Author Archives: beatriceco

THE REPRO RUNDOWN | Inside the Science and Politics of In Vitro Fertilization

In the wake of President-elect Donald Trump’s victory, women and birthing people have been left to wonder how his return to power will impact their reproductive rights. Though his political record demonstrates an unwillingness to recognize key reproductive freedoms, including abortion, his avowed support for in vitro fertilization (IVF) breaks ranks with many Republican Party politicians who oppose the procedure. Trump, in an effort to appeal to the majority of U.S. voters who support IVF, even proclaimed himself the “father of IVF,” despite requiring Senator Katie Britt (R-Ala.) to explain the procedure to him earlier this year.
While Democrats and Republicans debate the future of IVF, around 22% of U.S. adults remain unsure if they support the procedure, possibly owing to a lack of knowledge about what IVF actually entails. Moreover, cultural taboo contributes to misconceptions about the treatment — for example, the Catholic Church condemns IVF, decrying that it disassociates marriage from procreation. So, putting politics and culture aside, what is the science behind IVF?
The journey from lab to life begins when a person seeks to become pregnant. There are many reasons someone might seek the help of assisted reproductive technology: 1 in 8 U.S. couples struggle with infertility, same-sex partners can use IVF to start a family and cancer patients may freeze their eggs to protect their fertility from chemotherapy-induced damage. Regardless, the first step in IVF is an evaluation of the male and female partners’ eggs and sperm, ensuring that at least some of their reproductive cells, called gametes, are healthy. Then, the female partner receives hormone injections for 10 to 12 days, which allows multiple eggs to mature inside the ovary. 
The next step in IVF is egg retrieval, which occurs while the patient is under anesthesia. With the guidance of an ultrasound machine, a doctor inserts a thin needle through the vagina into the ovary and extracts 10 to 20 eggs. The doctor then places the eggs in a dish with sperm isolated from the male partner’s semen. 
The fertilized eggs grow in an incubator until they reach the blastocyst stage of development, typically after 3 to 7 days. This incubation period — during which growth occurs in an artificial environment instead of in a live organism — explains why the procedure is called “in vitro.” What characterizes a blastocyst embryo are two layers of cells: one that becomes the fetus and one that becomes the placenta. 
Finally, doctors either freeze the blastocyst embryos for future use or transfer one or more of them into the patient’s uterus. Herein lies the main point of contention for IVF’s opponents: If frozen embryos are accidentally destroyed, does that constitute murder? That is, should embryos enjoy the same rights as people? 
In February 2024, many in the United States reacted with shock and dismay at the Alabama Supreme Court’s answer to these questions. The court ruled that the accidental destruction of human embryos at IVF clinics constitutes child murder, spurring fertility clinics to cease operations. Though state lawmakers responded with a measure that protects IVF providers from wrongful death lawsuits, many clinicians remained worried that the court’s ruling could expose them to legal repercussions. 
Their legal concerns stem from the fact that it is common for fertilized eggs not to survive: 45% of lab-grown IVF embryos die before becoming blastocysts, and plenty of others die in the female body even when naturally conceived. Furthermore, when couples are left with extra embryos after a successful round of IVF, it is common practice for clinics to destroy them or donate them to research. 
Given that IVF enjoys widespread support throughout the United States, it is unlikely that concerns about fetal personhood will lead to significant bans, especially as the GOP remains split on the issue. However, fertility experts worry that increasing the legal risk of IVF could impose restrictions on the use of embryos, rendering the process more inefficient and requiring couples to undergo more cycles. IVF is already quite cost prohibitive, with one cycle costing up to $24,000 and lacking insurance coverage in 31 U.S. states. 
In the United States, the debate about IVF is far from settled — as recently as September 2024, Senate Republicans blocked a bill that would have enacted federal protections for IVF, contradicting Trump’s assertion that he wants to expand access to the treatment. Nevertheless, the science underpinning the safety and efficacy of IVF remains steadfast, as does the fact that thousands of families could not have exercised their right to bear children in its absence.

Will AI Agents Replace Human Subjects in Social Science?

In a groundbreaking study that reads like a blueprint for the future of social science, researchers have unveiled an innovative use of artificial intelligence to simulate the behavior, attitudes, and decisions of over 1,000 people.

Drawing from detailed qualitative interviews, these “generative agents” replicated participants’ responses with uncanny accuracy, begging the question about whether AI might one day render human recruitment for psychological and social science research obsolete.

By embedding the findings of extensive interviews into a large language model, the researchers created AI-driven agents capable of emulating the responses of real people to various surveys and experiments.

These agents mirrored the responses of the 1,052 human participants with an accuracy of 85%.

