Once a week.
Subscribe to our weekly newsletter.
Exposing our hidden biases curbs their influence, new research suggests
Do you know the implicit biases you have? Here are some ways to find them out.
- A study finds that even becoming aware of your own implicit bias can help you overcome it.
- We all have biases. Some of them are helpful — others not so much.
When we talk about a bias, what we're talking about, as Harvard University social psychologist Mahzarin Banaji puts it, is a shortcut our brain has created so that we don't have spend time and energy thinking about how we feel each time we encounter something — we have an opinion already formed and ready to use.
Many of these shortcuts are useful: A bias against hangovers, for example, has one refusing alcohol without having to think about it. The problem is the brain does a lot of this shortcutting, silently. What's more, it creates shortcuts for people different than ourselves, sometimes based on actual personal experience, but often based on incorrect information we've unknowingly absorbed: other peoples' opinions, media depictions, cultural attitudes, for instance.
Worst of all, this kind of bias may be created and deployed without our even being aware of it — it's implicit in our actions in spite of ourselves and our conscious intentions.
Our brains don't always get things right. We make errors in judgement all of the time. An accurate bias is a great time-saver. An inaccurate bias is a serious problem, especially if it causes us to unknowingly discriminate against others. For instance, the systemic assumptions about women that keep them from advancing in scientific fields.
How we can curb the effects of implicit biases
Image source: Radachynskyi Serhii / Shutterstock / Big Think
New research, published in Nature Human Behavior on August 26, suggests the gender bias, which continues to prevent women from advancing in science, has a lot to do with its hidden underbelly — human blindspots. During the study, French researchers discovered that more women were promoted after the scientists in charge of awarding research positions became consciously aware of the impact of their implicit bias.
When it was no longer being highlighted, their biases discriminatory effect re-asserted itself, with award grants regressing to their traditional, pro-male pattern. Other research suggests that diversity training doesn't really help and may even exacerbate the problem it seeks to address.
We can glean a new approach, though — one that could result in better outcomes — from the new research.
About the study
Image source: Tartila/Shutterstock/Big Think
What the new study encouragingly reveals is that a conscious awareness of one's own hidden bias can mitigate its effect. The mechanism, it would appear, is that awareness may not delete the bias so much as make it less implicit, or unconscious.
The study looked at the awards handed out during annual nationwide competitions for elite French research positions. There were 414 people on the committees altogether, assessing candidates' worthiness across a spectrum of research specialties — "from particle physics to political sciences." The study analyzed committee-level data without digging too deeply into whether a committee was internally gender-balanced. The assumption was that the consensus decision reached by group represented the outcome of its internal makeup, whatever that may be.
The study took place over two years. In the first year, committee members were given Harvard's implicit association test (IAT), which established there was a significant implicit gender biases among them. Nonetheless, that year, the influence of such biases appeared to be significantly suppressed in the awards the committees handed out.
To the researchers, this outcome suggested that simply being aware of one's own implicit biases may take away their invisibility — the callout could make the bias more apparent and, therefore, something that can be more readily over-ridden.
The second year of the study, from the subjects' point of view at least, was quite silent. The researchers were still watching, but the issue of implicit bias wasn't called out. What ended up happening? The committee members returned to awarding more positions to men than women. A regression, it seemed.
It should be said, there are some possible flaws in the study: Perhaps the committee members were simply on their good behavior the first time around — until they thought that they were no longer being observed. Additionally, the study notes that there were more male submissions to the committees than female, which could skew the test. Further studies will need to be done to get a more accurate picture.
Nonetheless, the study's authors do conclude that becoming aware of one's own implicit biases may be the first step — maybe the most essential step — needed to overcome them.
How do I know if implicit bias is affecting my judgement?
Image source: AlexandreNunes / Shutterstock / Big Think
While the study looked at gender bias, of course, it's not the only variety to be concerned about, others pervade our culture: race bias, ethnicity bias, anti-LGBTQ bias, age bias, anti-Muslim bias, and so on. There are a couple of online methods available for sussing out our own. Note that if the researchers are correct, then just making yourself aware of your implicit biases can help you combat them.
The IAT mentioned above is one widely used way to identify your own bias issues. Project Implicit — from psychologists at Harvard, the University of Virginia, and the University of Washington — offers a self-test you can take. Be aware, though, that the IAT requires multiple tests to produce a meaningful result.
