Far-right extremist groups show surging growth, new annual study shows

Will Carless

USAToday.com

Originally published 7 June 24

Far-right extremist groups are actively working to undermine U.S. democracy and are organizing in record numbers, according to an annual report from the Southern Poverty Law Center. Meanwhile, extremist groups have been targeting faith-based groups that assist migrants on the U.S.-Mexico border, and a New Jersey state trooper is fired for having a racist tattoo.

It’s the week in extremism.

Far-right extremists suffered a blow in the wake of the Jan. 6 insurrection. More than 1,000 people were charged and key leaders were imprisoned, some for decades. But a new annual report from the Southern Poverty Law Center suggests the far-right has regrouped and is taking aim at democratic institutions across the country. 

The information is here.

The Year in Hate and Extremism from the Southern Poverty Law Center.

Here are some thoughts:

A new study highlighting the surge in far-right extremism holds significant weight for psychologists working with marginalized groups. This growth presents a heightened risk of threats and violence for these communities. Psychologists can play a vital role by understanding the vulnerabilities extremists prey on, fostering resilience in marginalized groups, and promoting social cohesion to counter extremist narratives. By acknowledging this trend, psychologists can equip themselves to better support the mental health of these vulnerable populations.

“I lost trust”: Why the OpenAI team in charge of safeguarding humanity imploded

By Sigal Samuel

vox.com

Originally posted 18 May 24

For months, OpenAI has been losing employees who care deeply about making sure AI is safe. Now, the company is positively hemorrhaging them.

Ilya Sutskever and Jan Leike announced their departures from OpenAI, the maker of ChatGPT, on Tuesday. They were the leaders of the company’s superalignment team — the team tasked with ensuring that AI stays aligned with the goals of its makers, rather than acting unpredictably and harming humanity. 

They’re not the only ones who’ve left. Since last November — when OpenAI’s board tried to fire CEO Sam Altman only to see him quickly claw his way back to power — at least five more of the company’s most safety-conscious employees have either quit or been pushed out. 

What’s going on here?

If you’ve been following the saga on social media, you might think OpenAI secretly made a huge technological breakthrough. The meme “What did Ilya see?” speculates that Sutskever, the former chief scientist, left because he saw something horrifying, like an AI system that could destroy humanity. 

But the real answer may have less to do with pessimism about technology and more to do with pessimism about humans — and one human in particular: Altman. According to sources familiar with the company, safety-minded employees have lost faith in him.

The information is here.

Here are some thoughts:

The OpenAI team's reported issues expose critical ethical concerns in AI development. A potential misalignment of values emerges when profit or technological advancement overshadows safety and ethical considerations. Businesses must strive for transparency, prioritizing human well-being and responsible innovation throughout the development process.

Prioritizing AI Safety

The departure of the safety team underscores the need for robust safeguards. Businesses developing AI should dedicate resources to mitigating risks like bias and misuse. Strong ethical frameworks and oversight committees can ensure responsible development.

Employee Concerns and Trust

The article hints at a lack of trust within OpenAI. Businesses must foster open communication by addressing employee concerns about project goals, risks, and ethics. Respecting employee rights to raise ethical concerns is crucial for maintaining trust and responsible AI development.

By prioritizing ethical considerations, aligning values, and fostering transparency, businesses can navigate the complexities of AI development and ensure their creations benefit humanity.

Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings

Lee, L. Y., et al. (2024).

EClinicalMedicine, 102725.

https://doi.org/10.1016/j.eclinm.2024.102725

Background

Predicting dementia early has major implications for clinical management and patient outcomes. Yet, we still lack sensitive tools for stratifying patients early, resulting in patients being undiagnosed or wrongly diagnosed. Despite rapid expansion in machine learning models for dementia prediction, limited model interpretability and generalizability impede translation to the clinic.

Methods

We build a robust and interpretable predictive prognostic model (PPM) and validate its clinical utility using real-world, routinely-collected, non-invasive, and low-cost (cognitive tests, structural MRI) patient data. To enhance scalability and generalizability to the clinic, we: 1) train the PPM with clinically-relevant predictors (cognitive tests, grey matter atrophy) that are common across research and clinical cohorts, 2) test PPM predictions with independent multicenter real-world data from memory clinics across countries (UK, Singapore).

