Tag Archives: times

#434534 To Extend Our Longevity, First We Must ...

Posted on January 25, 2019 by Android

Healthcare today is reactive, retrospective, bureaucratic, and expensive. It’s sick care, not healthcare.

But that is radically changing at an exponential rate.

Through this multi-part blog series on longevity, I’ll take a deep dive into aging, longevity, and healthcare technologies that are working together to dramatically extend the human lifespan, disrupting the $3 trillion healthcare system in the process.

I’ll begin the series by explaining the nine hallmarks of aging, as explained in this journal article. Next, I’ll break down the emerging technologies and initiatives working to combat these nine hallmarks. Finally, I’ll explore the transformative implications of dramatically extending the human health span.

In this blog I’ll cover:

Why the healthcare system is broken
Why, despite this, we live in the healthiest time in human history
The nine mechanisms of aging

Let’s dive in.

The System is Broken—Here’s the Data:

Doctors spend $210 billion per year on procedures that aren’t based on patient need, but fear of liability.
Americans spend, on average, $8,915 per person on healthcare—more than any other country on Earth.
Prescription drugs cost around 50 percent more in the US than in other industrialized countries.
At current rates, by 2025, nearly 25 percent of the US GDP will be spent on healthcare.
It takes 12 years and $359 million, on average, to take a new drug from the lab to a patient.
Only 5 in 5,000 of these new drugs proceed to human testing. From there, only 1 of those 5 is actually approved for human use.

And Yet, We Live in the Healthiest Time in Human History
Consider these insights, which I adapted from Max Roser’s excellent database Our World in Data:

Right now, the countries with the lowest life expectancy in the world still have higher life expectancies than the countries with the highest life expectancy did in 1800.
In 1841, a 5-year-old had a life expectancy of 55 years. Today, a 5-year-old can expect to live 82 years—an increase of 27 years.
We’re seeing a dramatic increase in healthspan. In 1845, a newborn would expect to live to 40 years old. For a 70-year-old, that number became 79. Now, people of all ages can expect to live to be 81 to 86 years old.
100 years ago, 1 of 3 children would die before the age of 5. As of 2015, the child mortality rate fell to just 4.3 percent.
The cancer mortality rate has declined 27 percent over the past 25 years.

Figure: Around the globe, life expectancy has doubled since the 1800s. | Image from Life Expectancy by Max Roser – Our World in Data / CC BY SA
Figure: A dramatic reduction in child mortality in 1800 vs. in 2015. | Image from Child Mortality by Max Roser – Our World in Data / CC BY SA
The 9 Mechanisms of Aging
*This section was adapted from CB INSIGHTS: The Future Of Aging.

Longevity, healthcare, and aging are intimately linked.

With better healthcare, we can better treat some of the leading causes of death, impacting how long we live.

By investigating how to treat diseases, we’ll inevitably better understand what causes these diseases in the first place, which directly correlates to why we age.

Following are the nine hallmarks of aging. I’ll share examples of health and longevity technologies addressing each of these later in this blog series.

Genomic instability: As we age, the environment and normal cellular processes cause damage to our genes. Activities like flying at high altitude, for example, expose us to increased radiation or free radicals. This damage compounds over the course of life and is known to accelerate aging.
Telomere attrition: Each strand of DNA in the body (known as chromosomes) is capped by telomeres. These short snippets of DNA repeated thousands of times are designed to protect the bulk of the chromosome. Telomeres shorten as our DNA replicates; if a telomere reaches a certain critical shortness, a cell will stop dividing, resulting in increased incidence of disease.
Epigenetic alterations: Over time, environmental factors will change how genes are expressed, i.e., how certain sequences of DNA are read and the instruction set implemented.
Loss of proteostasis: Over time, different proteins in our body will no longer fold and function as they are supposed to, resulting in diseases ranging from cancer to neurological disorders.
Deregulated nutrient-sensing: Nutrient levels in the body can influence various metabolic pathways. Among the affected parts of these pathways are proteins like IGF-1, mTOR, sirtuins, and AMPK. Changing levels of these proteins’ pathways has implications on longevity.
Mitochondrial dysfunction: Mitochondria (our cellular power plants) begin to decline in performance as we age. Decreased performance results in excess fatigue and other symptoms of chronic illnesses associated with aging.
Cellular senescence: As cells age, they stop dividing and cannot be removed from the body. They build up and typically cause increased inflammation.
Stem cell exhaustion: As we age, our supply of stem cells begins to diminish as much as 100 to 10,000-fold in different tissues and organs. In addition, stem cells undergo genetic mutations, which reduce their quality and effectiveness at renovating and repairing the body.
Altered intercellular communication: The communication mechanisms that cells use are disrupted as cells age, resulting in decreased ability to transmit information between cells.

