One potentially positive outcome from the COVID pandemic is that it was a wakeup call – if there was any doubt previously about the fact that we all live in one giant interconnected world, it should not have survived the recent pandemic. This is particularly true when it comes to infectious disease. A bug that breaks out on the other side of the world can make its way to your country, your home, and cause havoc. It’s also not just about the spread of infectious organisms, but the breeding of these organisms.

One source of infectious agents is zoonotic spillover, where viruses, for example, can jump from an animal reservoir to a human. So the policies in place in any country to reduce the chance of this happening affect the world. The same is true of policies for laboratories studying potentially infectious agents.

It’s also important to remember that infectious agents are not static – they evolve. They can evolve even within a single host as they replicate, and they can evolve as they jump from person to person and replicate some more. The more bugs are allows to replicate, the greater the probability that new mutations will allow them to become more infectious, or more deadly, or more resistant to treatment. Resistance to treatment is especially critical, and is more likely to happen in people who are partially treated. Give someone an antibiotic to kill 99.9% of the bacteria that’s infecting them, but stop before the infection is completely wiped out, and then the surviving bacteria can resume replication. Those surviving bacteria are likely to be the most resistant bugs to the antibiotic. Bacteria can also swap antibiotic resistant genes, and build up increasing resistance.

In short, controlling infectious agents is a world-wide problem, and it requires a world-wide response. Not only is this a humanitarian effort, it is in our own best self-interest. The rest of the world is a breeding ground for bugs that will come to our shores. This is why we really need an organization, funded by the most wealthy nations, to help establish, fund, and enforce good policies when it comes to identifying, treating, and preventing infectious illness. This includes vaccination programs, sanitation, disease outbreak monitoring, drug treatment programs, and supportive care programs (like nutrition). We would also benefit from programs that target specific hotspots of infectious disease in poor countries that do not have the resources to adequately deal with them, like HIV in sub-Saharan Africa, and tuberculosis in Bangladesh.

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It’s probably not a surprise that a blog author dedicated to critical thinking and neuroscience feels that misinformation is one of the most significant threats to society, but I really to think this. Misinformation (false, misleading, or erroneous information) and disinformation (deliberately misleading information) have the ability to cause a disconnect between the public and reality. In a democracy this severs the feedback loop between voters and their representatives. In an authoritarian government it a tool of control and repression. In either case citizens cannot freely choose their representatives. This is also the problem with extreme jerrymandering – in which politicians choose their voters rather than the other way around.

Misinformation and disinformation have always existed in human society, and it is an interesting question whether or not they have increased recently and to what extent social media has amplified them. Regardless, it is useful to understand what factors contribute to susceptibility to misinformation in order to make people more resilient to it. We all benefit if the typical citizen has the ability to discern reality and identify fake news when they see it.

There has been a lot of research on this question over the years, and I have discussed it often, but it’s always useful to try to gather together years of research into a single systematic review and/or meta-analysis. It’s possible I and others may be selectively choosing or remembering parts of the research to reinforce a particular view – a problem that can be solved with a thorough analysis of all existing data. And of course I must point out that such reviews are subject to their own selection bias, but if properly done such bias should be minimal. The best case scenario is for there to be multiple systematic reviews, so I can get a sense of the consensus of those reviews, spreading out bias as much as possible in the hopes it will average out in the end.

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A recent BBC article highlights some of the risk of the new age of social media we have crafted for ourselves. The BBC investigated the number one ranked UK podcast, Diary of a CEO with host Steven Bartlett, for the accuracy of the medical claims recently made on the show. While the podcast started out as focusing on tips from successful businesspeople, it has recently turned toward unconventional medical opinions as this has boosted downloads.

“In an analysis of 15 health-related podcast episodes, BBC World Service found each contained an average of 14 harmful health claims that went against extensive scientific evidence.”

These includes showcasing an anti-vaccine crank, Dr. Malhotra, who claimed that the “Covid vaccine was a net negative for society”. Meanwhile the WHO estimates that the COVID vaccine saved 14 million lives worldwide. A Lancet study estimates that in the European region alone the vaccine saved 1.4 million lives. This number could have been greater were in not for the very type of antivaccine misinformation spread by Dr. Malhotra.

Another guest promoted the Keto diet as a treatment for cancer. Not only is there no evidence to support this claim, dietary restrictions while undergoing treatment for cancer can be very dangerous, and imperil the health of cancer patients.

This reminds me of the 2014 study that found that, “For recommendations in The Dr Oz Show, evidence supported 46%, contradicted 15%, and was not found for 39%.” Of course, evidence published in the BMJ does little to counter misinformation spread on extremely popular shows. The BBC article highlights the fact that in the UK podcasts are not covered by the media regulator Ofcom, which has standards of accuracy and fairness for legacy media.

