## 4.4 Using Visualization and Psychology to Support EA

People share many misconceptions and biases preventing them from helping effectively – for example, geographical and cultural proximity often greatly affect how much people feel like helping [CSG21]. Researchers studying humanitarian visualization can take inspiration from recent work on judgment and decision making with visualizations [Dim+18], and apply findings and methods from psychology to study how visualizations interact with cognitive biases, and whether visualizations can help alleviate those biases. Unfortunately, much like visualization research, psychology research has mostly focused on how to make people donate more, rather than more effectively. However, Lucius Caviola and colleagues [CSG21] have recently done a tremendous job at reframing past findings through an EA lens, leading them to identify major psychological obstacles to effectiveness, which fall in two categories:

1. *Motivational obstacles*. People think that whether and how to help is largely a matter of personal preference; they give based on how much they feel emotionally connected to the issue (e.g., they feel more strongly about diseases that are common in their country or have affected their loved ones); they dislike prioritizing some causes over others; they view people who try to donate rationally more negatively than those who donate based on empathy.

2. *Epistemic obstacles*. People think that charity overhead is wasteful, or find funding overhead unsatisfying; they think that effectiveness cannot be quantified; they do not think clearly about probabilities; they are not aware that charities differ greatly in their effectiveness; they don't know which charities are the most effective.

Caviola and colleagues also identified four types of strategies to increase effective giving: information, choice architectures and incentives, philosophical reasoning, and norm changes.

*Information* addresses epistemic obstacles through education. As I mentioned before, one of the areas where visualization can help is by conveying rich quantitative facts about charity effectiveness in a way that is easy to process. Visualization could also be used to argue for lesser-known EA causes such as wild animal suffering and global catastrophic risks, by conveying data about how serious, neglected and tractable these causes are. Finally, visualization could also help dispel misconceptions, for example by showing data about how charity overhead is employed, together with simulations illustrating how cutting overhead would likely yield less positive outcomes. Information is the type of
strategy where the possible benefits of visualizations are the most evident, and where a
lot can be done in collaboration with the EA community.

*Choice architectures and incentives* address motivational obstacles by nudging (e.g., using effective charity programs as default options) and incentivization (e.g., using donation matching or tax deductions targeted to effective charity programs). Here, possible roles for visualization are less immediately evident, but this type of strategy can potentially lead to the most interesting innovations and contributions to knowledge. In particular, it could be interesting to study which nudging techniques can translate to visualizations. For example, a well-documented bias is the decrease of people's concern for individual victims as the number of victims increases: a tragedy that affects one million people typically does not generate 100 times more concern or donations than a tragedy that affects a thousand people [CSG21]. However, this effect is less pronounced when donors evaluate all options at the same time – which could mean seeing data about multiple tragedies visualized side-by-side – than if they evaluate the options sequentially.

The last two categories listed by Caviola and colleagues, *philosophical reasoning* (exposing people to philosophical arguments) and *norm changes* (pushing for a change of moral standards) are important but probably less directly relevant to visualization.

## 4.5 Combining Experiences with Quantitative Facts

Again, a major way in which visualization can support EA is by helping people compare
charity programs. To take a trivial example, an EA website could include as an overview
of its top programs a bar chart of the number of lives saved per unit of donation for each
program.

However, this is only a minimalist example, and important visualization design challenges
arise when a variety of outcomes need to be visualized and compared. For example,
about 600 mosquito nets prevent the death of a child, but they also prevent 500 to 1,000
cases of malaria [Aga22]. This is an enormous benefit in and of itself, as malaria is
a crippling disease with flu-like symptoms that can periodically return, can be highly
disruptive for the life of households, and can leave children disabled [Ric12]. Similarly,
GiveWell lists a charity that saves lives by giving vitamin A supplements to children, but
even when it is not fatal, vitamin A deficiency causes a range of terrible problems such as
repetitive infections and blindness [Wor22]. GiveWell sometimes go beyond lives saved
and considers charities expected to impact the recipient's lifetime earnings (treatments for parasitic worm infections) or their overall quality of life (cash transfers for extreme poverty). Another effective altruism website lists a charity that can use about $20,000 to prevent a year of homelessness in the US or UK [Cla20], and another one that can use $200–$300 to prevent the equivalent of one year of severe major depressive disorder for a woman in Uganda [Hal19]. It is very hard to imagine how to visualize those widely different types of outcomes in a way that supports informed, effective-altruist decisions.