Conclusion
Over the past 200 years, we have seen an abundance of healthcare technologies enable a massive lifespan boom.

Now, exponential technologies like artificial intelligence, 3D printing and sensors, as well as tremendous advancements in genomics, stem cell research, chemistry, and many other fields, are beginning to tackle the fundamental issues of why we age.

In the next blog in this series, we will dive into how genome sequencing and editing, along with new classes of drugs, are augmenting our biology to further extend our healthy lives.

What will you be able to achieve with an extra 30 to 50 healthy years (or longer) in your lifespan? Personally, I’m excited for a near-infinite lifespan to take on moonshots.

Join Me
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Posted in Human Robots

#434492 Black Mirror’s ‘Bandersnatch’ ...

Posted on January 20, 2019 by Android

When was the last time you watched a movie where you could control the plot?

Bandersnatch is the first interactive film in the sci fi anthology series Black Mirror. Written by series creator Charlie Brooker and directed by David Slade, the film tells the story of young programmer Stefan Butler, who is adapting a fantasy choose-your-own-adventure novel called Bandersnatch into a video game. Throughout the film, viewers are given the power to influence Butler’s decisions, leading to diverging plots with different endings.

Like many Black Mirror episodes, this film is mind-bending, dark, and thought-provoking. In addition to innovating cinema as we know it, it is a fascinating rumination on free will, parallel realities, and emerging technologies.

Pick Your Own Adventure
With a non-linear script, Bandersnatch is a viewing experience like no other. Throughout the film viewers are given the option of making a decision for the protagonist. In these instances, they have 10 seconds to make a decision until a default decision is made. For example, in the early stage of the plot, Butler is given the choice of accepting or rejecting Tuckersoft’s offer to develop a video game and the viewer gets to decide what he does. The decision then shapes the plot accordingly.

The video game Butler is developing involves moving through a graphical maze of corridors while avoiding a creature called the Pax, and at times making choices through an on-screen instruction (sound familiar?). In other words, it’s a pick-your-own-adventure video game in a pick-your-own-adventure movie.

Many viewers have ended up spending hours exploring all the different branches of the narrative (though the average viewing is 90 minutes). One user on reddit has mapped out an entire flowchart, showing how all the different decisions (and pseudo-decisions) lead to various endings.

However, over time, Butler starts to question his own free will. It’s almost as if he’s beginning to realize that the audience is controlling him. In one branch of the narrative, he is confronted by this reality when the audience indicates to him that he is being controlled in a Netflix show: “I am watching you on Netflix. I make all the decisions for you”. Butler, as you can imagine, is horrified by this message.

But Butler isn’t the only one who has an illusion of choice. We, the seemingly powerful viewers, also appear to operate under the illusion of choice. Despite there being five main endings to the film, they are all more or less the same.

The Science Behind Bandersnatch
The premise of Bandersnatch isn’t based on fantasy, but hard science. Free will has always been a widely-debated issue in neuroscience, with reputable scientists and studies demonstrating that the whole concept may be an illusion.