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At CSICON this year I gave talk about topics over which skeptics have and continue to disagree with each other. My core theme was that these are the topics we absolutely should be discussing with each other, especially at skeptical conferences. Nothing should be taboo or too controversial. We are an intellectual community dedicated to science and reason, and have spent decades talking about how to find common ground and resolve differences, when it comes to empirical claims about reality. But the fact is we sometimes disagree, and this is a great learning opportunity. It’s also humbling, reminding ourselves that the journey toward critical thinking and reason never ends. On several topics self-identified skeptics disagree largely along political grounds, which is a pretty sure sign we are not immune to ideology and partisanship.

I spent most of the talk, however, discussing the issue of biological sex in humans, which I perceive as the currently most controversial topic within skepticism. My goal was to explore where it is we actually disagree. Generally speaking skeptics don’t disagree about the facts or about the proper role of science in determining what is likely to be true. We tend to disagree for more subtle reasons, although often the reason does come down to a lack of specific topic expertise on questions that are highly technical. The most important thing is that we actually engage with each-other’s arguments and positions, to make sure we truly understand what those who disagree with us are saying so that we can properly explore premises and logic.

Jerry Coyne, author of the book and blog Why Evolution is True, was also at CSICON and gave a talk essentially taking the opposing position to my own. His position is that biological sex in humans is binary, that this is the only scientific position, and anything else is simply ideology trumping science. His talk was after mine so I was very interested in how he would respond to my position. He essentially didn’t – he just gave the talk he was going to give and then included a single slide with his “responses” to my talk. Except, they weren’t responses at all, just a list of standard talking points that really had nothing to do with my talk.

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How certain are you of anything that you believe? Do you even think about your confidence level, and do you have a process for determining what your confidence level should be or do you just follow your gut feelings?

Thinking about confidence is a form of metacognition – thinking about thinking. It is something, in my opinion, that we should all do more of, and it is a cornerstone of scientific skepticism (and all good science and philosophy). As I like to say, our brains are powerful tools, and they are our most important and all-purpose tool for understanding the universe. So it’s extremely useful to understand how that tool works, including all its strengths, weaknesses, and flaws.

A recent study focuses in on one tiny slice of metacognition, but an important one – how we form confidence in our assessment of a situation or a question. More specifically, it highlights The illusion of information adequacy. This is yet another form of cognitive bias. The experiment divided subjects into three groups – one group was given one half of the information about a specific situation (the information that favored one side), while a second group was given the other half. The control group was given all the information. They were then asked to evaluate the situation and how confident they were in their conclusions. They were also asked if they thought other people would come to the same conclusion.

You can probably see this coming – the subjects in the test groups receiving only half the information felt that they had all the necessary information to make a judgement and were highly confident in their assessment. They also felt that other people would come to the same conclusion as they did. And of course, the two test groups came to the conclusion favored by the information they were given.

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I am not the first to say this but it bears repeating – it is wrong to use the accusation of a mental illness as a political strategy. It is unfair, stigmatizing, and dismissive. Thomas Szasz (let me say straight up – I am not a Szaszian) was a psychiatrist who made it his professional mission to make this point. He was concerned especially about oppressive governments diagnosing political dissidents with mental illness and using that as a justification to essentially imprison them.

Szasz had a point (especially back in the 1960s when he started making it) but unfortunately took his point way too far, as often happens. He decided that mental illness, in fact, does not exist, and is 100% political oppression. He took a legitimate criticism of the institution of mental health and abuse by oppressive political figures and systems and turned it into science denial. But that does not negate the legitimate points at the core of his argument – we should be careful not to conflate unpopular political opinions with mental illness, and certainly not use it as a deliberate political strategy.

While the world of mental illness is much better today (at least in developed nations), the strategy of labeling your political opponents as mentally ill continues. I truly sincerely wish it would stop. For example, in a recent interview on ABC, senator Tom Cotton was asked about some fresh outrageous thing Trump said, criticism of which Cotton waved away as “Trump Derangement Syndrome”.

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It’s been less than two years (November 2022) since ChatGPT launched. In some ways the new large language model (LLM) type of artificial intelligence (AI) applications have been on the steep part of the improvement curve. And yet, they are still LLMs with the same limitations. In the last two years I have frequently used ChatGPT and other AI applications, and often give them tasks just to see how they are doing.

For a quick review, LLMs are AIs that are trained on vast amounts of information from the internet. They essentially predict the next work chunk in order to build natural-sounding responses to queries. Their responses therefore represent a sort-of zeitgeist of the internet, building on what is out there. Responses are therefore necessarily derivative, but can contain unique combinations of information. This has lead to a so-far endless debate about how truly creative LLMs can be, or if they are just stealing and regurgitating content from human creators.

