Monthly Archives: July 2022

The Biases of Artificial Intelligence: Our Devils are in the Data

I believe that – over time – technology does move us forward. I further believe that, even with all the unintended consequences it brings, technology has made the world a better place to live in. I would rather step forward with my children and grandchildren (the first of which has just arrived) into a more advanced world than step backwards in the world of my grandparents, or my great grandparents. We now have a longer and better life, thanks in large part to technology. This, I’m sure, makes me a techno-optimist.

But my optimism is of a pragmatic sort. I’m fully aware that it is not a smooth path forward. There are bumps and potholes aplenty along the way. I accept that along with my optimism

Technology, for example, does not play all that fairly. Techno-optimists tend to be white and mostly male. They usually come from rich countries, because technology helps rich countries far more than it helps poor ones. Technology plays by the same rules as trickle-down economics: a rising tide that will eventually raise all boats, just not at the same rate.

Take democracy, for instance. In June 2009, journalist Andrew Sullivan declared “The revolution will be Twittered!” after protests erupted in Iran. Techno-optimists and neo-liberals were quick to declare social media and the Internet as the saviour of democracy. But, even then, the optimism was premature – even misplaced.

In his book The Net Delusion: The Dark Side of Internet Freedom, journalist and social commentator Evgeny Morozov details how digital technologies have been just as effectively used by repressive regimes to squash democracy. The book was published in 2011. Just 5 years later, that same technology would take the U.S. on a path that came perilously close to dismantling democracy. As of right now, we’re still not sure how it will all work out. As Morozov reminds us, technology – in and of itself – is not an answer. It is a tool. Its impact will be determined by those that built the tool and, more importantly, those that use the tool.

Also, tools are not built out of the ether. They are necessarily products of the environment that spawned them. And this brings us to the systemic problems of artificial intelligence.

Search is something we all use every day. And we probably didn’t think that Google (or other search engines) are biased, or even racist. But a recent study published in the journal Proceedings of the National Academy of Sciences, shows that the algorithms behind search are built on top of the biases endemic in our society.

“There is increasing concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are embedded,” says Madalina Vlasceanu, a postdoctoral fellow in New York University’s psychology department and the paper’s lead author.

To assess possible gender bias in search results, the researchers examined whether words that should refer with equal probability to a man or a woman, such as “person,” “student,” or “human,” are more often assumed to be a man. They conducted Google image searches for “person” across 37 countries. The results showed that the proportion of male images yielded from these searches was higher in nations with greater gender inequality, revealing that algorithmic gender bias tracks with societal gender inequality.

In a 2020 opinion piece in the MIT Technology Review, researcher and AI activist Deborah Raji wrote:

“I’ve often been told, ‘The data does not lie.’ However, that has never been my experience. For me, the data nearly always lies. Google Image search results for ‘healthy skin’ show only light-skinned women, and a query on ‘Black girls’ still returns pornography. The CelebA face data set has labels of ‘big nose’ and ‘big lips’ that are disproportionately assigned to darker-skinned female faces like mine. ImageNet-trained models label me a ‘bad person,’ a ‘drug addict,’ or a ‘failure.”’Data sets for detecting skin cancer are missing samples of darker skin types. “

Deborah Raji, MIT Technology Review

These biases in search highlight the biases in a culture. Search brings back a representation of content that has been published online; a reflection of a society’s perceptions. In these cases, the devil is in the data. The search algorithm may not be inherently biased, but it does reflect the systemic biases of our culture. The more biased the culture, the more it will be reflected in technologies that comb through the data created by that culture. This is regrettable in something like image search results, but when these same biases show up in the facial recognition software used in the justice system, it can be catastrophic.

In article in Penn Law’s Regulatory Review, the authors reported that, “In a 2019  National Institute of Standards and Technology report, researchers studied 189 facial recognition algorithms—“a majority of the industry.” They found that most facial recognition algorithms exhibit bias. According to the researchers, facial recognition technologies falsely identified Black and Asian faces 10 to 100 times more often than they did white faces. The technologies also falsely identified women more than they did men—making Black women particularly vulnerable to algorithmic bias. Algorithms using U.S. law enforcement images falsely identified Native Americans more often than people from other demographics.”

Most of these issues lie with how technology is used. But how about those that build the technology? Couldn’t they program the bias out of the system?

There we have a problem. The thing about societal bias is that it is typically recognized by its victims, not those that propagate it. And the culture of the tech industry is hardly gender balanced nor diverse.  According to a report from the McKinsey Institute for Black Economic Mobility, if we followed the current trajectory, experts in tech believe it would take 95 years for Black workers to reach an equitable level of private sector paid employment.

Facebook, for example, barely moved one percentage point from 3% in 2014 to 3.8% in 2020 with respect to hiring Black tech workers but improved by 8% in those same six years when hiring women. Only 4.3% of the company’s workforce is Hispanic. This essential whiteness of tech extends to the field of AI as well.

Yes, I’m a techno-optimist, but I realize that optimism must be placed in the people who build and use the technology. And because of that, we must try harder. We must do better. Technology alone isn’t the answer for a better, fairer world.  We are.

