Merge branch 'data-feminism' into main

site/write_ups/2024-08-31_writeup.md

@@ -148,3 +148,70 @@ In addition to the community focussed alternatives, there are ways in which we c
 With respect to changing the way undergrad courses teach data skills, we thought that instead of data ethics being taught as one lecture it could be infused throughout. Outside of university, more engagement with groups outside of academia who are fighting for social change, such as community and non-profit groups, would also improve mobilisation. An example of a relevant outreach project is [DataFace]( https://jeangoldinginstitute.blogs.bristol.ac.uk/2024/07/03/empowering-schools-to-improve-the-data-literacy-of-young-people/), which is working to improve the data literacy of children in schools.
 
 Problems with time and funding would need to be addressed to enable wider implementation of projects like Local Lotto. For many, involvement in these projects is extra to their current workload, and people do not have enough time to balance both. If the workload can be too much for people who are passionate about the cause, we thought it unlikely that data scientists without the same enthusiasm would be willing to dedicate their free time to these kinds of projects.
+
+# Data Feminism Chapter 3 – On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints
+
+## Principle: Elevate Emotion and Embodiment
+
+_“Data feminism teaches us to value multiple forms of knowledge, including the knowledge that comes from people as living, feeling bodies in the world.”_
+
+## Chapter Summary
+
+Traditionally, data science has placed value on ‘objectivity’ and ‘neutrality’, designing data visualisation in ways that are plain to avoid eliciting emotion. Underpinning the principles of ‘objective’ data visualisation is statistical graphics expert [Edward Tuft’s](https://en.wikipedia.org/wiki/Edward_Tufte) [‘data-ink ratio’](https://infovis-wiki.net/wiki/Data-Ink_Ratio), which denotes that designers should aim to use ink to display data alone. Separating emotion from knowledge is assumed to avoid attempting to persuade, giving readers the space to interpret results for themselves. However, this chapter argues that the lack of persuasion is an illusion. No matter how the data is presented, there will always be decisions that angle the data from a particular viewpoint; usually, the viewpoint of the dominant, default group. 
+
+When visualising data it is impossible to avoid interpretation as, unless you are presenting the raw data, there will be some things that are necessarily highlighted and some things that are necessarily obscured. Conventions of data visualisation reinforce people’s perceptions of their factual basis, contributing to the perception of it as objective, scientific, and neutral, and making it more likely that people will believe it. Donna Haraway argues that all forms of knowledge are situated, produced by specific people in specific cultural, historical, and geographic circumstances. Disclosing your position is an important strategy to be transparent about the limits of your knowledge claims. 
+
+The logic that grounds the false binary between emotion and reason is gendered because of the stereotype that men are more emotional than women. Emotion can be leveraged alongside visual minimalism to engage different parts of the brain, allowing for a range of learning types and communicating to a wider group of people. Harnessing emotion can help people to experience uncertainty, something which is famously difficult to communicate in data visualisation. 
+
+When thinking about how to present data, there is not one hard and fast rule. Each person has a unique skillset and intersection of subject positions which can offer a set of valuable perspectives that frame your work. Decisions should be informed by context, working towards a more holistic and inclusive ideal.
+
+## Definitions
+
+__Framing effect__ – the choices that designers make when visualising data (what to highlight and what to obscure) impacts how people interpret the graphics and what they take away from them
+
+__Provenance rhetoric [Hullman and Diakopoulos](https://ieeexplore-ieee-org.bris.idm.oclc.org/document/6064988)__ – signalling the transparency and trustworthiness of the presentation source to end users by citing the source of one’s data, increasing the likelihood that viewers will believe what they see
+
+__Feminist objectivity [D Haraway](https://www.jstor.org/stable/3178066)__ - that all forms of knowledge are situated, that is that they are produced by specific people in specific circumstances. We can use this to bring together multiple partial perspectives
+
+__God trick/view from nowhere [D Haraway](https://www.jstor.org/stable/3178066)__ - the perceived ability to use an impossible/imaginary standpoint that appears to be neutral.
+
+
+## Discussion Summary
+
+### What does it mean to you to ‘Elevate Emotion and Embodiment’ - in data visualisation and/or data generally?
+
+Elevating emotion and embodiment can help circumnavigate the myth of rationality and impartiality. There are some things that we experience and know to be true, however, scientific process demands empirical evidence to accept they are real. Demanding empirical evidence can detract from other forms of knowledge. When we simplify behaviour research to animal models, for example, we lose context; we think that there must be more useful and humane methods for research.
+
+Science as we know it today was shaped by the [Enlightenment movement](https://plato.stanford.edu/entries/enlightenment/) in Europe, which advanced [rationalism and empiricism](https://plato.stanford.edu/entries/rationalism-empiricism/) and discredited the importance of emotion and embodiment. Ideas from the Enlightenment propagated around the world through European colonies, consequentially supressing indigenous knowledge and ways of living. Revaluing emotion and embodiment creates space for different types of knowledge systems. [Indigenous populations have been incorporating data visualisation into record keeping for centuries](https://journals.sfu.ca/jmde/index.php/jmde_1/article/view/783/729), and have ingenious methods of data collection such as the [Marshall Islands stick charts](https://en.wikipedia.org/wiki/Marshall_Islands_stick_chart) which represent ocean swell patterns. [Data sovereignty movements](https://www.science.org/doi/10.1126/science.adl4664) are working towards indigenous communities regaining control over their information whilst pushing back against data colonialism and its harms. In Canada there are [toolkits to support indigenous governments in managing and owning their data](https://indigenousdatatoolkit.ca/getting-started/indigenous-approaches-to-data-and-evaluation/), appreciating different types of dynamic knowledge systems including qualitative, oral, empirical, and scientific knowledge.
