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#434637 AI Is Rapidly Augmenting Healthcare and ...

When it comes to the future of healthcare, perhaps the only technology more powerful than CRISPR is artificial intelligence.

Over the past five years, healthcare AI startups around the globe raised over $4.3 billion across 576 deals, topping all other industries in AI deal activity.

During this same period, the FDA has given 70 AI healthcare tools and devices ‘fast-tracked approval’ because of their ability to save both lives and money.

The pace of AI-augmented healthcare innovation is only accelerating.

In Part 3 of this blog series on longevity and vitality, I cover the different ways in which AI is augmenting our healthcare system, enabling us to live longer and healthier lives.

In this blog, I’ll expand on:

Machine learning and drug design
Artificial intelligence and big data in medicine
Healthcare, AI & China

Let’s dive in.

Machine Learning in Drug Design
What if AI systems, specifically neural networks, could predict the design of novel molecules (i.e. medicines) capable of targeting and curing any disease?

Imagine leveraging cutting-edge artificial intelligence to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.

And what if these molecules, accurately engineered by AIs, always worked? Such a feat would revolutionize our $1.3 trillion global pharmaceutical industry, which currently holds a dismal record of 1 in 10 target drugs ever reaching human trials.

It’s no wonder that drug development is massively expensive and slow. It takes over 10 years to bring a new drug to market, with costs ranging from $2.5 billion to $12 billion.

This inefficient, slow-to-innovate, and risk-averse industry is a sitting duck for disruption in the years ahead.

One of the hottest startups in digital drug discovery today is Insilico Medicine. Leveraging AI in its end-to-end drug discovery pipeline, Insilico Medicine aims to extend healthy longevity through drug discovery and aging research.

Their comprehensive drug discovery engine uses millions of samples and multiple data types to discover signatures of disease, identify the most promising protein targets, and generate perfect molecules for these targets. These molecules either already exist or can be generated de novo with the desired set of parameters.

In late 2018, Insilico’s CEO Dr. Alex Zhavoronkov announced the groundbreaking result of generating novel molecules for a challenging protein target with an unprecedented hit rate in under 46 days. This included both synthesis of the molecules and experimental validation in a biological test system—an impressive feat made possible by converging exponential technologies.

Underpinning Insilico’s drug discovery pipeline is a novel machine learning technique called Generative Adversarial Networks (GANs), used in combination with deep reinforcement learning.

Generating novel molecular structures for diseases both with and without known targets, Insilico is now pursuing drug discovery in aging, cancer, fibrosis, Parkinson’s disease, Alzheimer’s disease, ALS, diabetes, and many others. Once rolled out, the implications will be profound.

Dr. Zhavoronkov’s ultimate goal is to develop a fully-automated Health-as-a-Service (HaaS) and Longevity-as-a-Service (LaaS) engine.

Once plugged into the services of companies from Alibaba to Alphabet, such an engine would enable personalized solutions for online users, helping them prevent diseases and maintain optimal health.

Insilico, alongside other companies tackling AI-powered drug discovery, truly represents the application of the 6 D’s. What was once a prohibitively expensive and human-intensive process is now rapidly becoming digitized, dematerialized, demonetized and, perhaps most importantly, democratized.

Companies like Insilico can now do with a fraction of the cost and personnel what the pharmaceutical industry can barely accomplish with thousands of employees and a hefty bill to foot.

As I discussed in my blog on ‘The Next Hundred-Billion-Dollar Opportunity,’ Google’s DeepMind has now turned its neural networks to healthcare, entering the digitized drug discovery arena.

In 2017, DeepMind achieved a phenomenal feat by matching the fidelity of medical experts in correctly diagnosing over 50 eye disorders.

And just a year later, DeepMind announced a new deep learning tool called AlphaFold. By predicting the elusive ways in which various proteins fold on the basis of their amino acid sequences, AlphaFold may soon have a tremendous impact in aiding drug discovery and fighting some of today’s most intractable diseases.

