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Could A.I. detect mass shooters before they strike?
President Trump has called for Silicon Valley to develop digital precogs, but such systems raise efficacy concerns.
- President Donald Trump wants social media companies to develop A.I. that can flag potential mass shooters.
- Experts agree that artificial intelligence is not advanced enough, nor are current moderating systems up to the task.
- A majority of Americans support stricter gun laws, but such policies have yet to make headway.
On August 3, a man in El Paso, Texas, shot and killed 22 people and injured 24 others. Hours later, another man in Dayton, Ohio, shot and killed nine people, including his own sister. Even in a country left numb by countless mass shootings, the news was distressing and painful.
President Donald Trump soon addressed the nation to outline how his administration planned to tackle this uniquely American problem. Listeners hoping the tragedies might finally spur motivation for stricter gun control laws, such as universal background checks or restrictions on high-capacity magazines, were left disappointed.
Trump's plan was a ragbag of typical Republican talking points: red flag laws, mental health concerns, and regulation on violent video games. Tucked among them was an idea straight out of a Philip K. Dick novel.
"We must recognize that the internet has provided a dangerous avenue to radicalize disturbed minds and perform demented acts," Trump said. "First, we must do a better job of identifying and acting on early warning signs. I am directing the Department of Justice to work in partnership with local, state and federal agencies as well as well as social media companies to develop tools that can detect mass shooters before they strike."
Basically, Trump wants digital precogs. But has artificial intelligence reached such grand, and potentially terrifying, heights?
A digitized state of mind
It's worth noting that A.I. has made impressive strides at reading and quantifying the human mind. Social media is a vast repository of data on how people feel and think. If we can suss out the internal from the performative, we could improve mental health care in the U.S. and abroad.
For example, a study from 2017 found that A.I. could read the predictive markers for depression in Instagram photos. Researchers tasked machine learning tools with analyzing data from 166 individuals, some of whom had been previously diagnosed with depression. The algorithms looked at filter choice, facial expressions, metadata tags, etc., in more than 43,950 photos.
The results? The A.I. outperformed human practitioners at diagnosing depression. These results held even when analyzing images from before the patients' diagnoses. (Of course, Instagram is also the social media platform most likely to make you depressed and anxious, but that's another study.)
Talking with Big Think, Eric Topol, a professor in the Department of Molecular Medicine at Scripps, called this the ability to "digitize our state of mind." In addition to the Instagram study, he pointed out that patients will share more with a self-chosen avatar than a human psychiatrist.
"So when you take this ability to digitize a state of mind and also have a support through an avatar, this could turn out to be a really great way to deal with the problem we have today, which is a lack of mental health professionals with a very extensive burden of depression and other mental health conditions," Topol said.
Detecting mass shooters?
....mentally ill or deranged people. I am the biggest Second Amendment person there is, but we all must work togeth… https://t.co/T9OthUAsXe— Donald J. Trump (@Donald J. Trump)1565352202.0
However, it's not as simple as turning the A.I. dial from "depression" to "mass shooter." Machine learning tools have gotten excellent at analyzing images, but they lag behind the mind's ability to read language, intonation, and social cues.
As Facebook CEO Mark Zuckerberg said: "One of the pieces of criticism we get that I think is fair is that we're much better able to enforce our nudity policies, for example, than we are hate speech. The reason for that is it's much easier to make an A.I. system that can detect a nipple than it is to determine what is linguistically hate speech."
Trump should know this. During a House Homeland Security subcommittee hearing earlier this year, experts testified that A.I. was not a panacea for curing online extremism. Alex Stamos, Facebook's former chief security officer, likened the world's best A.I. to "a crowd of millions of preschoolers" and the task to demanding those preschoolers "get together to build the Taj Mahal."
None of this is to say that the problem is impossible, but it's certainly intractable.
Yes, we can create an A.I. that plays Go or analyzes stock performance better than any human. That's because we have a lot of data on these activities and they follow predictable input-output patterns. Yet even these "simple" algorithms require some of the brightest minds to develop.
Mass shooters, though far too common in the United States, are still rare. We've played more games of Go, analyzed more stocks, and diagnosed more people with depression, which millions of Americans struggle with. This gives machine learning software more data points on these activities in order to create accurate, responsible predictions — that still don't perform flawlessly.
Add to this that hate, extremism, and violence don't follow reliable input-output patterns, and you can see why experts are leery of Trump's direction to employ A.I. in the battle against terrorism.