+
+Incorporating more types of knowledge broadens the experience that we can have of data. Whilst the final output may be static, the process of iteratively designing a data visualisation is actually quite embodied. [We can expand embodiment to the final output itself by harnessing multiple senses to communicate data](https://www.youtube.com/watch?v=Hu_uCAUxYQk&t=0s). [Using illustrative visuals can also be very effective in conveying a message](https://www.instagram.com/monachalabi/?hl=en) and more colour doesn’t mean less factual. There is a lot of value in making data visualisation emotive, as it can help to represent topics beyond the binary. [Data visualisations during the pandemic](https://www.eyemagazine.com/feature/article/the-pandemic-that-launched-a-thousand-visualisations), for example, were very emotive.
+
+Minimising ink use, making visualisation less expressive and more sterile, is reductionist and ignores potential connotations. Being minimal in the way data is displayed does not mean it is impartial; minimalism is influenced by entrenched biases. The idea of minimising extra ink being best practice is linked to men as ‘less emotional’ and ‘more factual’ than women. How people respond to certain colours [is also influenced by their culture](https://link.springer.com/article/10.1007/s00426-022-01697-5), such as Western populations reacting to red-green colours.
+
+Minimalist design decisions, such as choice of colour palette, may often be subliminal yet are impactful for accessibility. Making a visualisation accessible for one group often makes it inaccessible for another. For example, when plotting points for app usage in schools in Bristol we used a heat map (red, yellow, green) to indicate levels of use. Using a red-yellow-green spectrum isn’t colourblind friendly at all, however. People can check their designs using [colour blindness checking palettes](https://lospec.com/palette-list/ibm-color-blind-safe). 
+
+We also wondered who it is that gets to do embodied/visceral art: what resources are required; how easy it is to access those resources; how equal access is across different areas of society.
+
+### What did you think of the examples of how positionality is expressed in data visualisations (intended or not) using different graph styles, colour or annotations?
+
+Default data visualisations imply a white and western interpretation of science, insinuating that because it has this positionality it should be trusted. Whilst Florence Nightingale’s greatest contribution to combating disease and death resulted from the graphs she made to back her public health campaigns, [she also drew attention to how graphs persuade, *whether or not they depict reality*](https://timharford.com/2021/03/cautionary-tales-florence-nightingale-and-her-geeks-declare-war-on-death/).
+
+### What did you think of the reactions of NYT readers to the election gauge? How should we represent uncertainty?
+
+People generally hate uncertainty, which could be because uncertainty is often equated with messiness. It is important to remember that a lot of people haven’t touched statistics or maths since they were 16, making it difficult to understand statistical uncertainty. Even professors can misunderstand confidence intervals. We wondered if talking about confidence conveys uncertainty, or if talking about significance conveys certainty. [People have a lot of cognitive biases which affect the way they interpret data. For example, truncating the y-axis so that it doesn’t start at 0 can heavily influence the way that a graph is read](https://www.youtube.com/watch?v=oluf9j5Uv4k). 
+
+Audiences not understanding data visualisation until it’s explained to them is such a common real world problem. We find that some audiences expect ‘boring’ and ostensibly ‘neutral’ visualisations that this chapter criticises. However, if we don’t provide those kinds of visualisations, the audience thinks that we’re either not doing our job properly or we have an agenda and are actively manipulating them. It is difficult to balance these expectations with the recommendations of this chapter. 
+
+Figuring out how to balance expectations can perhaps be helped by our takeaways from this chapter: multiple views of data are relevant, and emotion is a valid one of these – not just that ‘emotion is important’. [Christian Amanpour famously argued that the role of a journalist is to be truthful, not neutral](https://www.mediaite.com/media/christiane-amanpour-recalls-moment-she-came-up-with-be-truthful-not-neutral-mantra-she-used-to-criticize-new-cnn-regime/).
+
+Thinking about how to present data is supported by [Nicole Dalzell’s three questions for pedagogical decisions](https://stattlc.com/2024/04/11/evaluating-pedagogical-choices-with-an-inclusive-approach/) that [affect how LGBTQ+ students learn in the classroom](https://stattlc.com/2024/04/30/evaluating-pedagogical-choices-with-an-eye-toward-lgbtq-students/): 
+-	What students are not supported by the design decision? 
+-	How can we adapt to reflect student needs not supported by the initial design? 
+-	Is applying this design decision sustainable for both me and the students?
+
+---
+
+```{admonition}
+We hope you enjoyed this writeup of our discussion of chapter 1 of Data Feminism from our Data Feminism book club over the summer of 2024. We hope to run another in the summer of 2025!
+In the mean time, the co-organisers would be really enthusiastic to support anyone interested in running another book club! Please reach out if you want to get involved.
+```