reveal rather than enframe

The worldview of Cybernetics was about systems — human, nonhuman, and both — and that our world of “exceedingly complex systems”[1] can never be fully knowable, it’s instead a “performative dance of agency”[2]. Norbert Wiener talked about the coordination and collaboration between men and machines at the very core of Cybernetics. In his book “God and Golemn”, 1964, he wrote about many of the problems we have today in the way technology interacts with other systems. The “gadget worshipers”, he writes, “have the special motive of the desire to avoid personal responsibility for a dangerous or disastrous decision by placing the responsibility elsewhere”.

Stories and Systems

We’re woven into a web a personal and cultural stories. I somehow don’t laugh when a friend jokingly says something about communism as probably the story of communism in my head is different than other people’s image of it and that is all perfectly normal. For example, many times when we see a number our mind is quick to create many different stories about it. This has become increasingly easy with the help of technology, Statistics and AI in particular. In her essay “Leverage Points: Places to Intervene in a System”, Donella Meadows puts numbers in the very bottom of the list. Leverage points are “places within a complex system (a corporation, an economy, a living body, a city, an ecosystem) where a small shift in one thing can produce big changes in everything”. In her analysis numbers make up the least effective leverage point in a System. Here’s the full list:

Representations as part of our human condition

A story is intimately connected to the concept of representation. Social Science talks about the different meanings of a representation. First we can distinguish between the act or action of representing and the state of being represented. In the first case we are creating meaning by actively picturing, describing in language or in some other form. In the latter case the representation literally takes the place of the subject of our representation in some respect.

Time travel as imagined in 1989's Back To The Future

Why do we want to learn representations when doing Machine Learning?

In their book on Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaaron Courville ask the question about what makes one representation better than another? An ideal representation, they say, “is one that disentangles the underlying causal factors of variation that generated the data”. We describe how data is generated by using probability distributions. Many deep learning algorithms are motivated by the assumption that the hidden units of neural networks can learn to represent the underlying causal factors that explain the data. This hypothesis is still a huge challenge and opportunity for us to critique the algorithms we create[5].

  • Investigative: What is investigative journalism? The best definition of what constitutes “investigative” journalism is journalism that seeks to reveal something that someone with some level of power (a person, group or institution) seeks to keep a secret[6].
  • Community-driven: Communities bring the energy and expertise to reinvent themselves from within. We wanted to investigate and learn from the success stories of how civic journalism used to serve communities in the 1990s. The focus of civic journalism was to go beyond informing the public and help provide support and interventions that enriched community life. Its founders described it as “a craft that builds up the world while simultaneously describing it”[7].
  • Journalism: Journalism has historically played a central role in shaping public discourse. We wanted to understand the challenges journalists face in their invaluable work to help some of the most vulnerable people in the world in the setting of refugee camps.
  • AI tools for intervention rather than prediction: we wanted to research what kinds of technological tools are already in place in the broader ecosystem of organizations and individuals working to provide sustainable solutions to the problems facing displaced communities.

What becomes possible if we have good representations?

Seeing Whole Systems

References

[1] Stafford Beer’s concept of an exceedingly complex system — “a system with its own inner dynamics, with which we can interact, but which we can never exhaustively know, which can always surprise us”. The quote is from Andrew Pickering’s book “The Cybernetic Brain: Sketches of Another Future”.
[2] Andrew Pickering’s worldview about what it means to be within the world we live in — a world built from exceedingly complex systems.
[3] Barocas, Solon and Selbst, Andrew D., Big Data’s Disparate Impact (2016). 104 California Law Review 671 (2016). Available at SSRN: https://ssrn.com/abstract=2477899
[4] Dobbe, Roel, Dean, Sarah, Gilbert, Thomas and Kohli, Nitin, A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics https://arxiv.org/pdf/1807.00553.pdf
[5] Blei, David M., Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models
https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-022513-115657?journalCode=statistics
[6] Tofel, Richard J., A White Paper from ProPublica, Non-Profit Journalism: Issues Around Impact https://s3.amazonaws.com/propublica/assets/about/LFA_ProPublica-white-paper_2.1.pdf
[7] Overholser, Geneva, How to Best Serve Communities: Reflections on Civic Journalism
https://www.democracyfund.org/publications/how-to-best-serve-communities-reflections-on-civic-journalism
[8] A dataset available on Kaggle and containing 143,000 articles from 15 American publications https://www.kaggle.com/snapcrack/all-the-news
[9] UN Sustainable Development Goal #16 https://sustainabledevelopment.un.org/sdg16

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Bogdana Rakova

Bogdana Rakova

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Senior Trustworthy AI Fellow at Mozilla Foundation, working on improving AI contestability, transparency, accountability, and human agency