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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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#434585 This Week’s Awesome Stories From ...

ARTIFICIAL INTELLIGENCE
The World’s Fastest Supercomputer Breaks an AI Record
Tom Simonite | Wired
“Summit, which occupies an area equivalent to two tennis courts, used more than 27,000 powerful graphics processors in the project. It tapped their power to train deep-learning algorithms, the technology driving AI’s frontier, chewing through the exercise at a rate of a billion billion operations per second, a pace known in supercomputing circles as an exaflop.”

ROBOTICS
iRobot Finally Announces Awesome New Terra Robotic Lawnmower
Evan Ackerman | IEEE Spectrum
“Since the first Roomba came out in 2002, it has seemed inevitable that one day iRobot would develop a robotic lawn mower. After all, a robot mower is basically just a Roomba that works outside, right? Of course, it’s not nearly that simple, as iRobot has spent the last decade or so discovering, but they’ve finally managed to pull it off.”

3D Printing
Watch This Super Speedy 3D Printer Make Objects Suddenly Appear
Erin Winick | MIT Technology Review
“The new machine—which the team nicknamed the ‘replicator’ after the machine from Star Trek—instead forms the entire item all in one go. It does this by shining light onto specific spots in a rotating resin that solidifies when exposed to a certain light level.”

GENETICS
The DIY Designer Baby Project Funded With Bitcoin
Antonio Regalado | MIT Technology Review
“i‘Is DIY bio anywhere close to making a CRISPR baby? No, not remotely,’ David Ishee says. ‘But if some rich guy pays a scientist to do the work, it’s going to happen.’ He adds: ‘What you are reporting on isn’t Bryan—it’s the unseen middle space, a layer of gray-market biotech and freelance science where people with resources can get things done.’i”

SCIENCE
The Complete Cancer Cure Story Is Both Bogus and Tragic
Megan Molteni | Wired
“You’d think creators and consumers of news would have learned their lesson by now. But the latest version of the fake cancer cure story is even more flagrantly flawed than usual. The public’s cancer cure–shaped amnesia, and media outlets’ willingness to exploit it for clicks, are as bottomless as ever. Hope, it would seem, trumps history.”

BOOKS
An AI Reading List—From Practical Primers to Sci-Fi Short Stories
James Vincent | The Verge
“The Verge has assembled a reading list: a brief but diverse compendium of books, short stories, and blogs, all chosen by leading figures in the AI world to help you better understand artificial intelligence.”

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Posted in Human Robots

#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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#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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#434297 How Can Leaders Ensure Humanity in a ...

It’s hard to avoid the prominence of AI in our lives, and there is a plethora of predictions about how it will influence our future. In their new book Solomon’s Code: Humanity in a World of Thinking Machines, co-authors Olaf Groth, Professor of Strategy, Innovation and Economics at HULT International Business School and CEO of advisory network Cambrian.ai, and Mark Nitzberg, Executive Director of UC Berkeley’s Center for Human-Compatible AI, believe that the shift in balance of power between intelligent machines and humans is already here.

I caught up with the authors about how the continued integration between technology and humans, and their call for a “Digital Magna Carta,” a broadly-accepted charter developed by a multi-stakeholder congress that would help guide the development of advanced technologies to harness their power for the benefit of all humanity.

Lisa Kay Solomon: Your new book, Solomon’s Code, explores artificial intelligence and its broader human, ethical, and societal implications that all leaders need to consider. AI is a technology that’s been in development for decades. Why is it so urgent to focus on these topics now?

Olaf Groth and Mark Nitzberg: Popular perception always thinks of AI in terms of game-changing narratives—for instance, Deep Blue beating Gary Kasparov at chess. But it’s the way these AI applications are “getting into our heads” and making decisions for us that really influences our lives. That’s not to say the big, headline-grabbing breakthroughs aren’t important; they are.

But it’s the proliferation of prosaic apps and bots that changes our lives the most, by either empowering or counteracting who we are and what we do. Today, we turn a rapidly growing number of our decisions over to these machines, often without knowing it—and even more often without understanding the second- and third-order effects of both the technologies and our decisions to rely on them.

There is genuine power in what we call a “symbio-intelligent” partnership between human, machine, and natural intelligences. These relationships can optimize not just economic interests, but help improve human well-being, create a more purposeful workplace, and bring more fulfillment to our lives.

However, mitigating the risks while taking advantage of the opportunities will require a serious, multidisciplinary consideration of how AI influences human values, trust, and power relationships. Whether or not we acknowledge their existence in our everyday life, these questions are no longer just thought exercises or fodder for science fiction.

