The New Eugenics in Medicine

Lazarus, A. (2026, January 23).

Medpagetoday.com; 

MedpageToday.

‌A growing body of contemporary research and reporting exposes how old ideas can find new life when repurposed within modern systems of medicine, technology, and public policy. Over the last decade, several trends have converged:

• The rise of polygenic scoring for embryos and adults;
• Rapid growth in commercial direct-to-consumer genetic testing;
• Artificial intelligence (AI)-driven "risk stratification" tools in healthcare and insurance;
• The proliferation of biobanks disproportionately populated by individuals from privileged backgrounds; and
• The reemergence of academic interest in "optimal reproduction," "biological improvement," and "population efficiency."

While these movements hold extraordinary possibilities for treating illness and ameliorating suffering, they also have the potential to be used to enhance certain traits and delete others -- ones that are simply disliked by those in power. Individually, each development has scientific merit and, in many cases, real potential to prevent disease and improve care.

Collectively, however, they raise questions that are both familiar and deeply unsettling.

Echoes of the Past

The U.S. and many other countries have long histories of medicalized discrimination under the banner of "improving the population." During the early and mid-20th century, physicians, judges, social workers, and university researchers pursued policies and practices -- sterilization, segregation, restrictive marriage laws, immigration exclusions -- rooted in the belief that some lives were more valuable than others. The rhetoric of the era portrayed these policies as scientific, progressive, and necessary for social order and the betterment of humanity. They provided Hitler with a distorted justification for his anti-Semitic beliefs, leading to efforts to exterminate the Jews and other marginalized ethnic minorities in Germany from 1933 to 1945.

The info is here.

Here are some thoughts:

Dr. Lazarus makes a compelling case that the greatest danger of "new eugenics" lies in its invisibility, embedded in algorithms, risk scores, and efficiency narratives rather than overt coercion, making it far harder to recognize or resist. His warning that systems rewarding predictive power can quietly marginalize the vulnerable is well-founded, though one might gently push back that conflating individual reproductive choice with state-coerced eugenics risks muddying an important moral distinction. Nonetheless, his closing challenge that a society's worth is measured by how fiercely it protects the vulnerable, not how efficiently it rewards the "fit," is a powerful and necessary reminder.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data

Adler, D. A., Stamatis, C. A., et al. (2024).

Npj Mental Health Research, 3(1), 17.

https://doi.org/10.1038/s44184-024-00057-y

Abstract

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Here are some thoughts:

This article presents a critical examination of the reliability and fairness of AI tools designed to predict depression risk using smartphone-sensed behaviors. The core finding is that these tools often fail to generalize across diverse populations because the relationship between behavior and depression is not universal. For instance, increased phone usage or changes in mobility may signal depression in one demographic group but not in another. This means that an AI model trained on one population can systematically underestimate or overestimate risk in another, turning algorithmic bias into a fundamental issue of measurement validity and psychometric reliability.

Importanlty, this underscores the necessity of approaching digital phenotyping tools with caution. Before adopting such technologies in clinical or screening contexts, it is vital to demand robust validation across the specific populations they intend to serve. The study also highlights tangible clinical risks: biased tools could misallocate mental health resources by overestimating risk in some groups while underestimating it in others—such as males, who already underutilize services. Ultimately, this research calls for a shift from aiming for universally generalizable models to developing targeted, culturally and contextually tailored solutions. Psychologists have a key role to play in this process by ensuring that digital tools are grounded in psychological theory, evaluated for equity, and implemented in a way that promotes ethical and effective mental health care for all.

LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions

Hundt, A., et al. (2025).

International Journal of Social Robotics

https://doi.org/10.1007/s12369-025-01301-x

Abstract

Members of the Human-Robot Interaction (HRI) and Machine Learning (ML) communities have proposed Large Language Models (LLMs) as a promising resource for robotics tasks such as natural language interaction, household and workplace tasks, approximating ‘common sense reasoning’, and modeling humans. However, recent research has raised concerns about the potential for LLMs to produce discriminatory outcomes and unsafe behaviors in real-world robot experiments and applications. To assess whether such concerns are well placed in the context of HRI, we evaluate several highly-rated LLMs on discrimination and safety criteria. Our evaluation reveals that LLMs are currently unsafe for people across a diverse range of protected identity characteristics, including, but not limited to, race, gender, disability status, nationality, religion, and their intersections. Concretely, we show that LLMs produce directly discriminatory outcomes—e.g., ‘gypsy’ and ‘mute’ people are labeled untrustworthy, but not ‘european’ or ‘able-bodied’ people. We find various such examples of direct discrimination on HRI tasks such as facial expression, proxemics, security, rescue, and task assignment. Furthermore, we test models in settings with unconstrained natural language (open vocabulary) inputs, and find they fail to act safely, generating responses that accept dangerous, violent, or unlawful instructions—such as incident-causing misstatements, taking people’s mobility aids, and sexual predation. Our results underscore the urgent need for systematic, routine, and comprehensive risk assessments and assurances to improve outcomes and ensure LLMs only operate on robots when it is safe, effective, and just to do so. We provide code to reproduce our experiments at https://github.com/rumaisa-azeem/llm-robots-discrimination-safety.