Interpretation

Our results provide evidence for a robust and explainable clinical AI-guided marker for early dementia prediction that is validated against longitudinal, multicenter patient data across countries, and has strong potential for adoption in clinical practice.

A pdf of the research can be found here.

Here is a summary and some thoughts:

Cambridge scientists have developed an AI tool capable of predicting with high accuracy whether individuals with early signs of dementia will remain stable or develop Alzheimer’s disease. This tool utilizes non-invasive, low-cost patient data such as cognitive tests and MRI scans to make its predictions, showing greater sensitivity than current diagnostic methods. The algorithm was able to correctly identify 82% of individuals who would develop Alzheimer’s and 81% of those who wouldn’t, surpassing standard clinical markers. This advancement could reduce the reliance on invasive and costly diagnostic tests and allow for early interventions, potentially improving treatment outcomes.

The machine learning model stratifies patients into three groups: those whose symptoms remain stable, those who progress slowly to Alzheimer’s, and those who progress rapidly. This stratification could help clinicians tailor treatments and closely monitor high-risk individuals. Validated with real-world data from memory clinics in the UK and Singapore, the tool demonstrates its applicability in clinical settings. The researchers aim to extend this model to other forms of dementia and incorporate additional data types, with the ultimate goal of providing precise diagnostic and treatment pathways, thereby accelerating the discovery of new treatments for dementia.

Behavioral attraction predicts morbidly curious women's mating interest in men with dark personalities

Khosbayar, A., Brown, M., & Scrivner, C. (2024).

Personality and Individual Differences, 228, 112738.

https://doi.org/10.1016/j.paid.2024.112738

Abstract

Morbid curiosity indexes interests in learning about dangerous phenomena. Individuals with high levels of dark triad traits (narcissism, psychopathy, and Machiavellianism) can be dangerous, implicating them as relatively desirable to those reporting heightened morbid curiosity. Despite the potential costs of high-dark triad men, it could benefit morbidly curious women to upregulate their preference for such men to satisfy short-term mating goals. This study tasked women to men exhibiting high and low levels of dark personality traits and complete a measure of trait morbid curiosity. Men described as exhibiting high levels of dark personality traits were more desirable as short-term mates than as long-term mates, although men described as reporting low levels of dark traits were more desirable overall. Morbidly curious women reported greater behavioral attraction toward dark-personality men but did not affective attraction. Findings suggest a function to morbidly curious women's interest in dark personalities.

The research is here.

Here are some thoughts:

This research sheds light on an intriguing pattern: women with a high level of morbid curiosity often exhibit a strong attraction to men with dark personality traits, such as narcissism, Machiavellianism, and psychopathy. This study highlights that behavioral attraction—initial intrigue and engagement—is a key predictor of these women’s mating interest in such men. For clinical psychologists, this insight is crucial in addressing the relational dynamics of their female patients who may be drawn to potentially harmful partners.

We play a pivotal role in helping these patients recognize and understand their attraction patterns. Through psychotherapy, we can assist patients in challenging and reframing their perceptions and decision-making processes regarding romantic interests. Emphasizing self-awareness and self-esteem can significantly reduce the allure of unhealthy relationships, enabling patients to set healthier boundaries and seek partners with positive traits.

Moreover, it's vital for therapists to educate their patients about the characteristics and risks associated with dark personality traits. By screening for attraction patterns during assessments and offering psychoeducation, psychologists can empower women to make safer relationship choices. Enhancing coping mechanisms and relationship skills are also critical strategies, providing patients with the tools to build healthy relationships and recognize red flags. Ultimately, these efforts can support women in navigating their romantic lives more safely and healthily.

Happiness and well-being: Is it all in your head?Evidence from the folk

Kneer, M., & Haybron, D. M. (2024).

