toa books of the year (2021, part four)

We resume our countdown toward The Most Irrelevant Prize in World Literature with a look at four of the books on the shortlist.

Daddy Was a Number Runner by Louise Meriwether (May)

TOA Review: not started (likely mid-2022)

Notes: Given how much I read, it's unusual for me to encounter a book that I suspect might live on the shelves of the "one of a kind" section down at the library, but this may be an appropriate description of Meriwether's story about Francie, a young girl in 1934 Harlem. Perhaps "first of its kind" is a closer fit, given that at the time of its release in 1970 a book centered around a Black girl's experience probably wasn't very common. It struck me while thinking about Daddy Was a Number Runner that there is definitely a discrepancy within my own reading list in terms of books about Black young men and boys having far greater representation than those about Black young women and girls, but I don't have much to say on this observation at the moment other than sharing my resolve to find some more reading to bring a sense of balance to my list.

I want to credit this post about the book by Deesha Philyaw, whose writing helped me think through some of the points I mentioned above. In addition to noting her collection as a future read, I also recognized that I would likely reread this book again at some point in the future. Quite frankly, to write such a review of any book is an unstated aim of mine, and in this example Philyaw accomplishes the main objective in my mind of such a piece - to make a reader want to pick up the book.  

Parting thought: There is a certain injustice when relief workers insist on deducting additional earnings from the original relief check.

A Swim in a Pond in the Rain by George Saunders (August)

TOA Review: not started (likely mid-2022)

Notes: This book is presented in seven sections, each with two components - a short story (not written by Saunders) followed by an analysis (written by Saunders). The short stories are excellent, but this book made my shortlist due to Saunders's examinations of each, detailing the works from the perspective of how structure, technique, and style contribute to the storytelling. This book finished in my top-three for 2021, so that's all for now - we'll hear more about it in the finals.

Parting thought: A strong structure works as a Q-and-A, where the author must respond to the questions that arise in the reader. A good writer, then, is one who is keenly aware of the questions a reader will have as the story flows forward; a good story knows how to respond to itself.

Parting thought #2: Good revision means constantly revising toward specificity, then allowing specificity to drive the story forward.

Parting thought #3: In a highly organized story, each event is precisely selected to escalate through causality.

Race After Technology by Ruha Benjamin (September)

TOA Review: not started (likely mid-2022)

Other notable TOA appearances: in October, I mused on some of the challenges with data collection, including the problem of data collection for the sake of data collection.

Notes: Benjamin explores the role of (recent) digital technology in reinforcing or perpetuating racism. Like with A Swim in the Pond in the Rain, this was a top-three read for me in 2021, so we'll return to this one in the finalist post.

Parting thought: People tend to behave in ways that contradict their stated beliefs - for example, by supporting single-payer healthcare, but resisting all attempts made to enforce higher taxes. The same goes for the tech industry, which claims to support regulation while resisting all attempts made to regulate its own activity. In these examples, political values are revealed more so in the action than in the statement.

Parting thought #2: Algorithms can code inequity, seen in examples such as higher paying jobs being shown to men or real estate ads being hidden from minorities.

Parting thought #3: Much as we use nutrition labels, perhaps regulators could find a way to demonstrate how certain tools or algorithms were created, with the information helping consumers understand the extent to which they may be reinforcing existing bias.

Tenth of December by George Saunders (December)

TOA Review: February 2022

Other notable TOA appearances: in the above review, there are additional links to three prior TOA appearances.

Notes: Wow, two books on the shortlist! I'm sure George Saunders considers this the pinnacle of his writing career (though he may be dismayed that this one does not advance to the top-three).

I think I've written enough about Tenth of December over the years that you might think I have nothing left to say about it. True! But I can steal from others, in this case a friend who pointed out that this was a tough book to read - not in the sense of an emotional challenge, but more so that Saunders uses a style which requires a bit of extra effort from the reader. Fair enough, but I think this is a lot like the situation of buying ramen - you can deal with the $1 Cup Noodles, or you can go someplace where the chef proves that extra effort is usually worth the price (TOA officially recommends Sapporo Ramen in Porter Square). So concludes the first and last paragraph in the history of this language that will mention both Tenth of December and Sapporo Ramen, the writing of which is perhaps the pinnacle of my writing career.