In the 1970s, a psychologist named Benjamin Libet conducted a series of experiments that studied voluntary decision making in humans. He found that brain activity imitating an action, such as moving your wrist, preceded the conscious awareness of the action.

Psychologist Malcom Gladwell theorizes that while we like to believe we spend a lot of time thinking about our decisions, our mental processes actually work rapidly, automatically, and often subconsciously, from relatively little information. In addition to this, thinking and making decisions are usually a byproduct of several different brain systems, such as the hippocampus, amygdala, and prefrontal cortex working together. You are more conscious of some information processes in the brain than others.

As neuroscientist and philosopher Sam Harris points out in his book Free Will, “You did not pick your parents or the time and place of your birth. You didn’t choose your gender or most of your life experiences. You had no control whatsoever over your genome or the development of your brain. And now your brain is making choices on the basis of preferences and beliefs that have been hammered into it over a lifetime.” Like Butler, we may believe we are operating under full agency of our abilities, but we are at the mercy of many internal and external factors that influence our decisions.

Beyond free will, Bandersnatch also taps into the theory of parallel universes, a facet of the astronomical theory of the multiverse. In astrophysics, there is a theory that there are parallel universes other than our own, where all the choices you made are played out in alternate realities. For instance, if today you had the option of having cereal or eggs for breakfast, and you chose eggs, in a parallel universe, you chose cereal. Human history and our lives may have taken different paths in these parallel universes.

The Future of Cinema
In the future, the viewing experience will no longer be a passive one. Bandersnatch is just a glimpse into how technology is revolutionizing film as we know it and making it a more interactive and personalized experience. All the different scenarios and branches of the plot were scripted and filmed, but in the future, they may be adapted real-time via artificial intelligence.

Virtual reality may allow us to play an even more active role by making us participants or characters in the film. Data from your history of preferences and may be used to create a unique version of the plot that is optimized for your viewing experience.

Let’s also not underestimate the social purpose of advancing film and entertainment. Science fiction gives us the ability to create simulations of the future. Different narratives can allow us to explore how powerful technologies combined with human behavior can result in positive or negative scenarios. Perhaps in the future, science fiction will explore implications of technologies and observe human decision making in novel contexts, via AI-powered films in the virtual world.

Image Credit: andrey_l / Shutterstock.com

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Posted in Human Robots

#434336 These Smart Seafaring Robots Have a ...

Posted on January 16, 2019 by Android

Drones. Self-driving cars. Flying robo taxis. If the headlines of the last few years are to be believed, terrestrial transportation in the future will someday be filled with robotic conveyances and contraptions that will require little input from a human other than to download an app.

But what about the other 70 percent of the planet’s surface—the part that’s made up of water?

Sure, there are underwater drones that can capture 4K video for the next BBC documentary. Remotely operated vehicles (ROVs) are capable of diving down thousands of meters to investigate ocean vents or repair industrial infrastructure.

Yet most of the robots on or below the water today still lean heavily on the human element to operate. That’s not surprising given the unstructured environment of the seas and the poor communication capabilities for anything moving below the waves. Autonomous underwater vehicles (AUVs) are probably the closest thing today to smart cars in the ocean, but they generally follow pre-programmed instructions.

A new generation of seafaring robots—leveraging artificial intelligence, machine vision, and advanced sensors, among other technologies—are beginning to plunge into the ocean depths. Here are some of the latest and most exciting ones.

The Transformer of the Sea
Nic Radford, chief technology officer of Houston Mechatronics Inc. (HMI), is hesitant about throwing around the word “autonomy” when talking about his startup’s star creation, Aquanaut. He prefers the term “shared control.”

Whatever you want to call it, Aquanaut seems like something out of the script of a Transformers movie. The underwater robot begins each mission in a submarine-like shape, capable of autonomously traveling up to 200 kilometers on battery power, depending on the assignment.

When Aquanaut reaches its destination—oil and gas is the primary industry HMI hopes to disrupt to start—its four specially-designed and built linear actuators go to work. Aquanaut then unfolds into a robot with a head, upper torso, and two manipulator arms, all while maintaining proper buoyancy to get its job done.