Noûs/NoûS.

https://doi.org/10.1111/nous.12492

Abstract

Despite a voluminous literature on happiness and well-being, debates have been stunted by persistent dissensus on what exactly the subject matter is. Commentators frequently appeal to intuitions about the nature of happiness or well-being, raising the question of how representative those intuitions are. In a series of studies, we examined lay intuitions involving happiness- and well-being-related terms to assess their sensitivity to internal (psychological) versus external conditions. We found that all terms, including ‘happy’, ‘doing well’ and ‘good life’, were far more sensitive to internal than external conditions, suggesting that for laypersons, mental states are the most important part of happiness and well-being. But several terms, including ‘doing well’, ‘good life’ and ‘enviable life’ were substantially more sensitive to external conditions than others, such as ‘happy’, consistent with dominant philosophical views of well-being. Interestingly, the expression ‘happy’ was completely insensitive to external conditions for about two thirds of our participants, suggesting a purely psychological concept among most individuals. Overall, our findings suggest that lay thinking in this domain divides between two concepts, or families thereof: a purely psychological notion of being happy, and one or more concepts equivalent to, or encompassing, the philosophical concept of well-being. In addition, being happy is dominantly regarded as just one element of well-being. These findings have considerable import for philosophical debates, empirical research and public policy.

The article is linked above.

Here are some thoughts:

The authors argue that while cognitive and emotional processes are crucial, happiness is also significantly shaped by external elements such as social relationships, economic conditions, and physical health. This perspective challenges the notion that happiness is solely an internal state, emphasizing the importance of environmental influences.

Cultural narratives play a pivotal role in shaping perceptions of happiness, as different societies prioritize various aspects of well-being. Collectivist cultures may value social harmony and community well-being, whereas individualist cultures emphasize personal achievement and autonomy. Additionally, the article explores how folk psychology—the intuitive beliefs people have about their own and others' mental states—affects how happiness is understood and pursued, highlighting the widespread belief in the power of mindset and attitudes.

The article also touches on the philosophical dimensions of happiness, questioning whether it is a static state or a dynamic process involving purpose, fulfillment, and engagement with life's challenges. This inquiry suggests that happiness is more than just a collection of pleasurable experiences, but rather a complex phenomenon that integrates internal and external factors. Overall, the article calls for a holistic understanding of well-being that acknowledges the intricate interplay between mental states and socio-cultural contexts.

Can AI Understand Human Personality? -- Comparing Human Experts and AI Systems at Predicting Personality Correlations

Schoenegger, P., et al. (2024, June 12).

arXiv.org.

https://arxiv.org/abs/2406.08170

Abstract

We test the abilities of specialised deep neural networks like PersonalityMap as well as general LLMs like GPT-4o and Claude 3 Opus in understanding human personality. Specifically, we compare their ability to predict correlations between personality items to the abilities of lay people and academic experts. We find that when compared with individual humans, all AI models make better predictions than the vast majority of lay people and academic experts. However, when selecting the median prediction for each item, we find a different pattern: Experts and PersonalityMap outperform LLMs and lay people on most measures. Our results suggest that while frontier LLMs' are better than most individual humans at predicting correlations between personality items, specialised models like PersonalityMap continue to match or exceed expert human performance even on some outcome measures where LLMs underperform. This provides evidence both in favour of the general capabilities of large language models and in favour of the continued place for specialised models trained and deployed for specific domains.

A pdf is here.

Here are some thoughts on the intersection of technology and psychology.

The research investigates how AI systems fare against human experts, including both laypeople and academic psychologists, in predicting correlations between personality traits.

The findings suggest that AI, particularly specialized deep learning models, may outperform individual humans in this specific task. This is intriguing, as it highlights the potential of AI to analyze vast amounts of data and identify patterns that might escape human intuition. However, it's important to remember that personality is a complex interplay of internal states, experiences, and environmental factors.

While AI may excel at recognizing statistical connections, it currently lacks the ability to grasp the underlying reasons behind these correlations.  A true understanding of personality necessitates the human capacity for empathy, cultural context, and consideration of individual narratives. In clinical settings, for instance, a skilled psychologist goes beyond identifying traits; they build rapport, explore the origin of these traits, and tailor interventions accordingly. AI, for now, remains a valuable tool for analysis, but it should be seen as complementary to, rather than a replacement for, human expertise in understanding the rich tapestry of human personality.