Parting thought: People will sometimes repeat the things they are doing, over and over, even if those things are rotten or evil, until they can convince themselves through repetition that it is normal.

the bias for data

I just finished taking down notes for Ruha Benjamin's Race After Technology, and the process confirmed that this will be on my list of best books from this year's reading. Benajmin's book focuses on the ways algorithms, applications, and programs, which are often presented to the public as neutral, can reinforce or deepen inequality, bias, and structural racism. I'm looking forward to collecting a few of my thoughts for a reading review later this year.

There is one idea I have been thinking about this week, which is related to Benjamin's comment regarding the way some will demand more data before committing to action. From her perspective, this comes up even when many experts are already in agreement regarding the necessary next step toward solving a problem. Benjamin uses the task of improving childhood education as an example of this phenomenon, noting how despite expert agreement that reducing poverty is the most important intervention toward achieving this goal, some demonstrate a certain perversion of knowledge by demanding to collect more data before committing to the intervention.

There are many reasons why some might demand more data in this type of situation, with cynical motivations certainly among the possibilities. However, what I've been thinking about this week regards the type of mentality that could lead to an innocent mistake - the tendency among data-driven thinkers to view the collection of data, either in volume or quality terms, as an unassailable strategy. There is, in other words, a bias for data, and this bias manifests in situations where data collection itself becomes both process and outcome, with no consideration allowed for whether additional information can improve the quality of upcoming decisions. The trend over the past few years has elevated the importance of being "data-driven" to the point where it would be unfathomable for a person, team, or organization to describe itself otherwise, but like with many empty buzzwords its strictest adherents would struggle to explain the drawbacks. The key distinction to me is that although having more data increases the odds of making the right decision, there is no guarantee that collecting additional data in a given moment will increase the odds of making the right decision.

The question in my mind as it relates to Benjamin's example is how to separate the cynical intent from the innocent errors made by those who have stumbled blindly into the cult of the data-driven approach. But in a general sense, the distinction may be a trivial one, for those who believe more is always better will never sate their appetite for additional data, which means their behavior will always be indistinguishable from those in outright opposition. The good intent of data collection simply hides the fact that this mentality has more in common with certain sins like gluttony or greed, which are likewise defined by the inability to know when enough is enough. I would prefer that we generally adopt a more careful approach to data collection. I think it's impossible to effectively adopt a data-driven mindset if those in charge of a project cannot identify how much data is necessary to reach their outcome; those collecting additional data for its own sake likely do not understand the issue at the core of their specific problem. 

I've had this on my mind over the past week because I recently realized that I will soon encounter this type of situation in my work. Over the next few months, I'll join a workgroup seeking to drive progress against a set of inclusion, diversity, and equity goals within the organization. I'm unsure about how to implement or even introduce my approach because I fear it will challenge and possibly threaten some members, particularly those who perceive themselves as data-driven without having given the label a great deal of thought. Should I simply demand to see the plan that would be set in motion given the accumulation of more data? Or is it best to start with the idea, then work out a way to apply the philosophy to the situation? It may be wise to simply point out what I think is plainly evident - if the goal to improve decisions necessitates collecting better data, then the obvious question is to find a consensus regarding the point where we have enough higher quality data. In healthcare, there is a concept defined by HIPPA known as "minimum necessary", and perhaps invoking this principle could help us find the right starting point.

My fear is that we will make the kinds of mistakes that result from good intentions. For example, we may decide that certain decisions will be driven by the kind of information we can extract from only the highest quality data. This sounds good in the planning stage, but it's easy to imagine that we'll have more success collecting such data from those who are the easiest to collect from. And who would be the easiest to collect from? My strong hunch is that we'll collect from those already affiliated with our organization, excluding the potential supporter groups that we are trying to include in our work, which would only reinforce the existing structural challenges that prompted the establishment of the workgroup in the first place. This concern will remain right at the front of my mind, placed there perhaps by what I've recently read and noted from Race Against Technology, which maintains this kind of thinking as its unofficial theme - we must think seriously about how we use so-called neutral tools, and remember that their neutrality is no guarantee we won't use them to reinforce existing biases.