"As we psychological scientists have said repeatedly, the overwhelming majority of people with mental illness are not violent. And there is no single personality profile that can reliably predict who will resort to gun violence," Arthur C. Evans, CEO of the American Psychological Association, said in a release. "Based on the research, we know only that a history of violence is the single best predictor of who will commit future violence. And access to more guns, and deadlier guns, means more lives lost."
Social media can't protect us from ourselves
First Lady Melania Trump visits with the victims of the El Paso, Texas, shooting. Image source: Andrea Hanks / Flickr
One may wonder if we can utilize current capabilities more aggressively? Unfortunately, social media moderating systems are a hodgepodge, built piecemeal over the last decade. They rely on a mixture of A.I., paid moderators, and community policing. The outcome is an inconsistent system.
For example, the New York Times reported in 2017 that YouTube had removed thousands of videos using machine learning systems. The videos showed atrocities from the Syrian War, such as executions and people spouting Islamic State propaganda. The algorithm flagged and removed them as coming from extremist groups.
In truth, the videos came from humanitarian organizations to document human rights violations. The machine couldn't tell the difference. YouTube reinstated some of the videos after users reported the issue, but mistakes at such a scale do not give one hope that today's moderating systems could accurately identify would-be mass shooters.
That's the conclusion reached in a report from the Partnership on A.I. (PAI). It argued there were "serious shortcomings" in using A.I. as a risk-assessment tool in U.S. criminal justice. Its writers cite three overarching concerns: accuracy and bias; questions of transparency and accountability; and issues with the interface between tools and people.
"Although the use of these tools is in part motivated by the desire to mitigate existing human fallibility in the criminal justice system, it is a serious misunderstanding to view tools as objective or neutral simply because they are based on data," the report states. "While formulas and statistical models provide some degree of consistency and replicability, they still share or amplify many weaknesses of human decision-making."
In addition to the above, there are practical barriers. The technical capabilities of law enforcement vary between locations. Social media platforms deal in massive amounts of traffic and data. And even when the red flags are self-evident — such as when shooters publish manifestos — they offer a narrow window in which to act.
The tools to reduce mass shootings
Protesters at March for Our Lives 2018 in San Francisco. Image source: Gregory Varnum / Wikimedia Commons
Artificial intelligence offers many advantages today and will offer more in the future. But as an answer to extremism and mass shootings, experts agree it's simply the wrong tool. That's the bad news. The good news is we have the tools we need already, and they can be implemented with readily available tech.
"Based on the psychological science, we know some of the steps we need to take. We need to limit civilians' access to assault weapons and high-capacity magazines. We need to institute universal background checks. And we should institute red flag laws that remove guns from people who are at high risk of committing violent acts," Evans wrote.
We don't need advanced A.I. to figure this out. There's only one developed country in the world where someone can legally and easily acquire an armory of guns, and it's the only developed country that suffers mass shootings with such regularity. It's a simple arithmetic.
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The COVID-19 pandemic is making health disparities in the United States crystal clear. It is a clarion call for health care systems to double their efforts in vulnerable communities.
- The COVID-19 pandemic has exacerbated America's health disparities, widening the divide between the haves and have nots.
- Studies show disparities in wealth, race, and online access have disproportionately harmed underserved U.S. communities during the pandemic.
- To begin curing this social aliment, health systems like Northwell Health are establishing relationships of trust in these communities so that the post-COVID world looks different than the pre-COVID one.