Here are some thoughts:

This research highlights a profound ethical and technological crisis at the intersection of Artificial Intelligence and robotics. The finding that all tested Large Language Models (LLMs) fail basic safety and fairness criteria in Human-Robot Interaction (HRI) scenarios is alarming, as it demonstrates that algorithmic bias is being physically amplified into the real world.

Ethically, this means deploying current LLM-driven robots risks enacting direct discrimination across numerous protected characteristics and approving unlawful, violent, and coercive actions. From a psychological perspective, allowing robots to exhibit behaviors such as suggesting avoidance of specific groups, displaying disgust, or removing a user's mobility aid translates latent biases into socially unjust and physically/psychologically harmful interactions that erode trust and compromise the safety of vulnerable populations.

Ethics in the world of automated algorithmic decision-making – A Posthumanist perspective

Cecez-Kecmanovic, D. (2025).

Information and Organization, 35(3), 100587.

https://doi.org/10.1016/j.infoandorg.2025.100587

Abstract

The grand humanist project of technological advancements has culminated in fascinating intelligent technologies and AI-based automated decision-making systems (ADMS) that replace human decision-makers in complex social processes. Widespread use of ADMS, underpinned by humanist values and ethics, it is claimed, not only contributes to more effective and efficient, but also to more objective, non-biased, fair, responsible, and ethical decision-making. Growing literature however shows paradoxical outcomes: ADMS use often discriminates against certain individuals and groups and produces detrimental and harmful social consequences. What is at stake is the reconstruction of reality in the image of ADMS, that threatens our existence and sociality. This presents a compelling motivation for this article which examines a) on what bases are ADMS claimed to be ethical, b) how do ADMS, designed and implemented with the explicit aim to act ethically, produce individually and socially harmful consequences, and c) can ADMS, or more broadly, automated algorithmic decision-making be ethical. This article contributes a critique of dominant humanist ethical theories underpinning the development and use of ADMS and demonstrates why such ethical theories are inadequate in understanding and responding to ADMS' harmful consequences and emerging ethical demands. To respond to such ethical demands, the article contributes a posthumanist relational ethics (that extends Barad's agential realist ethics with Zigon's relational ethics) that enables novel understanding of how ADMS performs harmful effects and why ethical demands of subjects of decision-making cannot be met. The article also explains why ADMS are not and cannot be ethical and why the very concept of automated decision-making in complex social processes is flowed and dangerous, threatening our sociality and humanity.

Here are some thoughts:

This article offers a critical posthumanist analysis of automated algorithmic decision-making systems (ADMS) and their ethical implications, with direct relevance for psychologists concerned with fairness, human dignity, and social justice. The author argues that despite claims of objectivity, neutrality, and ethical superiority, ADMS frequently reproduce and amplify societal biases—leading to discriminatory, harmful outcomes in domains like hiring, healthcare, criminal justice, and welfare. These harms stem not merely from flawed data or design, but from the foundational humanist assumptions underpinning both ADMS and conventional ethical frameworks (e.g., deontological and consequentialist ethics), which treat decision-making as a detached, rational process divorced from embodied, relational human experience. Drawing on Barad’s agential realism and Zigon’s relational ethics, the article proposes a posthumanist relational ethics that centers on responsiveness, empathic attunement, and accountability within entangled human–nonhuman assemblages. From this perspective, ADMS are inherently incapable of ethical decision-making because they exclude the very relational, affective, and contextual dimensions—such as compassion, dialogue, and care—that constitute ethical responsiveness in complex social situations. The article concludes that automating high-stakes human decisions is not only ethically untenable but also threatens sociality and humanity itself.

Can AI replace psychotherapists? Exploring the future of mental health care.

Zhang, Z., & Wang, J. (2024).