What I am finding is that LLMs are getting better at doing what they do, but have not broken out of the limitations of this regurgitation model. Here is a good example from the New York Times – an author (Curtis Sittenfeld) wrote a short story based on the same prompt given to ChatGPT, and published both to see if readers could tell the difference. For me, I knew right away which story was AI. The author’s story was interesting and engaging. ChatGPTs story bored me before the end of the first paragraph. It was soulless and mechanical. It reminded me of a bad story written by a freshman in high school. It got the job done, and used some tired and predictable literary devices, but failed to engage the reader and lacked any sense of taking the reader on an emotional journey.

This reinforced for me what I suspected from my own interactions – LLMs are getting better at being LLMs, but have not broken out of their fundamental limitations.

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This kind of abuse of deepfake endorsements was entirely predictable, so it’s not surprising that a recent BMJ study documents the scale of this fraud. The study focused on the UK, detailing instances of deepfakes of celebrity doctors endorsing dubious products. For example, there is this video of Dr. Hilary Jones used to endorse a snake oil product claiming to reduce blood pressure. The video is entirely fake. It’s also interesting that in the video the fake Jones refers only to “this product” – as if the deepfakers made a generic endorsement (ala Krusty the Clown) that could be then attached to any product.

This trend is obviously disturbing, although again entirely expected. This use of deepfakes is deliberate fraud, and should be treated as such. Public figures have a right to their own identity, including their name and likeness. Laws vary by country and by state, but most have some limited protections for the use of someone’s name or likeness. In the US, for example, there is a limited “right of publicity” which limits the use of someone’s name or likely for commercial purposes without their permission. This can also extend beyond death, with the estate holding the rights. Even imitating a recognizable voice has been successfully sued.

This means that using a deepfake clearly violates the right of publicity – in fact it is the ultimate violation of that right. There are generally three legal remedies for violations – monetary damages, injunctive relief, and punitive damages.

How good are the deepfakes? Good enough, especially if you are viewing a relatively low-res video on social media. And of course they are only getting better. We cannot wait until deepfakes are good enough to fool most people, right now they are high enough quality to constitute fraud. So what do we do about it?

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Which is worse – clickbaity headlines for news articles that are factually correct, but may be playing up a sensational angle, or straight-up misinformation? It depends on what you mean by “worse”. A new study tries to address this information, with some interesting findings.

Misinformation is an increasingly important topic, one with far reaching implications for society. Our individual lives and our society is increasingly run on information. It is a critical resource, and the ability to evaluate and utilize information may be a determining factor in our quality of life. My favorite example remains Steve Jobs, because he is such a stark example. He was one of the richest people on the planet, with every physical resource at his disposal, and was a titan of an information industry. And yet he died prematurely of a potentially curable disease. He chose to delay mainstream treatment in order to pursue “natural” therapies that were ultimately worthless. We cannot know for sure what would have happened if he did not take this course, but his odds of survival would have been better.

At a societal level the most visible impact that our information ecosystems have deals with politics and public health. We are facing a rather dramatic decision regarding the next presidential election in the US, and this will ultimately be determined by how people are accessing and evaluating information. This has always been the case in a democracy, but I think most people alive today have not experienced a divergence of narrative and opinion as intense as we have today.

We also just when through the worst pandemic in a century, which brought into focus every issue dealing with misinformation. How do we deal with it in an age of social media? How do we balance the interests of making sure people get accurate health information so they can make informed choices, and freedom of speech and the value of open debate? There is no one correct answer, we just have to choose our tradeoffs.

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There is an interesting disconnect in our culture recently. About 90% of people claim that they verify information they encounter in the news and on social media, and 96% of Americans say that we need to limit the spread of misinformation online. And yet, the spread of misinformation is rampant. Most people, 74%, report that that they have seen information online labeled as false. Only about 60% of people report regularly checking information before sharing it. And a relatively small number of users spread a disproportionate amount of misinformation.

Of course, what is considered “misinformation” is often is the eyes of the beholder. We tend to silo ourselves in information ecosystems that share our worldview, and define misinformation relative to our chosen outlets. Republicans and Democrats, for example, trust completely different sources of news, with no overlap (in the most trusted sources). What’s fake news on Fox, is mainstream news on MSNBC, and vice versa. There is not only a difference in what is considered real vs fake news, but how the news is curated. Choosing certain stories to amplify over others can greatly distort one’s view of reality.

Misinformation is not new, but the networks of how it is created and shared is changing fairly quickly. If we all agree we need to stem the tide of misinformation, how do we do it? As is often the case with big social or systemic questions like this, we can take a top-down or bottom-up approach. The top-down approach is for social media platforms and news outlets to take responsibility for the quality of the information being spread on their watch. Clear misinformation can be identified and then nipped in the bud. AI algorithms backed up by human evaluators can kill a lot of misinformation, if the platform wants. Also, they can choose algorithms that favor quality and reliability over sensationalism and maximizing clicks and eyeballs. In addition, government regulations can influence the incentives for platforms and outlets to favor reliability over sensationalism.

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