An example is their answers to the General Social Survey (GSS), a widely used sociological survey that assesses attitudes and beliefs.And 85% actually exceeds the consistency of humans when retaking the same tests several weeks apart.

The study, conducted by a consortium of scholars from Stanford, Northwestern, Google DeepMind, and the University of Washington, introduces a new paradigm for human behavioral simulation.The study was published on Cornell University’s arXiv.org, an open-access repository for academic research, on November 15, 2024.ArXiv is widely used by researchers in fields like computer science, physics, mathematics, and social sciences to share preprints of work that has not yet undergone peer review.

The Promise of Simulated Research

For decades, social scientists have relied on labor-intensive methods to recruit diverse participants, administer surveys, and run experiments.While these traditional approaches provide valuable insights, they also come with significant costs and logistical hurdles.

By offering a scalable, ethical alternative, generative agents could become a powerful tool for exploring human behavior on a massive scale.

Imagine testing public health messages, economic policies, marketing campaigns, or educational programs across thousands of simulated people representing diverse demographic groups, all without scheduling a single in-person session or paying hefty participation fees.

The authors describe this as creating “a laboratory for researchers to test a broad set of interventions and theories.”

Revolutionizing Psychology and Personality Research

Psychology and personality research have long relied on painstaking methods to gather data from human participants.Studies often involve surveys, interviews, or experiments conducted in laboratory or virtual settings, requiring significant investments of time, labor, and money.

These tasks typically involve many researchers and assistants.Participants, in turn, must dedicate their time, often over multiple sessions, leading to logistical challenges and high costs for compensation.

Participants also often receive compensation, and some studies additional payments for follow-up sessions or incentives for economic games.Including wages for researchers, software costs, and institutional overhead, a study of this scale could easily run into six figures and take months — or even years — to complete.

Using AI-driven generative agents offers a transformative alternative.Instead of recruiting and surveying people, researchers could program these agents with data from previous interviews or personality assessments.

These agents, trained on data from tools like the Big Five Inventory or General Social Survey, can simulate responses to hypothetical scenarios, yielding insights that closely mirror human behaviors.

By eliminating the need to recruit participants or administer surveys, a study replicating the scope of traditional research could be completed in days instead of months.

Researchers could simulate additional scenarios or experiments without incurring significant extra effort or expense.

Beyond the immediate savings, this approach opens new doors for smaller research teams or institutions with limited funding, enabling them to conduct large-scale studies that were previously out of reach.

By reshaping the logistical and economic landscape of social and psychological research, generative agents could significantly accelerate the pace of discovery in understanding human behavior and personality.

Methodology

The study recruited a sample of 1,052 U.S. participants via Bovitz, a study recruitment firm.They were diverse in terms of age, region, education, ethnicity, gender, income, political ideology, and sexual identity to represent the broader population.Ages ranged from 18 to 84, with a mean of 48 years.

The participants completed two-hour voice interviews conducted by an AI interviewer, which asked follow-up questions based on participants’ responses, ensuring depth and richness of data.

The interviews explored personal history, values, and opinions on societal topics, capturing an average of 6,491 words per participant.These transcripts formed the knowledge base for creating the generative agents.

To evaluate the AI agents, the human participants completed the General Social Survey (GSS), the Big Five Personality Inventory (BFI-44), behavioral economic games like the Dictator Game and Trust Game.

Two weeks after the initial interview, participants retook the surveys and experiments to provide a benchmark for their internal consistency.

The transcripts of the participant interviews were embedded into a large language model to create the AI agents, and these agents were evaluated by comparing their responses to human responses.

But 85% is far from perfect, right?

While this may not seem perfect, it is remarkably close to human-like accuracy, particularly since human respondents themselves are inconsistent over time.

The researchers measured this inconsistency by asking participants to retake surveys and experiments two weeks after their initial responses.The human participants’ own replication accuracy — the rate at which they gave the same answers on both occasions — was 81% on average.This indicates that human responses naturally vary, influenced by factors like memory, mood, and context.

In other words, 85% accuracy effectively nears the ceiling of what could be expected from humans.As the researchers note, the generative agents “predict participants’ behavior and attitudes well, especially when compared to participants’ own rate of internal consistency.”

So while improvements are still possible, the agents are already effective enough to simulate behaviors and attitudes with a level of fidelity that mirrors real-world variability in human responses.

A New Era of Accelerated Social Science Discovery

This new methodology allows for rapid hypothesis testing, simultaneous exploration of multiple research questions, and instant availability of results, drastically shortening the time from concept to insight.