The lightbulb moment of how to engineer this transformation from submarine to robot came one day while Aquanaut’s engineers were watching the office’s stand-up desks bob up and down. The answer to the engineering challenge of the hull suddenly seemed obvious.

“We’re just gonna build a big, gigantic, underwater stand-up desk,” Radford told Singularity Hub.

Hardware wasn’t the only problem the team, comprised of veteran NASA roboticists like Radford, had to solve. In order to ditch the expensive support vessels and large teams of humans required to operate traditional ROVs, Aquanaut would have to be able to sense its environment in great detail and relay that information back to headquarters using an underwater acoustics communications system that harkens back to the days of dial-up internet connections.

To tackle that problem of low bandwidth, HMI equipped Aquanaut with a machine vision system comprised of acoustic, optical, and laser-based sensors. All of that dense data is compressed using in-house designed technology and transmitted to a single human operator who controls Aquanaut with a few clicks of a mouse. In other words, no joystick required.

“I don’t know of anyone trying to do this level of autonomy as it relates to interacting with the environment,” Radford said.

HMI got $20 million earlier this year in Series B funding co-led by Transocean, one of the world’s largest offshore drilling contractors. That should be enough money to finish the Aquanaut prototype, which Radford said is about 99.8 percent complete. Some “high-profile” demonstrations are planned for early next year, with commercial deployments as early as 2020.

“What just gives us an incredible advantage here is that we have been born and bred on doing robotic systems for remote locations,” Radford noted. “This is my life, and I’ve bet the farm on it, and it takes this kind of fortitude and passion to see these things through, because these are not easy problems to solve.”

On Cruise Control
Meanwhile, a Boston-based startup is trying to solve the problem of making ships at sea autonomous. Sea Machines is backed by about $12.5 million in capital venture funding, with Toyota AI joining the list of investors in a $10 million Series A earlier this month.

Sea Machines is looking to the self-driving industry for inspiration, developing what it calls “vessel intelligence” systems that can be retrofitted on existing commercial vessels or installed on newly-built working ships.

For instance, the startup announced a deal earlier this year with Maersk, the world’s largest container shipping company, to deploy a system of artificial intelligence, computer vision, and LiDAR on the Danish company’s new ice-class container ship. The technology works similar to advanced driver-assistance systems found in automobiles to avoid hazards. The proof of concept will lay the foundation for a future autonomous collision avoidance system.

It’s not just startups making a splash in autonomous shipping. Radford noted that Rolls Royce—yes, that Rolls Royce—is leading the way in the development of autonomous ships. Its Intelligence Awareness system pulls in nearly every type of hyped technology on the market today: neural networks, augmented reality, virtual reality, and LiDAR.

In augmented reality mode, for example, a live feed video from the ship’s sensors can detect both static and moving objects, overlaying the scene with details about the types of vessels in the area, as well as their distance, heading, and other pertinent data.

While safety is a primary motivation for vessel automation—more than 1,100 ships have been lost over the past decade—these new technologies could make ships more efficient and less expensive to operate, according to a story in Wired about the Rolls Royce Intelligence Awareness system.

Sea Hunt Meets Science
As Singularity Hub noted in a previous article, ocean robots can also play a critical role in saving the seas from environmental threats. One poster child that has emerged—or, invaded—is the spindly lionfish.

A venomous critter endemic to the Indo-Pacific region, the lionfish is now found up and down the east coast of North America and beyond. And it is voracious, eating up to 30 times its own stomach volume and reducing juvenile reef fish populations by nearly 90 percent in as little as five weeks, according to the Ocean Support Foundation.

That has made the colorful but deadly fish Public Enemy No. 1 for many marine conservationists. Both researchers and startups are developing autonomous robots to hunt down the invasive predator.

