🎾 Baseline Battles 🎾

“If I look at the mass, I will never act. If I look at the one, I will.” -Mother Teresa

In September 2018, Serena Williams disappeared.

Ok, not exactly.

Serena Williams is the greatest tennis player of my lifetime, and when any celebrity stops posting on social media, fans take notice. At the time, it did not seem terribly odd, because most of Williams’ fans knew that she was pregnant, and she likely stopped posting to spend time with her new baby.

However, when Williams resurfaced to the public, she announced that her extended absence was due to her labor and delivery and because she had terrible complications while giving birth. Scary stuff.

But what stood out about Williams’ heartfelt post was not only that she spoke about her near-death experience but how her experience was too common for women of color. Read the post below:

I didn’t expect that sharing our family’s story of Olympia’s birth and all of complications after giving birth would start such an outpouring of discussion from women — especially black women — who have faced similar complications and women whose problems go unaddressed.

These aren’t just stories: according to the CDC, (Center for Disease Control) black women are over 3 times more likely than White women to die from pregnancy- or childbirth-related causes. We have a lot of work to do as a nation and I hope my story can inspire a conversation that gets us to close this gap.

Let me be clear: EVERY mother, regardless of race, or background deserves to have a healthy pregnancy and childbirth. I personally want all women of all colors to have the best experience they can have. My personal experience was not great but it was MY experience and I’m happy it happened to me. It made me stronger and it made me appreciate women — both women with and without kids — even more. We are powerful!!!

I want to thank all of you who have opened up through online comments and other platforms to tell your story. I encourage you to continue to tell those stories. This helps. We can help others. Our voices are our power.

Williams’ experience, recounted in an excellent article in Vogue, gave her the opportunity to share with the world that women of color were three to four times more likely to die in childbirth than white women (even when you control for factors like education, income, etc.) If you need more proof, read this, or this, or this, or this.

This problem was not new or unknown if you knew where to look (and, for the record, I didn’t know about it until I saw this Facebook post). And, for the moment, the problem may be getting worse. But it raises two questions we will explore this week:

1. Why did Serena Williams’ story create greater awareness around a terrible inequity in the medical system staring in plain sight?
2. Does data promote equity?

We all need some productive discomfort from time to time.

Identifiable Victim Effect

Pretend you want to convince someone of an argument, and you can only provide one piece of information to justify your position. Which should you pick: a statistic or a personal story?

While naturally, your choice will depend on your audience, the evidence suggests that, in a vacuum, people are more convinced to act based on the story of a single person than the statistics in a large data set. This is the “identifiable victim effect” (IVE).

Attributed originally to the late Thomas Schelling, Schelling argues that a fundamental difference exists in how we react to an “individual life” versus a “statistical life.”

In popular culture, if you’ve ever wondered why people will act and donate money when they hear about a sob story on social media or television when the problem is much bigger and longstanding than that one person, you are watching the IVE in action.

Expanding upon Schelling’s original formulation, Karen Jenni and George Loewenstein identify four possible causes for IVE and why people gravitate to the individual life over the statistical life:

1. Vividness: “When an identifiable person is at risk of death…we may come to feel that we know them” (238). If you’ve heard a story and thought, “That could be my child/brother/sister/spouse,” you are experiencing the IVE.
2. Certainty vs. Uncertainty: “Identifiable deaths are usually certain to occur if action is not taken, whereas statistical deaths, by definition, are probabilistic” (239). Once you argue that death “might” happen, you start thinking of why it won’t…
3. Proportion of the Reference Group that Can be Saved: If you are told that X number of children die of Y cause yearly, X represents the total number of children out of all children worldwide (i.e. billions of children). However, “identifiable victims become their own reference group, creating a situation where n out of n people will die if action is not taken” (239).
4. Ex-Post Versus Ex-Ante Evaluation: Deciding to save one person is typically made “ex-post, or after, the occurrence of some risk-producing event.” However, addressing statistical risks means that one is typically making a decision “ex-ante, or before the risk-producing event has occurred” (239). While an ounce of prevention may be worth more than a pound of cure, we typically don’t act that way.

Now, let’s return to Serena Williams.

Women of color were far likelier to die in childbirth than white women, which means this problem likely affects millions (perhaps billions) of people. However, Serena Williams experiencing this made her an identifiable (potential) victim. Moreover, Serena Williams was a famous identifiable victim, compounding the impact of her story by attracting people’s attention to someone who already was wildly known.

IVE is a great example of how heuristics are neither inherently good nor bad; heuristics simply reflect how our minds work.

But here’s the troubling question:

If people are more inclined to save an identifiable life over a statistical mass of victims, how do we ensure we count the “right” things and tell the “right” stories?

Data Feminism

If you found Williams’ story compelling, and it’s hard not to, you might wonder what the data says about how to prevent the problem Williams describes on a grander scale, as it would be an unalloyed societal good to bridge gaps in maternal mortality. But before 2018, the sad answer was that “nobody was counting.”

Serena Williams’ story is one of many shared in Catherine D’Ignazio and Lauren Klein’s Data Feminism, a book that makes a cogent and comprehensive case for the scary truth that, in the wrong hands, data can support the same systems of oppression that data used in a different way could tear down.

D’Ignazio and Klein combine serious street cred in data analysis and critical theory, which are a dynamic duo if you want to look at how data is utilized from the lens of gender. And the authors argue that big data ultimately begins as small sets of data collected and, more importantly, chosen to be collected by “small groups of people and then scaled to users around the globe” (28).

And this is where the problem starts.

In other words, before you have a large data set that is capable of making evidence-based conclusions, at a granular level, you have small data samples people might choose to collect. And intentional or not, it is natural that people will pick different questions to explore based on their lived experienced. Given that there are well-documented representation issues in data science (27), it’s reasonable to assume that the very questions one chooses to analyze will be skewed toward the experiences of those doing the collecting.

As for the question of whether or not data promote equity, the answer is “It depends.” D’Iganzio and Klein argue that Serena Williams’ story was a societal ill staring in plain sight, but when confronted with the question of what kind of data analysis was done about maternal mortality and women of color, little could be found. But there is a tremendous danger to assuming that if there is no data on a topic, the topic is not important. Rather, we may confront some troubling truths when we identify questions that are critically important but lack any substantial data for anyone to analyze.

New Books in Critical Theory

What I Read This Week

1. Time To Take Laughter Seriously: I consider myself a serious person, sometimes too serious. But I love to laugh, and apparently, we need to take laughter seriously as a leadership best practice.
2. What Do You Do With An Oil Fortune?: I did not inherit billions of dollars, so it’s really hard for me to imagine what I would do if I had all the money I’d ever need, but that money came from an ethically compromised source. This profile of Leah Hart-Hendrix forced me to think.
3. Jewish Rave Scene?: No thanks, but it’s totally real. But as I mentioned, I doubt I will ever go to Burning Man for Shabbat; I’m pretty lame. But I may still make a career as a DJ…
4. Quest for a Purposeful God: The John Templeton Foundation funds thoughtful projects and research on religion. I love this article.
5. Race for a Room Temperature Superconductor: I’m unsure I understood this article. Feel free to explain it to me.