Meta-analyses, traditionally time-intensive, could become standard practice, allowing researchers to validate findings across large datasets quickly and systematically.

By testing complex interactions, exploring diverse scenarios, and developing personalized theories, the field could address long-standing challenges like the reproducibility crisis while advancing ethical and policy interventions at scale.

“Human behavioral simulation—general-purpose computational agents that replicate human behavior across domains—could enable broad applications in policymaking and social science,” the researchers write.

Study details:

Title: “Generative Agent Simulations of 1,000 People”

Date Submitted: 15 Nov 2024

Authors: Joon Sung Park (Stanford University), Carolyn Q. Zou (Stanford University/Northwestern University), Aaron Shaw (Northwestern University), Benjamin Mako Hill (University of Washington), Carrie Cai (Google DeepMind), Meredith Ringel Morris (Google DeepMind), Robb Willer (Stanford University), Percy Liang (Stanford University), Michael S. Bernstein (Stanford University)

Link: https://arxiv.org/abs/2411.10109

Will AI Agents Replace Human Subjects in Social Science?

In a groundbreaking study that reads like a blueprint for the future of social science, researchers have unveiled an innovative use of artificial intelligence to simulate the behavior, attitudes, and decisions of over 1,000 people.

Drawing from detailed qualitative interviews, these “generative agents” replicated participants’ responses with uncanny accuracy, begging the question about whether AI might one day render human recruitment for psychological and social science research obsolete.

By embedding the findings of extensive interviews into a large language model, the researchers created AI-driven agents capable of emulating the responses of real people to various surveys and experiments.

These agents mirrored the responses of the 1,052 human participants with an accuracy of 85%.

An example is their answers to the General Social Survey (GSS), a widely used sociological survey that assesses attitudes and beliefs.And 85% actually exceeds the consistency of humans when retaking the same tests several weeks apart.

The study, conducted by a consortium of scholars from Stanford, Northwestern, Google DeepMind, and the University of Washington, introduces a new paradigm for human behavioral simulation.The study was published on Cornell University’s arXiv.org, an open-access repository for academic research, on November 15, 2024.ArXiv is widely used by researchers in fields like computer science, physics, mathematics, and social sciences to share preprints of work that has not yet undergone peer review.

The Promise of Simulated Research

For decades, social scientists have relied on labor-intensive methods to recruit diverse participants, administer surveys, and run experiments.While these traditional approaches provide valuable insights, they also come with significant costs and logistical hurdles.

By offering a scalable, ethical alternative, generative agents could become a powerful tool for exploring human behavior on a massive scale.

Imagine testing public health messages, economic policies, marketing campaigns, or educational programs across thousands of simulated people representing diverse demographic groups, all without scheduling a single in-person session or paying hefty participation fees.

The authors describe this as creating “a laboratory for researchers to test a broad set of interventions and theories.”

Revolutionizing Psychology and Personality Research

Psychology and personality research have long relied on painstaking methods to gather data from human participants.Studies often involve surveys, interviews, or experiments conducted in laboratory or virtual settings, requiring significant investments of time, labor, and money.

These tasks typically involve many researchers and assistants.Participants, in turn, must dedicate their time, often over multiple sessions, leading to logistical challenges and high costs for compensation.

Participants also often receive compensation, and some studies additional payments for follow-up sessions or incentives for economic games.Including wages for researchers, software costs, and institutional overhead, a study of this scale could easily run into six figures and take months — or even years — to complete.

Using AI-driven generative agents offers a transformative alternative.Instead of recruiting and surveying people, researchers could program these agents with data from previous interviews or personality assessments.

These agents, trained on data from tools like the Big Five Inventory or General Social Survey, can simulate responses to hypothetical scenarios, yielding insights that closely mirror human behaviors.

By eliminating the need to recruit participants or administer surveys, a study replicating the scope of traditional research could be completed in days instead of months.

Researchers could simulate additional scenarios or experiments without incurring significant extra effort or expense.

Beyond the immediate savings, this approach opens new doors for smaller research teams or institutions with limited funding, enabling them to conduct large-scale studies that were previously out of reach.

By reshaping the logistical and economic landscape of social and psychological research, generative agents could significantly accelerate the pace of discovery in understanding human behavior and personality.

Methodology

The study recruited a sample of 1,052 U.S. participants via Bovitz, a study recruitment firm.They were diverse in terms of age, region, education, ethnicity, gender, income, political ideology, and sexual identity to represent the broader population.Ages ranged from 18 to 84, with a mean of 48 years.

The participants completed two-hour voice interviews conducted by an AI interviewer, which asked follow-up questions based on participants’ responses, ensuring depth and richness of data.