At the Worcester Polytechnic Institute, for example, students are building a spear-carrying robot that uses machine learning and computer vision to distinguish lionfish from other aquatic species. The students trained the algorithms on thousands of different images of lionfish. The result: a lionfish-killing machine that boasts an accuracy of greater than 95 percent.

Meanwhile, a small startup called the American Marine Research Corporation out of Pensacola, Florida is applying similar technology to seek and destroy lionfish. Rather than spearfishing, the AMRC drone would stun and capture the lionfish, turning a profit by selling the creatures to local seafood restaurants.

Lionfish: It’s what’s for dinner.

Water Bots
A new wave of smart, independent robots are diving, swimming, and cruising across the ocean and its deepest depths. These autonomous systems aren’t necessarily designed to replace humans, but to venture where we can’t go or to improve safety at sea. And, perhaps, these latest innovations may inspire the robots that will someday plumb the depths of watery planets far from Earth.

Image Credit: Houston Mechatronics, Inc. Continue reading →

Posted in Human Robots

#434324 Big Brother Nation: The Case for ...

Posted on January 13, 2019 by Android

Powerful surveillance cameras have crept into public spaces. We are filmed and photographed hundreds of times a day. To further raise the stakes, the resulting video footage is fed to new forms of artificial intelligence software that can recognize faces in real time, read license plates, even instantly detect when a particular pre-defined action or activity takes place in front of a camera.

As most modern cities have quietly become surveillance cities, the law has been slow to catch up. While we wait for robust legal frameworks to emerge, the best way to protect our civil liberties right now is to fight technology with technology. All cities should place local surveillance video into a public cloud-based data trust. Here’s how it would work.

In Public Data We Trust
To democratize surveillance, every city should implement three simple rules. First, anyone who aims a camera at public space must upload that day’s haul of raw video file (and associated camera meta-data) into a cloud-based repository. Second, this cloud-based repository must have open APIs and a publicly-accessible log file that records search histories and tracks who has accessed which video files. And third, everyone in the city should be given the same level of access rights to the stored video data—no exceptions.

This kind of public data repository is called a “data trust.” Public data trusts are not just wishful thinking. Different types of trusts are already in successful use in Estonia and Barcelona, and have been proposed as the best way to store and manage the urban data that will be generated by Alphabet’s planned Sidewalk Labs project in Toronto.

It’s true that few people relish the thought of public video footage of themselves being looked at by strangers and friends, by ex-spouses, potential employers, divorce attorneys, and future romantic prospects. In fact, when I propose this notion when I give talks about smart cities, most people recoil in horror. Some turn red in the face and jeer at my naiveté. Others merely blink quietly in consternation.

The reason we should take this giant step towards extreme transparency is to combat the secrecy that surrounds surveillance. Openness is a powerful antidote to oppression. Edward Snowden summed it up well when he said, “Surveillance is not about public safety, it’s about power. It’s about control.”

Let Us Watch Those Watching Us
If public surveillance video were put back into the hands of the people, citizens could watch their government as it watches them. Right now, government cameras are controlled by the state. Camera locations are kept secret, and only the agencies that control the cameras get to see the footage they generate.

Because of these information asymmetries, civilians have no insight into the size and shape of the modern urban surveillance infrastructure that surrounds us, nor the uses (or abuses) of the video footage it spawns. For example, there is no swift and efficient mechanism to request a copy of video footage from the cameras that dot our downtown. Nor can we ask our city’s police force to show us a map that documents local traffic camera locations.

By exposing all public surveillance videos to the public gaze, cities could give regular people tools to assess the size, shape, and density of their local surveillance infrastructure and neighborhood “digital dragnet.” Using the meta-data that’s wrapped around video footage, citizens could geo-locate individual cameras onto a digital map to generate surveillance “heat maps.” This way people could assess whether their city’s camera density was higher in certain zip codes, or in neighborhoods populated by a dominant ethnic group.