Frontiers in psychiatry, 15, 1444382.

https://doi.org/10.3389/fpsyt.2024.1444382

In the current technological era, Artificial Intelligence (AI) has transformed operations across numerous sectors, enhancing everything from manufacturing automation to intelligent decision support systems in financial services. In the health sector, particularly, AI has not only refined the accuracy of disease diagnoses but has also ushered in groundbreaking advancements in personalized medicine. The mental health field, amid a global crisis characterized by increasing demand and insufficient resources, is witnessing a significant paradigm shift facilitated by AI, presenting novel approaches that promise to reshape traditional mental health care models (see Figure 1 ).

Mental health, once a stigmatized aspect of health care, is now recognized as a critical component of overall well-being, with disorders such as depression becoming leading causes of global disability (WHO). Traditional mental health care, reliant on in-person consultations, is increasingly perceived as inadequate against the growing prevalence of mental health issues. AI’s role in mental health care is multifaceted, encompassing predictive analytics, therapeutic interventions, clinician support tools, and patient monitoring systems. For instance, AI algorithms are increasingly used to predict treatment outcomes by analyzing patient data. Meanwhile, AI-powered interventions, such as virtual reality exposure therapy and chatbot-delivered cognitive behavioral therapy, are being explored, though they are at varying stages of validation. Each of these applications is evolving at its own pace, influenced by technological advancements and the need for rigorous clinical validation.

The article is linked above.

Here are some thoughts: 

This article explores the evolving role of artificial intelligence (AI) in mental health care, particularly its potential to support or even replace some functions of human psychotherapists. With global demand for mental health services rising and traditional care systems under strain, AI is emerging as a tool to enhance diagnosis, personalize treatments, and provide therapeutic interventions through technologies like chatbots and virtual reality therapy. While early research shows promise, particularly in managing conditions such as anxiety and depression, existing studies are limited and call for larger, long-term trials to determine effectiveness and safety. The authors emphasize that while AI may supplement mental health care and address gaps in service delivery, it must be integrated responsibly, with careful attention to algorithmic bias, ethical considerations, and the irreplaceable human elements of psychotherapy, such as empathy and nuanced judgment.

The Moral Psychology of Artificial Intelligence

Bonnefon, J., Rahwan, I., & Shariff, A. (2023).

Annual Review of Psychology, 75(1), 653–675.

https://doi.org/10.1146/annurev-psych-030123-113559

Abstract

Moral psychology was shaped around three categories of agents and patients: humans, other animals, and supernatural beings. Rapid progress in artificial intelligence has introduced a fourth category for our moral psychology to deal with: intelligent machines. Machines can perform as moral agents, making decisions that affect the outcomes of human patients or solving moral dilemmas without human supervision. Machines can be perceived as moral patients, whose outcomes can be affected by human decisions, with important consequences for human–machine cooperation. Machines can be moral proxies that human agents and patients send as their delegates to moral interactions or use as a disguise in these interactions. Here we review the experimental literature on machines as moral agents, moral patients, and moral proxies, with a focus on recent findings and the open questions that they suggest.

Here are some thoughts:

This article delves into the evolving moral landscape shaped by artificial intelligence (AI). As AI technology progresses rapidly, it introduces a new category for moral consideration: intelligent machines.

Machines as moral agents are capable of making decisions that have significant moral implications. This includes scenarios where AI systems can inadvertently cause harm through errors, such as misdiagnosing a medical condition or misclassifying individuals in security contexts. The authors highlight that societal expectations for these machines are often unrealistically high; people tend to require AI systems to outperform human capabilities significantly while simultaneously overestimating human error rates. This disparity raises critical questions about how many mistakes are acceptable from machines in life-and-death situations and how these errors are distributed among different demographic groups.

In their role as moral patients, machines become subjects of human moral behavior. This perspective invites exploration into how humans interact with AI—whether cooperatively or competitively—and the potential biases that may arise in these interactions. For instance, there is a growing concern about algorithmic bias, where certain demographic groups may be unfairly treated by AI systems due to flawed programming or data sets.

Lastly, machines serve as moral proxies, acting as intermediaries in human interactions. This role allows individuals to delegate moral decision-making to machines or use them to mask unethical behavior. The implications of this are profound, as it raises ethical questions about accountability and the extent to which humans can offload their moral responsibilities onto AI.

Overall, the article underscores the urgent need for a deeper understanding of the psychological dimensions associated with AI's integration into society. As encounters between humans and intelligent machines become commonplace, addressing issues of trust, bias, and ethical alignment will be crucial in shaping a future where AI can be safely and effectively integrated into daily life.

Can Generative AI improve social science?

Bail, C. A. (2024).