The interviews explored personal history, values, and opinions on societal topics, capturing an average of 6,491 words per participant.These transcripts formed the knowledge base for creating the generative agents.

To evaluate the AI agents, the human participants completed the General Social Survey (GSS), the Big Five Personality Inventory (BFI-44), behavioral economic games like the Dictator Game and Trust Game.

Two weeks after the initial interview, participants retook the surveys and experiments to provide a benchmark for their internal consistency.

The transcripts of the participant interviews were embedded into a large language model to create the AI agents, and these agents were evaluated by comparing their responses to human responses.

But 85% is far from perfect, right?

While this may not seem perfect, it is remarkably close to human-like accuracy, particularly since human respondents themselves are inconsistent over time.

The researchers measured this inconsistency by asking participants to retake surveys and experiments two weeks after their initial responses.The human participants’ own replication accuracy — the rate at which they gave the same answers on both occasions — was 81% on average.This indicates that human responses naturally vary, influenced by factors like memory, mood, and context.

In other words, 85% accuracy effectively nears the ceiling of what could be expected from humans.As the researchers note, the generative agents “predict participants’ behavior and attitudes well, especially when compared to participants’ own rate of internal consistency.”

So while improvements are still possible, the agents are already effective enough to simulate behaviors and attitudes with a level of fidelity that mirrors real-world variability in human responses.

A New Era of Accelerated Social Science Discovery

This new methodology allows for rapid hypothesis testing, simultaneous exploration of multiple research questions, and instant availability of results, drastically shortening the time from concept to insight.

Meta-analyses, traditionally time-intensive, could become standard practice, allowing researchers to validate findings across large datasets quickly and systematically.

By testing complex interactions, exploring diverse scenarios, and developing personalized theories, the field could address long-standing challenges like the reproducibility crisis while advancing ethical and policy interventions at scale.

“Human behavioral simulation—general-purpose computational agents that replicate human behavior across domains—could enable broad applications in policymaking and social science,” the researchers write.

Study details:

Title: “Generative Agent Simulations of 1,000 People”

Date Submitted: 15 Nov 2024

Authors: Joon Sung Park (Stanford University), Carolyn Q. Zou (Stanford University/Northwestern University), Aaron Shaw (Northwestern University), Benjamin Mako Hill (University of Washington), Carrie Cai (Google DeepMind), Meredith Ringel Morris (Google DeepMind), Robb Willer (Stanford University), Percy Liang (Stanford University), Michael S. Bernstein (Stanford University)

Link: https://arxiv.org/abs/2411.10109

‘Science is a Drag’ fuses drag, science and humor at Oakland’s Chabot Center

The Chabot Space and Science Center in Oakland is holding “Science is a Drag,” where attendees can celebrate the fusion of science, humor and drag on Friday.

Drag superstar VERA is hosting the event at a time when many places, like the California Academy of Sciences, are embracing the science meets drag combination.

VERA said their favorite part of the science drag night is interacting with the audience.

“It’s education and entertainment combined. I learn things, the audience learns things and it’s just a pure good time,” VERA said. “It’s going to be a night packed with drag performances and science experiments, there’s going to be a plasma ball, colored flames, the list goes on.”

VERA said to expect drag numbers interspersed with science experiments and space exploration.

The event, which is open to everyone 21 years of age or older, will be held at the Chabot Space and Science Center on Friday.

Tickets can be purchased at the door or on Chabot’s site at https://chabotspace.org/calendar/science-is-a-drag/. Doors open at 6:45 p.m., and the show runs from 7:30 p.m. to 9 p.m.

‘Science is a Drag’ fuses drag, science and humor at Oakland’s Chabot Center

The Chabot Space and Science Center in Oakland is holding “Science is a Drag,” where attendees can celebrate the fusion of science, humor and drag on Friday.

Drag superstar VERA is hosting the event at a time when many places, like the California Academy of Sciences, are embracing the science meets drag combination.

VERA said their favorite part of the science drag night is interacting with the audience.

“It’s education and entertainment combined. I learn things, the audience learns things and it’s just a pure good time,” VERA said. “It’s going to be a night packed with drag performances and science experiments, there’s going to be a plasma ball, colored flames, the list goes on.”

VERA said to expect drag numbers interspersed with science experiments and space exploration.

The event, which is open to everyone 21 years of age or older, will be held at the Chabot Space and Science Center on Friday.

Tickets can be purchased at the door or on Chabot’s site at https://chabotspace.org/calendar/science-is-a-drag/. Doors open at 6:45 p.m., and the show runs from 7:30 p.m. to 9 p.m.