Ideally, a major donation or funds allocation decision should be based both on quantitative facts (e.g., the number of people affected, the cost of interventions) and a deep understanding of people's subjective experiences with and without the interventions, especially concerning the degree of physical and psychological suffering involved. However, it is hard for a person who has never contracted malaria or never had a vitamin A deficiency to have a reliable intuition of what those experiences entail. This is where stories – in the form of text, images, graphic novels, movies or video games – could play an important role by helping people understand subjective experiences on a visceral level. I have previously emphasized the limits of storytelling for EA purposes, but certain ways of combining stories with data may be very effective at supporting EA.

One potentially effective strategy could be to (i) use stories to give a qualitative understanding of the personal experiences involved in a human tragedy, and (ii) use data to give a quantitative understanding of the extent of the tragedy. It seems important that both elements are provided in order to support EA decisions. In particular, stories of personal tragedies provide a proof of existence but can give a distorted vision of reality in the presence of selection bias: news media, for example, often select atypical stories based on their shock value. But if personal stories are complemented with clear data about how representative they are, viewers will get a more accurate appreciation of the extent of the problems and of the magnitude of the human suffering involved.

It will likely be a major research challenge to find out more concretely how to effectively combine stories with quantitative data. There are at least two possible approaches: in a *data-then-story* approach, people would view statistical data about tragedies or social issues, and then zoom into individuals to see their personal stories, either real or hypothetical. The choice of individuals may be decided by the viewer following a detail-on-demand approach, or it may follow a random sampling scheme. Meanwhile, in a *story-then-data* approach, people would first see one or several typical stories (for example, the daily life of someone with disease A or disease B), and would then be able to explore statistical data (for example, the prevalence of those two diseases, and how they
<figure><img src="image_54.png" /><figcaption>**Fig. 4.4:** Excerpts from Hans Rosling's TED talk *The Magic Washing Machine*, which combines a personal story (left) with data (right). Source https://www.ted.com/.</figcaption></figure>

could be reduced with different interventions). An example of story-then-data approach is Hans Rosling's talk *The Magic Washing Machine* (Figure 4.4): he first tells a story that gives a powerful account of how life-changing washing machines are, and then goes through data about how many people in the world have access to them, and how this is likely to change with economic growth.

It is challenging to reconcile the world of numbers with the world of subjective experience, but not impossible – for example, if an effective altruist judges that having disease A is twice as bad as having disease B, they could conclude that preventing 10,000 cases of disease A is equally desirable as preventing 20,000 cases of disease B.

## 4.6 Emerging Technologies

In visualization research, there has been a lot of interest in conveying visualizations through other media than computer screens, like physical objects [DJV20] and mixed reality displays [Kra+21]. In a recent position paper [Dra22b], I discuss the interesting research opportunities offered by such media for the purpose of humanitarian visualization. I summarize them here.

**Virtual reality.** By providing a way for viewers (e.g., donors or charity managers) to immerse themselves more fully into personal stories, virtual reality (VR) may help enhance their visceral understanding of human issues and tragedies. VR documentaries already exist that cover topics such as war, migration, and diseases. Such immersive
<figure><img src="image_55.png" /><figcaption>Fig. 4.5: VR visualization of mass shooting data in the US. Source [Iva+19].</figcaption></figure>

stories could be combined with immersive data visualizations for EA purposes. This idea has started to be explored by Ivanov and colleagues [Iva+19], who designed a VR visualization of mass shooting casualties in the US (Figure 4.5). Each silhouette represents a person who died from a mass shooting in the US. Viewers can step back to get an overview of the dataset (A in the figure), or come closer to gather information about individual victims such as their age group or gender, which are encoded by the shape of the silhouette (B, C).