In many ways, these technologies can challenge what it means to be human, and their ramifications already affect us in real and often subtle ways. We need to understand how

LKS: There is a lot of hype and misconceptions about AI. In your book, you provide a useful distinction between the cognitive capability that we often associate with AI processes, and the more human elements of consciousness and conscience. Why are these distinctions so important to understand?

OG & MN: Could machines take over consciousness some day as they become more powerful and complex? It’s hard to say. But there’s little doubt that, as machines become more capable, humans will start to think of them as something conscious—if for no other reason than our natural inclination to anthropomorphize.

Machines are already learning to recognize our emotional states and our physical health. Once they start talking that back to us and adjusting their behavior accordingly, we will be tempted to develop a certain rapport with them, potentially more trusting or more intimate because the machine recognizes us in our various states.

Consciousness is hard to define and may well be an emergent property, rather than something you can easily create or—in turn—deduce to its parts. So, could it happen as we put more and more elements together, from the realms of AI, quantum computing, or brain-computer interfaces? We can’t exclude that possibility.

Either way, we need to make sure we’re charting out a clear path and guardrails for this development through the Three Cs in machines: cognition (where AI is today); consciousness (where AI could go); and conscience (what we need to instill in AI before we get there). The real concern is that we reach machine consciousness—or what humans decide to grant as consciousness—without a conscience. If that happens, we will have created an artificial sociopath.

LKS: We have been seeing major developments in how AI is influencing product development and industry shifts. How is the rise of AI changing power at the global level?

OG & MN: Both in the public and private sectors, the data holder has the power. We’ve already seen the ascendance of about 10 “digital barons” in the US and China who sit on huge troves of data, massive computing power, and the resources and money to attract the world’s top AI talent. With these gaps already open between the haves and the have-nots on the technological and corporate side, we’re becoming increasingly aware that similar inequalities are forming at a societal level as well.

Economic power flows with data, leaving few options for socio-economically underprivileged populations and their corrupt, biased, or sparse digital footprints. By concentrating power and overlooking values, we fracture trust.

We can already see this tension emerging between the two dominant geopolitical models of AI. China and the US have emerged as the most powerful in both technological and economic terms, and both remain eager to drive that influence around the world. The EU countries are more contained on these economic and geopolitical measures, but they’ve leaped ahead on privacy and social concerns.

The problem is, no one has yet combined leadership on all three critical elements of values, trust, and power. The nations and organizations that foster all three of these elements in their AI systems and strategies will lead the future. Some are starting to recognize the need for the combination, but we found just 13 countries that have created significant AI strategies. Countries that wait too long to join them risk subjecting themselves to a new “data colonialism” that could change their economies and societies from the outside.

LKS: Solomon’s Code looks at AI from a variety of perspectives, considering both positive and potentially dangerous effects. You caution against the rising global threat and weaponization of AI and data, suggesting that “biased or dirty data is more threatening than nuclear arms or a pandemic.” For global leaders, entrepreneurs, technologists, policy makers and social change agents reading this, what specific strategies do you recommend to ensure ethical development and application of AI?

OG & MN: We’ve surrendered many of our most critical decisions to the Cult of Data. In most cases, that’s a great thing, as we rely more on scientific evidence to understand our world and our way through it. But we swing too far in other instances, assuming that datasets and algorithms produce a complete story that’s unsullied by human biases or intellectual shortcomings. We might choose to ignore it, but no one is blind to the dangers of nuclear war or pandemic disease. Yet, we willfully blind ourselves to the threat of dirty data, instead believing it to be pristine.

So, what do we do about it? On an individual level, it’s a matter of awareness, knowing who controls your data and how outsourcing of decisions to thinking machines can present opportunities and threats alike.

For business, government, and political leaders, we need to see a much broader expansion of ethics committees with transparent criteria with which to evaluate new products and services. We might consider something akin to clinical trials for pharmaceuticals—a sort of testing scheme that can transparently and independently measure the effects on humans of algorithms, bots, and the like. All of this needs to be multidisciplinary, bringing in expertise from across technology, social systems, ethics, anthropology, psychology, and so on.

Finally, on a global level, we need a new charter of rights—a Digital Magna Carta—that formalizes these protections and guides the development of new AI technologies toward all of humanity’s benefit. We’ve suggested the creation of a multi-stakeholder Cambrian Congress (harkening back to the explosion of life during the Cambrian period) that can not only begin to frame benefits for humanity, but build the global consensus around principles for a basic code-of-conduct, and ideas for evaluation and enforcement mechanisms, so we can get there without any large-scale failures or backlash in society. So, it’s not one or the other—it’s both.

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