Artificial Intelligence and Data Crunching
AI is especially powerful in analyzing massive quantities of data to uncover patterns and insights that can save lives. Take WAVE, for instance. Every year, over 400,000 patients die prematurely in US hospitals as a result of heart attack or respiratory failure.

Yet these patients don’t die without leaving plenty of clues. Given information overload, however, human physicians and nurses alone have no way of processing and analyzing all necessary data in time to save these patients’ lives.

Enter WAVE, an algorithm that can process enough data to offer a six-hour early warning of patient deterioration.

Just last year, the FDA approved WAVE as an AI-based predictive patient surveillance system to predict and thereby prevent sudden death.

Another highly valuable yet difficult-to-parse mountain of medical data comprises the 2.5 million medical papers published each year.

For some time, it has become physically impossible for a human physician to read—let alone remember—all of the relevant published data.

To counter this compounding conundrum, Johnson & Johnson is teaching IBM Watson to read and understand scientific papers that detail clinical trial outcomes.

Enriching Watson’s data sources, Apple is also partnering with IBM to provide access to health data from mobile apps.

One such Watson system contains 40 million documents, ingesting an average of 27,000 new documents per day, and providing insights for thousands of users.

After only one year, Watson’s successful diagnosis rate of lung cancer has reached 90 percent, compared to the 50 percent success rate of human doctors.

But what about the vast amount of unstructured medical patient data that populates today’s ancient medical system? This includes medical notes, prescriptions, audio interview transcripts, and pathology and radiology reports.

In late 2018, Amazon announced a new HIPAA-eligible machine learning service that digests and parses unstructured data into categories, such as patient diagnoses, treatments, dosages, symptoms and signs.

Taha Kass-Hout, Amazon’s senior leader in health care and artificial intelligence, told the Wall Street Journal that internal tests demonstrated that the software even performs as well as or better than other published efforts.

On the heels of this announcement, Amazon confirmed it was teaming up with the Fred Hutchinson Cancer Research Center to evaluate “millions of clinical notes to extract and index medical conditions.”

Having already driven extraordinary algorithmic success rates in other fields, data is the healthcare industry’s goldmine for future innovation.

Healthcare, AI & China
In 2017, the Chinese government published its ambitious national plan to become a global leader in AI research by 2030, with healthcare listed as one of four core research areas during the first wave of the plan.

Just a year earlier, China began centralizing healthcare data, tackling a major roadblock to developing longevity and healthcare technologies (particularly AI systems): scattered, dispersed, and unlabeled patient data.

Backed by the Chinese government, China’s largest tech companies—particularly Tencent—have now made strong entrances into healthcare.

Just recently, Tencent participated in a $154 million megaround for China-based healthcare AI unicorn iCarbonX.

Hoping to develop a complete digital representation of your biological self, iCarbonX has acquired numerous US personalized medicine startups.

Considering Tencent’s own Miying healthcare AI platform—aimed at assisting healthcare institutions in AI-driven cancer diagnostics—Tencent is quickly expanding into the drug discovery space, participating in two multimillion-dollar, US-based AI drug discovery deals just this year.

China’s biggest, second-order move into the healthtech space comes through Tencent’s WeChat. In the course of a mere few years, already 60 percent of the 38,000 medical institutions registered on WeChat allow patients to digitally book appointments through Tencent’s mobile platform. At the same time, 2,000 Chinese hospitals accept WeChat payments.

Tencent has additionally partnered with the U.K.’s Babylon Health, a virtual healthcare assistant startup whose app now allows Chinese WeChat users to message their symptoms and receive immediate medical feedback.

Similarly, Alibaba’s healthtech focus started in 2016 when it released its cloud-based AI medical platform, ET Medical Brain, to augment healthcare processes through everything from diagnostics to intelligent scheduling.