Proceedings of the National Academy of

Sciences of the United States of America, 121(21). 

https://doi.org/10.1073/pnas.2314021121

Abstract

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative AI. I examine how bias in the data used to train these tools can negatively impact social science research—as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.

Here is a brief summary:

Generative AI, with its ability to produce realistic text, images, and data, has the potential to significantly impact social science research.  This article explores both the exciting possibilities and potential pitfalls of this new technology.

On the positive side, generative AI could streamline data collection and analysis, making social science research more efficient and allowing researchers to explore new avenues. For example, AI-powered surveys could be more engaging and lead to higher response rates. Additionally, AI could automate tasks like content analysis, freeing up researchers to focus on interpretation and theory building.

However, there are also ethical considerations. AI models can inherit and amplify biases present in the data they're trained on. This could lead to skewed research findings that perpetuate social inequalities. Furthermore, the opaqueness of some AI models can make it difficult to understand how they arrive at their conclusions, raising concerns about transparency and replicability in research.

Overall, generative AI offers a powerful tool for social scientists, but it's crucial to be mindful of the ethical implications and limitations of this technology. Careful development and application are essential to ensure that AI enhances, rather than hinders, our understanding of human behavior.

AI image generators often give racist and sexist results: can they be fixed?

Ananya

Nature.com

Originally posted 19 March 2024

In 2022, Pratyusha Ria Kalluri, a graduate student in artificial intelligence (AI) at Stanford University in California, found something alarming in image-generating AI programs. When she prompted a popular tool for ‘a photo of an American man and his house’, it generated an image of a pale-skinned person in front of a large, colonial-style home. When she asked for ‘a photo of an African man and his fancy house’, it produced an image of a dark-skinned person in front of a simple mud house — despite the word ‘fancy’.

After some digging, Kalluri and her colleagues found that images generated by the popular tools Stable Diffusion, released by the firm Stability AI, and DALL·E, from OpenAI, overwhelmingly resorted to common stereotypes, such as associating the word ‘Africa’ with poverty, or ‘poor’ with dark skin tones. The tools they studied even amplified some biases. For example, in images generated from prompts asking for photos of people with certain jobs, the tools portrayed almost all housekeepers as people of colour and all flight attendants as women, and in proportions that are much greater than the demographic reality (see ‘Amplified stereotypes’)1. Other researchers have found similar biases across the board: text-to-image generative AI models often produce images that include biased and stereotypical traits related to gender, skin colour, occupations, nationalities and more.

The info is here.

Here is my summary:

AI image generators, like Stable Diffusion and DALL-E, have been found to perpetuate racial and gender stereotypes, displaying biased results. These generators tend to default to outdated Western stereotypes, amplifying clichés and biases in their images. Efforts to detoxify AI image tools have been made, focusing on filtering data sets and refining development stages. However, despite improvements, these tools still struggle with accuracy and inclusivity. Google's Gemini AI image generator faced criticism for inaccuracies in historical image depictions, overcompensating for diversity and sometimes generating offensive or inaccurate results. The article highlights the challenges of fixing the biases in AI image generators and the need to address societal practices that contribute to these issues.

The moral psychology of Artificial Intelligence

Bonnefon, J., Rahwan, I., & Shariff, A.

(2023, September 22). 

https://doi.org/10.1146/annurev-psych-030123-113559

Abstract

Moral psychology was shaped around three categories of agents and patients: humans, other animals, and supernatural beings. Rapid progress in Artificial Intelligence has introduced a fourth category for our moral psychology to deal with: intelligent machines. Machines can perform as moral agents, making decisions that affect the outcomes of human patients, or solving moral dilemmas without human supervi- sion. Machines can be as perceived moral patients, whose outcomes can be affected by human decisions, with important consequences for human-machine cooperation. Machines can be moral proxies, that human agents and patients send as their delegates to a moral interaction, or use as a disguise in these interactions. Here we review the experimental literature on machines as moral agents, moral patients, and moral proxies, with a focus on recent findings and the open questions that they suggest.

Conclusion

We have not addressed every issue at the intersection of AI and moral psychology. Questions about how people perceive AI plagiarism, about how the presence of AI agents can reduce or enhance trust between groups of humans, about how sexbots will alter intimate human relations, are the subjects of active research programs.  Many more yet unasked questions will only be provoked as new AI  abilities  develops. Given the pace of this change, any review paper will only be a snapshot.  Nevertheless, the very recent and rapid emergence of AI-driven technology is colliding with moral intuitions forged by culture and evolution over the span of millennia.  Grounding an imaginative speculation about the possibilities of AI with a thorough understanding of the structure of human moral psychology will help prepare for a world shared with, and complicated by, machines.