COVID-19 deepens U.S. health disparities<p>Communities on the pernicious side of America's health disparities have their unique histories, environments, and social structures. They are spread across the United States, but they all have one thing in common.</p><p>"There is one common divide in American communities, and that is poverty," said <a href="https://www.northwell.edu/about/leadership/debbie-salas-lopez" target="_blank">Debbie Salas-Lopez, MD, MPH</a>, senior vice president of community and population health at Northwell Health. "That is the undercurrent that manifests poor health, poor health outcomes, or poor health prognoses for future wellbeing."</p><p>Social determinants have far-reaching effects on health, and poor communities have unfavorable social determinants. To pick one of many examples, <a href="https://www.npr.org/2020/09/27/913612554/a-crisis-within-a-crisis-food-insecurity-and-covid-19" target="_blank" rel="noopener noreferrer">food insecurity</a> reduces access to quality food, leading to poor health and communal endemics of chronic medical conditions. The U.S. Centers for Disease Control and Prevention has identified some of these conditions, such as obesity and Type 2 diabetes, as increasing the risk of developing a severe case of coronavirus.</p><p>The pandemic didn't create poverty or food insecurity, but it exacerbated both, and the results have been catastrophic. A study published this summer in the <em><a href="https://link.springer.com/article/10.1007/s11606-020-05971-3" target="_blank">Journal of General Internal Medicine</a></em> suggested that "social factors such as income inequality may explain why some parts of the USA are hit harder by the COVID-19 pandemic than others."</p><p>That's not to say better-off families in the U.S. weren't harmed. A <a href="https://voxeu.org/article/poverty-inequality-and-covid-19-us" target="_blank" rel="noopener noreferrer">paper from the Centre for Economic Policy Research</a> noted that families in counties with a higher median income experienced adjustment costs associated with the pandemic—for example, lowering income-earning interactions to align with social distancing policies. However, the paper found that the costs of social distancing were much greater for poorer families, who cannot easily alter their living circumstances, which often include more individuals living in one home and a reliance on mass transit to reach work and grocery stores. They are also disproportionately represented in essential jobs, such as retail, transportation, and health care, where maintaining physical distance can be all but impossible.</p><p>The paper also cited a positive correlation between higher income inequality and higher rates of coronavirus infection. "Our interpretation is that poorer people are less able to protect themselves, which leads them to different choices—they face a steeper trade-off between their health and their economic welfare in the context of the threats posed by COVID-19," the authors wrote.</p><p>"There are so many pandemics that this pandemic has exacerbated," Dr. Salas-Lopez noted.</p><p>One example is the health-wealth gap. The mental stressors of maintaining a low socioeconomic status, especially in the face of extreme affluence, can have a physically degrading impact on health. <a href="https://www.scientificamerican.com/index.cfm/_api/render/file/?method=inline&fileID=123ECD96-EF81-46F6-983D2AE9A45FA354" target="_blank" rel="noopener noreferrer">Writing on this gap</a>, Robert Sapolsky, professor of biology and neurology at Stanford University, notes that socioeconomic stressors can increase blood pressure, reduce insulin response, increase chronic inflammation, and impair the prefrontal cortex and other brain functions through anxiety, depression, and cognitive load. </p><p>"Thus, from the macro level of entire body systems to the micro level of individual chromosomes, poverty finds a way to produce wear and tear," Sapolsky writes. "It is outrageous that if children are born into the wrong family, they will be predisposed toward poor health by the time they start to learn the alphabet."</p>Research on the economic and mental health fallout of COVID-19 is showing two things: That unemployment is hitting <a href="https://www.pewsocialtrends.org/2020/09/24/economic-fallout-from-covid-19-continues-to-hit-lower-income-americans-the-hardest/" target="_blank" rel="noopener noreferrer">low-income and young Americans</a> most during the pandemic, potentially widening the health-wealth gap further; and that the pandemic not only exacerbates mental health stressors, but is doing so at clinically relevant levels. As <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7413844/" target="_blank" rel="noopener noreferrer">the authors of one review</a> wrote, the pandemic's effects on mental health is itself an international public health priority.
Working to close the health gap<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDc5MDk1MS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxNTYyMzQzMn0.KSFpXH7yHYrfVPtfgcxZqAHHYzCnC2bFxwSrJqBbH4I/img.jpg?width=980" id="b40e2" class="rm-shortcode" data-rm-shortcode-id="1b9035370ab7b02a0dc00758e494412b" data-rm-shortcode-name="rebelmouse-image" />
Northwell Health coronavirus testing center at Greater Springfield Community Church.