Why Scientific Fraud Is Suddenly Everywhere

Kevin T. Dugan

New York Magazine

Originally posted 21 May 24

Junk science has been forcing a reckoning among scientific and medical researchers for the past year, leading to thousands of retracted papers. Last year, Stanford president Marc Tessier-Lavigne resigned amid reporting that some of his most high-profile work on Alzheimer’s disease was at best inaccurate. (A probe commissioned by the university’s board of trustees later exonerated him of manipulating the data).

But the problems around credible science appear to be getting worse. Last week, scientific publisher Wiley decided to shutter 19 scientific journals after retracting 11,300 sham papers. There is a large-scale industry of so-called “paper mills” that sell fictive research, sometimes written by artificial intelligence, to researchers who then publish it in peer-reviewed journals — which are sometimes edited by people who had been placed by those sham groups. Among the institutions exposing such practices is Retraction Watch, a 14-year-old organization co-founded by journalists Ivan Oransky and Adam Marcus. I spoke with Oransky about why there has been a surge in fake research and whether fraud accusations against the presidents of Harvard and Stanford are actually good for academia.

I’ll start by saying that paper mills are not the problem; they are a symptom of the actual problem. Adam Marcus, my co-founder, had broken a really big and frightening story about a painkiller involving scientific fraud, which led to dozens of retractions. That’s what got us interested in that. There were all these retractions, far more than we thought but far fewer than there are now. Now, they’re hiding in plain sight.

The info is here.

Here are some thoughts:

Recent headlines might suggest a surge in scientific misconduct. However, it's more likely that increased awareness and stricter scrutiny are uncovering existing issues. From an ethical standpoint, the pressure to publish groundbreaking research can create a challenging environment. Publication pressure, coupled with the human tendency towards confirmation bias, can incentivize researchers to take unethical shortcuts that align data with their hypotheses. This can have a ripple effect, potentially undermining the entire scientific process.

Fortunately, the heightened focus on research integrity presents an opportunity for positive change. Initiatives promoting open science practices, such as data sharing and robust replication studies, can foster greater transparency. Furthermore, cultivating a culture that rewards ethical research conduct and whistleblowing, even in the absence of earth-shattering results, is crucial.  Science thrives on self-correction. By acknowledging these challenges and implementing solutions, the scientific community can safeguard the integrity of research and ensure continued progress.

Harassment of scientists is surging — institutions aren’t sure how to help

Bianca Nogrady

Nature.com

Originally posted 21 May 24

As a vocal advocate of vaccinations for public health, Peter Hotez was no stranger to online harassment and threats. But then the abuse showed up on his doorstep.

It was a Sunday during a brutal Texas heatwave in June 2023 when a man turned up at Hotez’s home, filming himself as he shouted questions at the scientist, who is a paediatrician and virologist at Baylor College of Medicine in Houston, Texas.

Because of the long-running online and real-life abuse he has faced, Hotez now has the Texas Medical Center Police, Houston Police Department and Harris County Sheriff’s Office on speed dial, an agent tasked to him from the FBI and extra security whenever he speaks publicly.

“This is a very powerful adversarial force that is seeking to undermine science, and now it’s not only going after the science. It’s going after the scientists,” he says.

Hotez is an especially well-known scientist, but his experience is far from unique. Every day around the world, scientists are being abused and harassed online. They are being attacked on social media and by e-mail, telephone, letter and in person. And their reputations are being smeared with baseless accusations of misconduct. Sometimes, this escalates to real-world confrontations and attacks.

The article is here.

Here is my summary:

The article discusses a rise in harassment faced by scientists, particularly those doing research on hot-button topics like climate change. Universities and research institutions are struggling to develop effective ways to help these scientists.

Some scientists are targeted with online abuse and threats. Others fear repercussions within their field if they report harassment. This fear can silence important voices and discourage scientists from communicating their research.

The article highlights the debate about balancing safety with academic freedom. While some suggest limiting scientists' communication, others argue for better support systems and protection for researchers engaging in public outreach.

Honest Government Ad (aka PSA from John Connor)

JuiceMedia

Honest Government Ad - AI

July 2024

Note: Give me gallows humor that illuminates.  Since I post a great deal about the ethics, morality, and risk of AI, this seems appropriate. Enjoy!!