Surveillance heat maps could be used to document which government agencies were refusing to upload their video files, or which neighborhoods were not under surveillance. Given what we already know today about the correlation between camera density, income, and social status, these “dark” camera-free regions would likely be those located near government agencies and in more affluent parts of a city.

Extreme transparency would democratize surveillance. Every city’s data trust would keep a publicly-accessible log of who’s searching for what, and whom. People could use their local data trust’s search history to check whether anyone was searching for their name, face, or license plate. As a result, clandestine spying on—and stalking of—particular individuals would become difficult to hide and simpler to prove.

Protect the Vulnerable and Exonerate the Falsely Accused
Not all surveillance video automatically works against the underdog. As the bungled (and consequently no longer secret) assassination of journalist Jamal Khashoggi demonstrated, one of the unexpected upsides of surveillance cameras has been the fact that even kings become accountable for their crimes. If opened up to the public, surveillance cameras could serve as witnesses to justice.

Video evidence has the power to protect vulnerable individuals and social groups by shedding light onto messy, unreliable (and frequently conflicting) human narratives of who did what to whom, and why. With access to a data trust, a person falsely accused of a crime could prove their innocence. By searching for their own face in video footage or downloading time/date stamped footage from a particular camera, a potential suspect could document their physical absence from the scene of a crime—no lengthy police investigation or high-priced attorney needed.

Given Enough Eyeballs, All Crimes Are Shallow
Placing public surveillance video into a public trust could make cities safer and would streamline routine police work. Linus Torvalds, the developer of open-source operating system Linux, famously observed that “given enough eyeballs, all bugs are shallow.” In the case of public cameras and a common data repository, Torvald’s Law could be restated as “given enough eyeballs, all crimes are shallow.”

If thousands of citizen eyeballs were given access to a city’s public surveillance videos, local police forces could crowdsource the work of solving crimes and searching for missing persons. Unfortunately, at the present time, cities are unable to wring any social benefit from video footage of public spaces. The most formidable barrier is not government-imposed secrecy, but the fact that as cameras and computers have grown cheaper, a large and fast-growing “mom and pop” surveillance state has taken over most of the filming of public spaces.

While we fear spooky government surveillance, the reality is that we’re much more likely to be filmed by security cameras owned by shopkeepers, landlords, medical offices, hotels, homeowners, and schools. These businesses, organizations, and individuals install cameras in public areas for practical reasons—to reduce their insurance costs, to prevent lawsuits, or to combat shoplifting. In the absence of regulations governing their use, private camera owners store video footage in a wide variety of locations, for varying retention periods.

The unfortunate (and unintended) result of this informal and decentralized network of public surveillance is that video files are not easy to access, even for police officers on official business. After a crime or terrorist attack occurs, local police (or attorneys armed with a subpoena) go from door to door to manually collect video evidence. Once they have the videos in hand, their next challenge is searching for the right “codex” to crack the dozens of different file formats they encounter so they can watch and analyze the footage.

The result of these practical barriers is that as it stands today, only people with considerable legal or political clout are able to successfully gain access into a city’s privately-owned, ad-hoc collections of public surveillance videos. Not only are cities missing the opportunity to streamline routine evidence-gathering police work, they’re missing a radically transformative benefit that would become possible once video footage from thousands of different security cameras were pooled into a single repository: the ability to apply the power of citizen eyeballs to the work of improving public safety.

Why We Need Extreme Transparency
When regular people can’t access their own surveillance videos, there can be no data justice. While we wait for the law to catch up with the reality of modern urban life, citizens and city governments should use technology to address the problem that lies at the heart of surveillance: a power imbalance between those who control the cameras and those who don’t.

Cities should permit individuals and organizations to install and deploy as many public-facing cameras as they wish, but with the mandate that camera owners must place all resulting video footage into the mercilessly bright sunshine of an open data trust. This way, cloud computing, open APIs, and artificial intelligence software can help combat abuses of surveillance and give citizens insight into who’s filming us, where, and why.

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Posted in Human Robots

#434311 Understanding the Hidden Bias in ...

