Truth or Consequences?
Truth. It’s all the rage. For a person with three philosophy degrees that ought to seem a bonanza. But it is a reminder of both how much and how little people understand about knowledge and, more particularly, objectivity.
I’ve recently participated in a couple of conversations that Techstars has run on the subject of so-called “truth tech,” an emerging category which could include everything from AI/ML to work at the Jigsaw unit at Google that “that explores threats to open societies, and builds technology that inspires scalable solutions.”
In those conversations, participants where asked to consider the viability of a “truth scale” that we might use to categorize levels of truthfulness by considering everything from validity, to credibility to reputation of sources. Of course, there’s nothing new under the sun here, in terms of criteria. The question is…“how might we automate or use machine learning to process” data and sources to produce reliable indicators of, what? Their Truth, one supposes.
There’s a ground floor problem, however, that this project must reckon with before it can even get out of the garage: it is precisely the Truth that is contested. The various data, knowledge and sources which support the idea that deleterious climate change is caused by human actions and systems, for example, are not convincing to those who “deny” climate change. An appeal to Truth, here, settles nothing.
Many object to the idea that there is any such thing as systemic racism. A close examination of the disagreements that exist between people who see racism as systemic and those that don’t tends to reveal deeper disagreements about what racism is in the first place, let alone how we make the leap from the regular and disproportionate number of police killings of Black people to the idea that this is evidence that a “system” is at work.
People on “both sides” of such conversations shake their heads at each other, bemoaning each other’s gullibility or blindness. Neither believes the other is in good faith. They are either stupid or partisan, and most likely both.
Who really thinks that this is a problem that Siri or Alexa are likely to help us sort out?
If we want to have conversations that build understanding, that change how people see and what they see, then we’d do well to think about the history of knowledge and of science. The history of scientific knowledge, technologies and practice are extremely helpful in this regard. Inventions like telescopes, microscopes and x-ray crystallography change both what we see and how we see. All of these are technologies that rely on optics to create evidence.
Racism is a complex optic that has emerged from activism (from abolitionism to BLM) sociology and law to name only a few of the many sources that have contributed to a discourse on race and white supremacy. We sometimes speak as if we believe that these concepts are transparent, self-evident and uncontroversial. And yet, you don’t have to sit long among those having conversations about race, who initially appear to agree about the reality of racism, before very different and conflicting points of view on the subject emerge.
What can help us see more clearly? We can begin by learning through study that objectivity is rarely what we think it is. Objectivity is a highly complex notion in scientific and philosophical discourse, and one does not have to read far in the professional literature of any discipline to find disagreements.
Objectivity in law is something else altogether. My Ph.D thesis was concerned with the history of the concept of objectivity in the law of self-defense, as expressed in the so-called “reasonable man” standard. Historical study shows that this standard concealed sexist (and other forms of) bias until the law’s “objectivity” was challenged by feminist lawyers who showed how this standard failed to account for the fact pattern of the experiences of abused women.
The feminist challenge to the “reasonable man” standard in the law of self-defense did not make the argument that the law should not aspire to objectivity (which in law is intended to provide mechanisms that ensure fairness and equality of treatment). Instead, this challenge revealed the unfairness of gender bias that was hiding in plain sight within the not-so-neutral concept of the “reasonable man.” Ideas like objectivity, in law and in science, have consequences. They have histories. And examination of those histories can reveal bias and/or partiality. These are sometimes human failings, these are sometimes deliberate technologies of repression and control.
We need to have more conversations about consequences. People’s beliefs, in the fairness of an election, for example, are the product of many factors and may be difficult or even impossible to settle with bare facts. For those who believed that the 2020 Presidential election was “stolen,” part and parcel of that belief was a fundamental distrust of the sources that confirmed the Truth of the Biden win. Many people asserted that Donald Trump was not legitimately elected as the 45th President of the United States because he did not win the popular vote. But this is to confuse objectivity and legitimacy. Trump’s election was a consequence of the way the Electoral College system works. We can argue that that system is a bad one, but we cannot argue that it is the one by which we objectively determine the outcome of elections in the United States.
What we know and how we know changes over time. Sometimes, but not always, for the better. Human limitations, cultural biases and political history are ineliminable elements of how we produce knowledge. We need to abandon appeals to some messianic conception of Truth as our savior. We need to start reckoning with the reality that all forms of truth have consequences.