The Physical Foundations of Friendship

It’s no secret that I worry about what the unintended consequences might be for us as we increasingly substitute a digital world for a physical one. What might happen to our society as we spend less time face-to-face with people and more time face-to-face with a screen?

Take friendship, for example. I have written before about how Facebook friends and real friends are not the same thing. A lot of this has to do with the mental work required to maintain a true friendship. This cognitive requirement led British anthropologist Robin Dunbar to come up with something called Dunbar’s Number – a rough rule-of-thumb that says we can’t really maintain a network of more than 150 friends, give or take a few.

Before you say, “I have way more friends on Facebook than that,” realize that I don’t care what your Facebook Friend count is. Mine numbers at least 3 times more than Dunbar’s 150 limit. But they are not all true friends. Many are just the result of me clicking a link on my laptop. It’s quick, it’s easy, and there is absolutely no requirement to put any skin in the game. Once clicked, I don’t have to do anything to maintain these friendships. They are just part of a digital tally that persists until I might click again, “unfriending” them. Nowhere is the ongoing physical friction that demands the maintenance required to keep a true friendship from slipping into entropy.

So I was wondering – what is that magical physical and mental alchemy that causes us to become friends with someone in the first place? When we share physical space with another human, what is the spark that causes us to want to get to know them better? Or – on the flip side – what are the red flags that cause us to head for the other end of the room to avoid talking to them? Fortunately, there is some science that has addressed those questions.

We become friends because of something in sociology call homophily – being like each other. In today’s world, that leads to some unfortunate social consequences, but in our evolutionary environment, it made sense. It has to do with kinship ties and what ethologist Richard Dawkins called The Selfish Gene. We want family to survive to pass on our genes. The best way to motivate us to protect others is to have an emotional bond to them. And it just so happens that family members tend to look somewhat alike. So we like – or love – others who are like us.

If we tie in the impact of geography over our history, we start to understand why this is so. Geography that restricted travel and led to inbreeding generally dictated a certain degree of genetic “sameness” in our tribe. It was a quick way to sort in-groups from out-groups. And in a bloodier, less politically correct world, this was a matter of survival.

But this geographic connection works both ways. Geographic restrictions lead to homophily, but repeated exposure to the same people also increases the odds that you’ll like them. In psychology, this is called mere-exposure effect.

In these two ways, the limitations of a physical world has a deep, deep impact on the nature of friendship. But let’s focus on the first for a moment. 

It appears we have built-in “friend detectors” that can actually sense genetic similarities. In a rather fascinating study, Nicholas Christakis and James Fowler found that friends are so alike genetically, they could actually be family. If you drill down to the individual building blocks of a gene at the nucleotide level, your friends are as alike genetically to you as your fourth cousin. As Christakis and Fowler say in their study, “friends may be a kind of ‘functional kin’.”

This shows how deeply friendships bonds are hardwired into us. Of course, this doesn’t happen equally across all genes. Evolution is nothing if not practical. For example, Christakis and Fowler found that specific systems do stay “heterophilic” (not alike) – such as our immune system. This makes sense. If you have a group of people who stay in close proximity to each other, it’s going to remain more resistant to epidemics if there is some variety in what they’re individually immune to. If everyone had exactly the same immunity profile, the group would be highly resistant to some bugs and completely vulnerable to others. It would be putting all your disease prevention eggs in one basket.

But in another example of extreme genetic practicality, how similar we smell to our friends can be determined genetically.  Think about it. Would you rather be close to people who generally smell the same, or those that smell different? It seems a little silly in today’s world of private homes and extreme hygiene, but when you’re sharing very close living quarters with others and there’s no such thing as showers and baths, how everyone smells becomes extremely important.

Christakis and Fowler found that our olfactory sensibilities tend to trend to the homophilic side between friends. In other words, the people we like smell alike. And this is important because of something called olfactory fatigue. We use smell as a difference detector. It warns us when something is not right. And our nose starts to ignore smells it gets used to, even offensive ones. It’s why you can’t smell your own typical body odor. Or, in another even less elegant example, it’s why your farts don’t stink as much as others. 

Given all this, it would make sense that if you had to spend time close to others, you would pick people who smelled like you. Your nose would automatically be less sensitive to their own smells. And that’s exactly what a new study from the Weizmann Institute of Science found. In the study, the scent signatures of complete strangers were sampled using an electronic sniffer called an eNose. Then the strangers were asked to engage in nonverbal social interactions in pairs. After, they were asked to rate each interaction based on how likely they would be to become friends with the person. The result? Based on their smells alone, the researchers were able to predict with 71% accuracy who would become friends.

The foundations of friendship run deep – down to the genetic building blocks that make us who we are. These foundations were built in a physical world over millions of years. They engage senses that evolved to help us experience that physical world. Those foundations are not going to disappear in the next decade or two, no matter how addictive Facebook or TikTok becomes. We can continue to layer technology over these foundations, but to deny them it to ignore human nature.