I see the concept from Ivanov et al. as only a starting point, as one could imagine conveying richer qualitative information about each victim like their physical appearance (as some memorials do by showing photo portraits) or elements of their personal stories, which viewers could choose to relive from a first-person perspective. Unfolding or hypothetical humanitarian issues could be conveyed in a similar manner using a combination of data visualizations and immersive video footage (such as already used in VR documentaries) or simulated scenes. VR could also be used to convey the positive outcomes of donations; For example, one could imagine an immersive version of GDLive (https://live.givedirectly.org/), a website that posts information and updates about recipients of cash transfers.

**Augmented reality.** Augmented reality (AR) can create illusions of objects and people around us, including objects and people that exist remotely. This opens up unprecedented possibilities for bringing the lives of distant suffering people closer to our own, and making humanitarian issues more salient or more memorable. For example, if a person walks in a refugee camp that has been temporarily relocated in their backyard, they may create a mental association and remember the refugees each time they see (or even think about) their backyard. Visualizing data about refugee camps in such a way could thus
<figure><img src="image_56.png" /><figcaption>**Fig. 4.6:** Physical data visualizations of 28 cases of sexual harassment – each plant conveys data reported by one person. Source [MAS22].</figcaption></figure>

give a much more lasting impression. In contrast, VR can subjectively transport viewers in distant places, but once the viewers are back, the event is remote again. As with VR, AR could also be used to convey positive outcomes of charitable donations. In the context of a donor/recipient pairing program, future AR technology may even make it possible for a donor to meet a past recipient on the street and chat with them: a long-distance cash transfer may suddenly feel like helping out an acquaintance in a small village. Finally, in the future, effective altruists may be able to use wearable AR devices as commitment devices, e.g., to get regularly reminded of remote tragedies or ways they can redirect unnecessary personal expenses to humanitarian causes.

**Data physicalization.** Data physicalizations are physical entities whose shape or geometry encodes data [DJV20]. Public spaces already contain physical objects that convey past human tragedies, such as memorials, sculptures and cemeteries. However, few of them focus on current issues and few convey quantitative facts, both of which are important for EA purposes. Rare exceptions include data sculptures (artistic data physicalizations) with a focus on humanitarian data, and occasional explorations by visualization researchers like the *Harassment Plants* (Figure 4.6). Like AR visualizations, physical visualizations can be embedded in our everyday environment. But unlike AR visualizations, they are always present, they can be touched, and do not need special equipment to be seen. On the other hand, AR content can be created and displayed at will.
**Ambient displays.** Ambient displays are displays that “present information within a space through subtle changes in light, sound, and movement, which can be processed in the background of awareness” [Wis+98]. In particular, research projects have explored how ambient displays can support remote intimacy – for example, the color of a lamp may change according to the affective state of a remote intimate partner captured through a wearable biofeedback device. Similar devices could be used to convey quantitative information about the plight of large populations of distant and anonymous people, such as the number of hospitalizations during a pandemic or the number of war casualties. Such ambient displays could give a continuous impression of the severity of an ongoing humanitarian crisis without having to constantly poll news reports.

## 4.7 Conclusion

I discussed perspectives for future research that combine the goals of the two main chapters of this thesis (supporting rational judgment and decisions, and supporting effective communication), and applies them to the area of humanitarian visualization. I argued that such research would gain a lot from being informed by effective altruism (EA). EA offers both a new thinking framework and new questions and problems for visualization research. Yet, it appears that there has been virtually no collaboration so far between EA actors and visualization researchers, perhaps largely due to a lack of mutual awareness between the two communities. But this is changing, as EA is becoming mainstream and highly influential [Mat22]. There are many fascinating questions and problems at the intersection of the two areas and unique opportunities for collaboration, so it is time the two communities connect.
<figure><img src="image_57.png" /></figure>
Conclusion

5

This habilitation thesis has presented research projects looking at how data visualizations
can be used to support data-driven decisions and communicate quantitative facts. Here I
list 17 key takeaways, noting that not all of them are supported by empirical evidence,
and that any takeaway is tentative and can be challenged. Also, the research projects in
this thesis are only dots in the vast space of research questions, and so the 17 takeaways
listed here may appear to be a collection of statements without a clear overarching
structure and without lots of coherence. I still hope they are useful.