Conclusion
As Nvidia CEO Jensen Huang has stated, “Software ate the world, but AI is going to eat software.” Extrapolating this statement to a more immediate implication, AI will first eat healthcare, resulting in dramatic acceleration of longevity research and an amplification of the human healthspan.

Next week, I’ll continue to explore this concept of AI systems in healthcare.

Particularly, I’ll expand on how we’re acquiring and using the data for these doctor-augmenting AI systems: from ubiquitous biosensors, to the mobile healthcare revolution, and finally, to the transformative power of the health nucleus.

As AI and other exponential technologies increase our healthspan by 30 to 40 years, how will you leverage these same exponential technologies to take on your moonshots and live out your massively transformative purpose?

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#434532 How Microrobots Will Fix Our Roads and ...

Swarms of microrobots will scuttle along beneath our roads and pavements, finding and fixing leaky pipes and faulty cables. Thanks to their efforts, we can avoid costly road work that costs billions of dollars each year—not to mention frustrating traffic delays.

That is, if a new project sponsored by the U.K. government is a success. Recent developments in the space seem to point towards a bright future for microrobots.

Microrobots Saving Billions
Each year, around 1.5 million road excavations take place across the U.K. Many are due to leaky pipes and faulty cables that necessitate excavation of road surfaces in order to fix them. The resulting repairs, alongside disruptions to traffic and businesses, are estimated to cost a whopping £6.3 billion ($8 billion).

A consortium of scientists, led by University of Sheffield Professor Kirill Horoshenkov, are planning to use microrobots to negate most of these costs. The group has received a £7.2 million ($9.2 million) grant to develop and build their bots.

According to Horoshenkov, the microrobots will come in two versions. One is an inspection bot, which will navigate along underground infrastructure and examine its condition via sonar. The inspectors will be complemented by worker bots capable of carrying out repairs with cement and adhesives or cleaning out blockages with a high-powered jet. The inspector bots will be around one centimeter long and possibly autonomous, while the worker bots will be slightly larger and steered via remote control.

If successful, it is believed the bots could potentially save the U.K. economy around £5 billion ($6.4 billion) a year.

The U.K. government has set aside a further £19 million ($24 million) for research into robots for hazardous environments, such as nuclear decommissioning, drones for oil pipeline monitoring, and artificial intelligence software to detect the need for repairs on satellites in orbit.

The Lowest-Hanging Fruit
Microrobots like the ones now under development in the U.K. have many potential advantages and use cases. Thanks to their small size they can navigate tight spaces, for example in search and rescue operations, and robot swarm technology would allow them to collaborate to perform many different functions, including in construction projects.

To date, the number of microrobots in use is relatively limited, but that could be about to change, with bots closing in on other types of inspection jobs, which could be considered one of the lowest-hanging fruits.

Engineering firm Rolls-Royce (not the car company, but the one that builds aircraft engines) is looking to use microrobots to inspect some of the up to 25,000 individual parts that make up an engine. The microrobots use the cockroach as a model, and Rolls Royce believes they could save engineers time when performing the maintenance checks that can take over a month per engine.

Even Smaller Successes
Going further down in scale, recent years have seen a string of successes for nanobots. For example, a team of researchers at the Femto-ST Institute have used nanobots to build what is likely the world’s smallest house (if this isn’t a category at Guinness, someone needs to get on the phone with them), which stands a ‘towering’ 0.015 millimeters.

One of the areas where nanobots have shown great promise is in medicine. Several studies have shown how the minute bots are capable of delivering drugs directly into dense biological tissue, which can otherwise be highly challenging to target directly. Such delivery systems have a great potential for improving the treatment of a wide range of ailments and illnesses, including cancer.

There’s no question that the ecosystem of microrobots and nanobots is evolving. While still in their early days, the above successes point to a near-future boom in the bots we may soon refer to as our ‘littlest everyday helpers.’

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

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.

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

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 ...

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