If you're willing to invest a little time, there's also the "bias cleanse" offered by MTV in partnership with the Kirwan Institute for the Study of Race and Ethnicity. It's a seven-day program aimed at helping you sort out implicit gender, race, or anti-LGBTQ biases you may be harboring. Each day you receive three eye-opening email thought exercises, one for each type of bias.
Side note: Did you know that more people die in female-named hurricanes because they're typically perceived as less threatening? We didn't.
It's a well-worn bromide that simply acknowledging you have a problem is the first step to solving it, but the new study provides supporting evidence that this is especially true when dealing with implicit biases — a pernicious, stubborn problem in our society. Our brains are clever beasties, silently putting together shortcuts that reduce our cognitive load. We just need to be smarter about seeing and consciously assessing them if we can ever hope to be the people that we hope to be. That may mean, on occasion, being humble enough to receive feedback in the form of callouts.
- Alexa, Siri reinforce gender biases, says United Nations - Big Think ›
- Can A.I. remove human bias from the hiring process? - Big Think ›
- The Difference Between Implicit Bias and Racism - Big Think ›
- How implicit biases hold us back - Big Think ›
Scientists discover what our human ancestors were making inside the Wonderwerk Cave in South Africa 1.8 million years ago.
- Researchers find evidence of early tool-making and fire use inside the Wonderwerk Cave in Africa.
- The scientists date the human activity in the cave to 1.8 million years ago.
- The evidence is the earliest found yet and advances our understanding of human evolution.
One of the oldest activities carried out by humans has been identified in a cave in South Africa. A team of geologists and archaeologists found evidence that our ancestors were making fire and tools in the Wonderwerk Cave in the country's Kalahari Desert some 1.8 million years ago.
A new study published in the journal Quaternary Science Reviews from researchers at the Hebrew University of Jerusalem and the University of Toronto proposes that Wonderwerk — which means "miracle" in Afrikaans — contains the oldest evidence of human activity discovered.
"We can now say with confidence that our human ancestors were making simple Oldowan stone tools inside the Wonderwerk Cave 1.8 million years ago," shared the study's lead author Professor Ron Shaar from Hebrew University.
Oldowan stone tools are the earliest type of tools that date as far back as 2.6 million years ago. An Oldowan tool, which was useful for chopping, was made by chipping flakes off of one stone by hitting it with another stone.
An Oldowan stone toolCredit: Wikimedia / Public domain
Professor Shaar explained that Wonderwerk is different from other ancient sites where tool shards have been found because it is a cave and not in the open air, where sample origins are harder to pinpoint and contamination is possible.
Studying the cave, the researchers were able to pinpoint the time over one million years ago when a shift from Oldowan tools to the earliest handaxes could be observed. Investigating deeper in the cave, the scientists also established that a purposeful use of fire could be dated to one million years back.
This is significant because examples of early fire use usually come from sites in the open air, where there is the possibility that they resulted from wildfires. The remnants of ancient fires in a cave — including burned bones, ash, and tools — contain clear clues as to their purpose.
To precisely date their discovery, the researchers relied on paleomagnetism and burial dating to measure magnetic signals from the remains hidden within a sedimentary rock layer that was 2.5 meters thick. Prehistoric clay particles that settled on the cave floor exhibit magnetization and can show the direction of the ancient earth's magnetic field. Knowing the dates of magnetic field reversals allowed the scientists to narrow down the date range of the cave layers.
The Kalahari desert Wonderwerk CaveCredit: Michael Chazan / Hebrew University of Jerusalem
Professor Ari Matmon of Hebrew University used another dating method to solidify their conclusions, focusing on isotopes within quartz particles in the sand that "have a built-in geological clock that starts ticking when they enter a cave." He elaborated that in their lab, the scientists were "able to measure the concentrations of specific isotopes in those particles and deduce how much time had passed since those grains of sand entered the cave."
Finding the exact dates of human activity in the Wonderwerk Cave could lead to a better understanding of human evolution in Africa as well as the way of life of our early ancestors.
If you ask your maps app to find "restaurants that aren't McDonald's," you won't like the result.
- The Chinese Room thought experiment is designed to show how understanding something cannot be reduced to an "input-process-output" model.
- Artificial intelligence today is becoming increasingly sophisticated thanks to learning algorithms but still fails to demonstrate true understanding.
- All humans demonstrate computational habits when we first learn a new skill, until this somehow becomes understanding.
It's your first day at work, and a new colleague, Kendall, catches you over coffee.
"You watch the game last night?" she says. You're desperate to make friends, but you hate football.