Credit: Northwell Health<p>Novel coronavirus may spread and infect indiscriminately, but pre-existing conditions, environmental stressors, and a lack of access to care and resources increase the risk of infection. These social determinants make the pandemic more dangerous, and erode communities' and families' abilities to heal from health crises that pre-date the pandemic.</p><p>How do we eliminate these divides? Dr. Salas-Lopez says the first step is recognition. "We have to open our eyes to see the suffering around us," she said. "Northwell has not shied away from that."</p><p>"We are steadfast in improving health outcomes for our vulnerable and underrepresented communities that have suffered because of the prevalence of chronic disease, a problem that led to the disproportionately higher death rate among African-Americans and Latinos during the COVID-19 pandemic," said Michael Dowling, Northwell's president and CEO. "We are committed to using every tool at our disposal—as a provider of health care, employer, purchaser and investor—to combat disparities and ensure the <a href="https://www.northwell.edu/education-and-resources/community-engagement/center-for-equity-of-care" target="_blank" rel="noopener noreferrer">equity of care</a> that everyone deserves." </p><p>With the need recognized, Dr. Salas-Lopez calls for health care systems to travel upstream and be proactive in those hard-hit communities. This requires health care systems to play a strong role, but not a unilateral one. They must build <a href="https://www.northwell.edu/news/insights/faith-based-leaders-are-the-key-to-improving-community-health" target="_blank" rel="noopener noreferrer">partnerships with leaders in those communities</a> and utilize those to ensure relationships last beyond the current crisis. </p><p>"We must meet with community leaders and talk to them to get their perspective on what they believe the community needs are and should be for the future. Together, we can co-create a plan to measurably improve [community] health and also to be ready for whatever comes next," she said.</p><p>Northwell has built relationships with local faith-based and community organizations in underserved communities of color. Those partnerships enabled Northwell to test more than 65,000 people across the metro New York region. The health system also offered education on coronavirus and precautions to curb its spread.</p><p>These initiatives began the process of building trust—trust that Northwell has counted on to return to these communities to administer flu vaccines to prepare for what experts fear may be a difficult flu season.</p><p>While Northwell has begun building bridges across the divides of the New York area, much will still need to be done to cure U.S. health care overall. There is hope that the COVID pandemic will awaken us to the deep disparities in the US.</p><p>"COVID has changed our world. We have to seize this opportunity, this pandemic, this crisis to do better," Dr. Salas-Lopez said. "Provide better care. Provide better health. Be better partners. Be better community citizens. And treat each other with respect and dignity.</p><p>"We need to find ways to unify this country because we're all human beings. We're all created equal, and we believe that health is one of those important rights."</p>
What’s Eminem doing in Missouri? Kanye West in Georgia? And Wiz Khalifa in, of all places, North Dakota?
This is a mysterious map. Obviously about music, or more precisely musicians. But what’s Eminem doing in Missouri? Kanye West in Georgia? And Wiz Khalifa in, of all places, North Dakota? None of these musicians are from those states! Everyone knows that! Is this map that stupid, or just looking for a fight? Let’s pause a moment and consider our attention spans, shrinking faster than polar ice caps.
Researchers make the case for "deep evidential regression."
- MIT researchers claim that deep learning neural networks need better uncertainty analysis to reduce errors.
- "Deep evidential regression" reduces uncertainty after only one pass on a network, greatly reducing time and memory.
- This could help mitigate problems in medical diagnoses, autonomous driving, and much more.
Credit: scharsfinn86 / Adobe Stock<p>On the road, 1 percent could be the difference between stopping at an intersection or rushing through just as another car runs a stop sign. Amini and colleagues wanted to produce a model that could better detect patterns in giant data sets. They named their solution "deep evidential regression."</p><p>Sorting through billions of parameters is no easy task. Amini's model utilizes uncertainly analysis—learning how much error exists within a model and supplying missing data. This approach in deep learning isn't novel, though it often takes a lot of time and memory. Deep evidential regression estimates uncertainty after only one run of the neural network. According to the team, they can assess uncertainty in both input data <em>and</em> the final decision, after which they can either address the neural network or recognize noise in the input data.</p><p>In real-world terms, this is the difference between trusting an initial medical diagnosis or seeking a second opinion. By arming AI with a built-in detection system for uncertainty, a new level of honesty with data is reached—in this model, with pixels. During a test run, the neural network was given novel images; it was able to detect changes imperceptible to the human eye. Ramini believes this technology can also be used to pinpoint <a href="https://www.theguardian.com/technology/2020/jan/13/what-are-deepfakes-and-how-can-you-spot-them" target="_blank">deepfakes</a>, a serious problem we must begin to grapple with.</p><p>Any field that uses machine learning will have to factor in uncertainty awareness, be it medicine, cars, or otherwise. As Amini says, </p><p style="margin-left: 20px;">"Any user of the method, whether it's a doctor or a person in the passenger seat of a vehicle, needs to be aware of any risk or uncertainty associated with that decision."</p><p>We might not have to worry about alien robots turning on us (yet), but we should be concerned with that new feature we just downloaded into our electric car. There will be many other issues to face with the emergence of AI in our world—and workforce. The safer we can make the transition, the better. </p><p>--</p><p><em>Stay in touch with Derek on <a href="http://www.twitter.com/derekberes" target="_blank">Twitter</a> and <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank" rel="noopener noreferrer">Facebook</a>. His new book is</em> "<em><a href="https://www.amazon.com/gp/product/B08KRVMP2M?pf_rd_r=MDJW43337675SZ0X00FH&pf_rd_p=edaba0ee-c2fe-4124-9f5d-b31d6b1bfbee" target="_blank" rel="noopener noreferrer">Hero's Dose: The Case For Psychedelics in Ritual and Therapy</a>."</em></p>
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