Welcome to Week 67 of Be the Change!

With a Grain of Salt

1. Expertise matters. As much as the Internet age has empowered us all to act like experts, weighing in on pandemics, terrorism, voting rights, and immigration as easily as we make an espresso, the modern world is highly complex and specialized. Because we cannot know everything, we have no choice but to rely on experts. I think we've come to conflate expertise with elitism, but the two are not the same, even if some experts come from elite institutions or act snobbishly. If we want to understand climate change, we should look to a climate scientist; if a climate scientist weighs in on COVID vaccines, we shouldn't put as much stock in their thoughts on that issue. Check the expert's credentials and their relevance to the issue at hand!
2. Understand a person's funding and incentives. While expertise is important, experts can be corrupted: scientists can be paid by fossil fuel companies to obscure the science of climate change, for instance, and you can find an economist who can match a dataset to any ideology. It is imperative, then, to understand what an expert, politician, TV personality, influencer, or anyone's incentives are. If you see an ad from a purportedly grassroots group called Save Our Coast claiming that an offshore wind project is going to destroy property values and wildlife, ask yourself who is behind it. Is it really grassroots or, as Michael Thomas found through excellent investigative journalism, is it funded by a right-wing foundation like the State Policy Network? Understanding who is behind an ad, campaign, essay, or post makes it much easier to weigh the strength of the argument at play. Powerful people like the Koch Brothers know this; it's why they go to such lengths to hide their influence, as Jane Meyer brilliantly illustrated in her book about them, Dark Money: The Hidden History of the Billionaires Behind the Rise of the Radical Right.
3. Correlation is not causation. This is logic 101, but boy do we forget that just because B happened after A does not mean that A caused B. The classic example is that although rates of murder go up around the same time that sales of ice cream increase, it is erroneous to say that ice cream causes murder. (Murder rates are higher in summer and, of course, people buy ice cream when it's hot.) The gold standard for determining causality is a double-blind randomized control trial, the kind that is required before most drugs can be approved by the FDA for use in treating, say, diabetes or high blood pressure. Unfortunately, these are expensive and time-consuming; for most things, we cannot rely on there being such a study to determine whether a relationship between two things is causal or correlative. That leaves us in the position of having to pose the question. If we lack the expertise to answer it, then what? Well, we can see what the experts say, ask ourselves what the motivations are of the people making the claim, or look up opposing views. While this seems hard and time-consuming, we do have access to the world's information in our pockets; not just Google, but tools like Semantic Scholar, which allows you to use AI to search scientific literature--for free!
4. Lies, damn lies, and statistics. It wasn't until Capital Good Fund grew to the point that we had a reasonably large dataset to analyze--for social impact, for identifying what variables on a loan application have a causal connection to loan performance--that I realized how careful we have to be when presented with data of any kind. Humanity is awash in petabytes of data; data may be the new gold, or the new oil, but it must be refined through reason and common sense before being consumed. Unsurprisingly, the conclusions we draw are only as good as the quality of the data we are using, and there, we run into problems: of omission, of deception, of carelessness. Consider that over 30% of credit reports contain errors, some of which are material enough to lower a person's FICO score to the point that they are denied a loan, or receive a higher interest rate. Even when the data is "clean," we have to make decisions about it. For instance, at Capital Good Fund we want to serve people that are low-income, which is defined as 80% of Area Median Income (AMI). Okay, seems straightforward enough. But what year do we use as the benchmark? Do we look at AMI for the state, or the county in which the applicant lives? Do we factor in household size or use an average figure? How hard do we work to verify every penny of the applicant's income, versus determining their % of AMI based on what they report on the application? Do we use their income for the prior year, or projected for this year? Very quickly, we go from what, on paper, is a basic calculation, to a series of messy decisions made at the fast-moving pace of a nonprofit online lender trying to balance scale with social impact. The best we can hope for from data providers is transparency about the sources and limitations of their data, as well as the choices they made in their analysis. Needless to say, few companies, authors, journalists, pundits, or advertisers bother with this transparency, preferring instead to make bold claims on weak data, or to draw the wrong conclusion from good data. It is in fact so easy to use statistics to justify your point of view, yet so hard for the average person to have the means, knowledge, or time to debunk a statistical claim, that this may be one of the most pernicious forms of misinformation and disinformation out there. Again, ask yourself who is making a claim, what their ideological and financial incentives are, and what conclusions others are drawing from the same datasets. You don't need a PhD in physics to question whether one scientist, funded by Exxon Mobil, is misusing data when he says that, unlike 99% of the scientific community, he can prove that the planet is actually cooling. The saying that extraordinary claims require extraordinary evidence is apt.
5. Try to keep emotion out of it. Social media algorithms are really good at promoting content that angers, outrages, or horrifies. If we see a meme about some terrible thing a politician or company did, we are inclined to pounce on our anger and like, retweet, and tell our friends about it. In minutes, something that may be false can spread like wildfire, and post-facto attempts to correct the story are unlikely to put the genie back in the bottle. We would all benefit from taking a breath before reacting to something we read or hear, from filtering news through our logic centers before it enters our centers of emotion.