The first part of this thesis focused on supporting rational judgments and decisions with
data visualizations. Some takeaways are:

<figure><img src="image_58.png" /></figure>

**Takeaway 1.** When giving people a nontrivial base-rate reasoning problem, adding a data visualization to the text does not help them much, except if all numbers are removed from the text in order to force people to perform visual estimation [MDF12].

<figure><img src="image_59.png" /></figure>

**Takeaway 2.** The attraction effect generalizes to data visualizations: when people use scatterplots to visualize multiple alternatives to choose from, the number and position of irrelevant (dominated) alternatives can unduly influence their choice [DBD17c].

<figure><img src="image_60.png" /></figure>

**Takeaway 3.** When evaluating visualizations for decision support using multi-attribute choice tasks, a ground truth can be approximated by asking people about their choice criteria and looking at the choices most consistent with those criteria [DBD17a].

<figure><img src="image_61.png" /></figure>

**Takeaway 4.** When evaluating visualizations for decision support, time-on-task can be used as a tie-breaker when visualizations achieve similar decision accuracy [DBD17a].
<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /></figure>

<figure><img src="image_65.png" /></figure>

**Takeaway 5.** Tabular visualizations seem to be a compelling choice for supporting multi-attribute choice tasks, despite them being less popular than other multidimensional data visualizations [DBD17a].

**Takeaway 6.** When evaluating data visualizations using crowdsourcing, it helps to add semantic context to the tasks (for example, explaining what axes mean in a plot), but elaborate narratives can make participants less attentive and less accurate [DBD17b].

**Takeaway 7.** A task-based taxonomy of cognitive biases can be a useful starting point for researchers interested in studying cognitive biases in a data visualization context [Dim+18].

**Takeaway 8.** Studying cognitive biases in a data visualization context is subject to the same threats to validity as general cognitive bias research – a major threat being that it is often hard to identify a rational ground truth with certainty (section 2.4).

The second part of this thesis discussed how to support effective communication with data visualizations. Key takeaways are:

<figure><img src="image_66.png" /></figure>

<figure><img src="image_67.png" /></figure>

<figure><img src="image_68.png" /></figure>

**Takeaway 9.** Bertin's matrices are a neglected family of tabular visualizations dating back to the 1970s, and that can greatly help both researchers and a wider audience understand and communicate their tabular data visually [PDF14; PFD19].

**Takeaway 10.** Multiverse analyses are a new and promising approach to transparent statistical reporting, and one interesting way of reporting them is through interactive research papers where readers can explore alternative analysis options by themselves [Dra+19].

**Takeaway 11.** We still do not have conclusive evidence that charts can persuade by their mere presence, e.g., because people associate them with science [DJ18a].
<figure><img src="image_69.png" /></figure>

**Takeaway 12.** There is no evidence that charts conveying only two values are trivial and unhelpful compared to reporting numerals [DJ18a].

<figure><img src="image_70.png" /></figure>

**Takeaway 13.** When conveying data about humanitarian issues, common sense dictates that the audience is more likely to relate to the victims if the visualization emphasizes individuals, but studies have largely failed to confirm this intuition [Mor+21].

<figure><img src="image_71.png" /></figure>

**Takeaway 14.** The design space of humanitarian visualizations is nevertheless very large, and most of it has remained unexplored [Mor+20].

<figure><img src="image_72.png" /></figure>

**Takeaway 15.** Research on humanitarian visualization would gain from being informed by the effective altruism movement, which offers a useful thinking framework and a range of interesting research questions [Dra22a].

Two general takeaways from this HDR thesis are:

<table><thead><tr><th></th><th>A</th><th>B</th></tr></thead><tbody><tr><td>variety</td><td>1</td><td>2</td></tr><tr><td>cleanliness</td><td>5</td><td>5</td></tr></tbody></table>

**Takeaway 16.** Although much of the data visualization literature focuses on large datasets, we often do not know how to best visualize even very small datasets.

<figure><img src="image_73.png" /></figure>

**Takeaway 17.** Interesting data visualization research questions can arise from, and be tested with, small datasets.
<figure><img src="image_74.png" /></figure>
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