Posted on January 11, 2019 by Android

Facial recognition technology has progressed to point where it now interprets emotions in facial expressions. This type of analysis is increasingly used in daily life. For example, companies can use facial recognition software to help with hiring decisions. Other programs scan the faces in crowds to identify threats to public safety.

Unfortunately, this technology struggles to interpret the emotions of black faces. My new study, published last month, shows that emotional analysis technology assigns more negative emotions to black men’s faces than white men’s faces.

This isn’t the first time that facial recognition programs have been shown to be biased. Google labeled black faces as gorillas. Cameras identified Asian faces as blinking. Facial recognition programs struggled to correctly identify gender for people with darker skin.

My work contributes to a growing call to better understand the hidden bias in artificial intelligence software.

Measuring Bias
To examine the bias in the facial recognition systems that analyze people’s emotions, I used a data set of 400 NBA player photos from the 2016 to 2017 season, because players are similar in their clothing, athleticism, age and gender. Also, since these are professional portraits, the players look at the camera in the picture.

I ran the images through two well-known types of emotional recognition software. Both assigned black players more negative emotional scores on average, no matter how much they smiled.

For example, consider the official NBA pictures of Darren Collison and Gordon Hayward. Both players are smiling, and, according to the facial recognition and analysis program Face++, Darren Collison and Gordon Hayward have similar smile scores—48.7 and 48.1 out of 100, respectively.

Basketball players Darren Collision (left) and Gordon Hayward (right). basketball-reference.com

However, Face++ rates Hayward’s expression as 59.7 percent happy and 0.13 percent angry and Collison’s expression as 39.2 percent happy and 27 percent angry. Collison is viewed as nearly as angry as he is happy and far angrier than Hayward—despite the facial recognition program itself recognizing that both players are smiling.

In contrast, Microsoft’s Face API viewed both men as happy. Still, Collison is viewed as less happy than Hayward, with 98 and 93 percent happiness scores, respectively. Despite his smile, Collison is even scored with a small amount of contempt, whereas Hayward has none.

Across all the NBA pictures, the same pattern emerges. On average, Face++ rates black faces as twice as angry as white faces. Face API scores black faces as three times more contemptuous than white faces. After matching players based on their smiles, both facial analysis programs are still more likely to assign the negative emotions of anger or contempt to black faces.

Stereotyped by AI
My study shows that facial recognition programs exhibit two distinct types of bias.

First, black faces were consistently scored as angrier than white faces for every smile. Face++ showed this type of bias. Second, black faces were always scored as angrier if there was any ambiguity about their facial expression. Face API displayed this type of disparity. Even if black faces are partially smiling, my analysis showed that the systems assumed more negative emotions as compared to their white counterparts with similar expressions. The average emotional scores were much closer across races, but there were still noticeable differences for black and white faces.

This observation aligns with other research, which suggests that black professionals must amplify positive emotions to receive parity in their workplace performance evaluations. Studies show that people perceive black men as more physically threatening than white men, even when they are the same size.

Some researchers argue that facial recognition technology is more objective than humans. But my study suggests that facial recognition reflects the same biases that people have. Black men’s facial expressions are scored with emotions associated with threatening behaviors more often than white men, even when they are smiling. There is good reason to believe that the use of facial recognition could formalize preexisting stereotypes into algorithms, automatically embedding them into everyday life.

Until facial recognition assesses black and white faces similarly, black people may need to exaggerate their positive facial expressions—essentially smile more—to reduce ambiguity and potentially negative interpretations by the technology.

Although innovative, artificial intelligence can perpetrate and exacerbate existing power dynamics, leading to disparate impact across racial/ethnic groups. Some societal accountability is necessary to ensure fairness to all groups because facial recognition, like most artificial intelligence, is often invisible to the people most affected by its decisions.

Lauren Rhue, Assistant Professor of Information Systems and Analytics, Wake Forest University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Posted in Human Robots