"Sure, I can't believe that result," you say, vaguely, and it works. She nods happily and talks at you for a while. Every day after that, you live a lie. You listen to a football podcast on the weekend and then regurgitate whatever it is you hear. You have no idea what you're saying, but it seems to impress Kendall. You somehow manage to come across as an expert, and soon she won't stop talking football with you.
The question is: do you actually know about football, or are you imitating knowledge? And what's the difference? Welcome to philosopher John Searle's "Chinese Room."
The Chinese Room
Searle's argument was designed as a critique of what's called a "functionalist" view of mind. This is the philosophy that argues that our mind can be explained fully by what role it plays, or in other words, what it does or what "function" it has.
One form of functionalism sees the human mind as following an "input-process-output" model. We have the input of our senses, the process of our brains, and a behavioral output. Searle thought this was at best an oversimplification, and his Chinese Room thought experiment goes to show how human minds are not simply biological computers. It goes like this:
Imagine a room, and inside is John, who can't speak a word of Chinese. Outside the room, a Chinese person sends a message into the room in Chinese. Luckily, John has an "if-then" book for Chinese characters. For instance, if he gets <你好吗>, the proper reply is <我还好>. All John has to do is follow his instruction book.
The Chinese speaker outside of the room thinks they're talking to someone inside who knows Chinese. But in reality, it's just John with his fancy book.
What is understanding?
Does John understand Chinese? The Chinese Room is, by all accounts, a computational view of the mind, yet it seems that something is missing. Truly understanding something is not an "if-then" automated response. John is missing that sinking in feeling, the absorption, the bit of understanding that's so hard to express. Understanding a language doesn't work like this. Humans are not Google Translate.
And yet, this is how AIs are programmed. A computer system is programmed to provide a certain output based on a finite list of certain inputs. If I double click the mouse, I open a file. If you type a letter, your monitor displays tiny black squiggles. If we press the right buttons in order, we win at Mario Kart. Input — Process — Output.
Can imitation become so fluid or competent that it is understanding.
But AIs don't know what they're doing, and Google Translate doesn't really understand what it's saying, does it? They're just following a programmer's orders. If I say, "Will it rain tomorrow?" Siri can look up the weather. But if I ask, "Will water fall from the clouds tomorrow?" it'll be stumped. A human would not (although they might look at you oddly).
A fun way to test just how little an AI understands us is to ask your maps app to find "restaurants that aren't McDonald's." Unsurprisingly, you won't get what you want.
The Future of AI
To be fair, the field of artificial intelligence is just getting started. Yes, it's easy right now to trick our voice assistant apps, and search engines can be frustratingly unhelpful at times. But that doesn't mean AI will always be like that. It might be that the problem is only one of complexity and sophistication, rather than anything else. It might be that the "if-then" rule book just needs work. Things like "the McDonald's test" or AI's inability to respond to original questions reveal only a limitation in programming. Given that language and the list of possible questions is finite, it's quite possible that AI will be able to (at the very least) perfectly mimic a human response in the not too distant future.
What's more, AIs today have increasingly advanced learning capabilities. Algorithms are no longer simply input-process-output but rather allow systems to search for information and adapt anew to what they receive.
A notorious example of this occurred when a Microsoft chat bot started spouting bigotry and racism after "learning" from what it read on Twitter. (Although, this might just say more about Twitter than AI.) Or, more sinister perhaps, two Facebook chat bots were shut down after it was discovered that they were not only talking to each other but were doing so in an invented language. Did they understand what they were doing? Who's to say that, with enough learning and enough practice, an AI "Chinese Room" might not reach understanding?
Can imitation become understanding?
We've all been a "Chinese Room" at times — be it talking about sports at work, cramming for an exam, using a word we didn't entirely know the meaning of, or calculating math problems. We can all mimic understanding, but it also begs the question: can imitation become so fluid or competent that it is understanding.
The old adage "fake it, 'till you make it" has been proven true over and over. If you repeat an action enough times, it becomes easy and habitual. For instance, when you practice a language, musical instrument, or a math calculation, then after a while, it becomes second nature. Our brain changes with repetition.
So, it might just be that we all start off as Chinese Rooms when we learn something new, but this still leaves us with a pertinent question: when, how, and at what point does John actually understand Chinese? More importantly, will Siri or Alexa ever understand you?
With the rise of Big Data, methods used to study the movement of stars or atoms can now reveal the movement of people. This could have important implications for cities.
- A treasure trove of mobility data from devices like smartphones has allowed the field of "city science" to blossom.
- I recently was part of team that compared mobility patterns in Brazilian and American cities.
- We found that, in many cities, low-income and high-income residents rarely travel to the same geographic locations. Such segregation has major implications for urban design.
Almost 55 percent of the world's seven billion people live in cities. And unless the COVID-19 pandemic puts a serious — and I do mean serious — dent in long-term trends, the urban fraction will climb almost to 70 percent by midcentury. Given that our project of civilization is staring down a climate crisis, the massive population shift to urban areas is something that could really use some "sciencing."
Is urbanization going to make things worse? Will it make things better? Will it lead to more human thriving or more grinding poverty and inequality? These questions need answers, and a science of cities, if there was such a thing, could provide answers.
Good news. There already is one!
The science of cities
With the rise of Big Data (for better or worse), scientists from a range of disciplines are getting an unprecedented view into the beating heart of cities and their dynamics. Of course, really smart people have been studying cities scientifically for a long time. But Big Data methods have accelerated what's possible to warp speed. As "exhibit A" for the rise of a new era of city science, let me introduce you to the field of "human mobility" and a new study just published by a team I was on.
Credit: nonnie192 / 405009778 via Adobe Stock
Human mobility is a field that's been amped up by all those location-enabled devices we carry around and the large-scale datasets of our activities, such as credit card purchases, taxi rides, and mobile phone usage. These days, all of us are leaving digital breadcrumbs of our everyday activities, particularly our movements around towns and cities. Using anonymized versions of these datasets (no names please), scientists can look for patterns in how large collections of people engage in daily travel and how these movements correlate with key social factors like income, health, and education.
There have been many studies like this in the recent past. For example, researchers looking at mobility patterns in Louisville, Kentucky found that low-income residents tended to travel further on average than affluent ones. Another study found that mobility patterns across different socioeconomic classes exhibit very similar characteristics in Boston and Singapore. And an analysis of mobility in Bogota, Colombia found that the most mobile population was neither the poorest nor the wealthiest citizens but the upper-middle class.
These were all excellent studies, but it was hard to make general conclusions from them. They seemed to point in different directions. The team I was part of wanted to get a broader, comparative view of human mobility and income. Through a partnership with Google, we were able to compare data from two countries — Brazil and the United States — of relatively equal populations but at different points on the "development spectrum." By comparing mobility patterns both within and between the two countries, we hoped to gain a better understanding of how people at different income levels moved around each day.
Mobility in Brazil vs. United States
Socioeconomic mobility "heatmaps" for selected cities in the U.S. and Brazil. The colors represent destination based on income level. Red depicts destinations traveled by low-income residents, while blue depicts destinations traveled by high-income residents. Overlapping areas are colored purple.Credit: Hugo Barbosa et al., Scientific Reports, 2021.
The results were remarkable. In a figure from our paper (shown above), it's clear that we found two distinct kinds of relationship between income and mobility in cities.
The first was a relatively sharp distinction between where people in lower and higher income brackets traveled each day. For example, in my hometown of Rochester, New York or Detroit, the places visited by the two income groups (e.g., job sites, shopping centers, doctors' offices) were relatively partitioned. In other words, people from low-income and high-income neighborhoods were not mixing very much, meaning they weren't spending time in the same geographical locations. In addition, lower income groups traveled to the city center more often, while upper income groups traveled around the outer suburbs.
The second kind of relationship was exemplified by cities like Boston and Atlanta, which didn't show this kind of partitioning. There was a much higher degree of mixing in terms of travel each day, indicating that income was less of a factor for determining where people lived or traveled.
In Brazil, however, all the cities showed the kind of income-based segregation seen in U.S. cities like Rochester and Detroit. There was a clear separation of regions visited with practically no overlap. And unlike the U.S., visits by the wealthy were strongly concentrated in the city centers, while the poor largely traversed the periphery.
Data-driven urban design
Our results have straightforward implications for city design. As we wrote in the paper, "To the extent that it is undesirable to have cities with residents whose ability to navigate and access resources is dependent on their socioeconomic status, public policy measures to mitigate this phenomenon are the need of the hour." That means we need better housing and public transportation policies.
But while our study shows there are clear links between income disparity and mobility patterns, it also shows something else important. As an astrophysicist who spent decades applying quantitative methods to stars and planets, I am amazed at how deep we can now dive into understanding cities using similar methods. We have truly entered a new era in the study of cities and all human systems. Hopefully, we'll use this new power for good.
A small percentage of people who consume psychedelics experience strange lingering effects, sometimes years after they took the drug.