Sara Giordano, Kennesaw State University

Even if you don’t recall many facts from high school biology, you likely remember the cells required for making babies: egg and sperm. Maybe you can picture a swarm of sperm cells battling each other in a race to be the first to penetrate the egg.

For decades, scientific literature described human conception this way, with the cells mirroring the perceived roles of women and men in society. The egg was thought to be passive while the sperm was active.

Over time, scientists realized that sperm are too weak to penetrate the egg and that the union is more mutual, with the two cells working together. It’s no coincidence that these findings were made in the same era when new cultural ideas of more egalitarian gender roles were taking hold.

Scientist Ludwik Fleck is credited with first describing science as a cultural practice in the 1930s. Since then, understanding has continued to build that scientific knowledge is always consistent with the cultural norms of its time.

Despite these insights, across political differences, people strive for and continue to demand scientific objectivity: the idea that science should be unbiased, rational and separable from cultural values and beliefs.

When I entered my Ph.D. program in neuroscience in 2001, I felt the same way. But reading a book by biologist Anne Fausto-Sterling called “Sexing the Body” set me down a different path. It systematically debunked the idea of scientific objectivity, showing how cultural ideas about sex, gender and sexuality were inseparable from the scientific findings. By the time I earned my Ph.D., I began to look more holistically at my research, integrating the social, historical and political context.

From the questions scientists begin with, to the beliefs of the people who conduct the research, to choices in research design, to interpretation of the final results, cultural ideas constantly inform “the science.” What if an unbiased science is impossible?

Emergence of idea of scientific objectivity

Science grew to be synonymous with objectivity in the Western university system only over the past few hundred years.

In the 15th and 16th centuries, some Europeans gained traction in challenging the religiously ordained royal order. Consolidation of the university system led to shifts from trust in religious leaders interpreting the word of “god,” to trust in “man” making one’s own rational decisions, to trust in scientists interpreting “nature.” The university system became an important site for legitimizing claims through theories and studies.

Previously, people created knowledge about their world, but there were not strict boundaries between what are now called the humanities, such as history, English and philosophy, and the sciences, including biology, chemistry and physics. Over time, as questions arose about how to trust political decisions, people split the disciplines into categories: subjective versus objective. The splitting came with the creation of other binary oppositions, including the closely related emotionality/rationality divide. These categories were not simply seen as opposite, but in a hierarchy with objectivity and rationality as superior.

A closer look shows that these binary systems are arbitrary and self-reinforcing.

Science is a human endeavor

The sciences are fields of study conducted by humans. These people, called scientists, are part of cultural systems just like everyone else. We scientists are part of families and have political viewpoints. We watch the same movies and TV shows and listen to the same music as nonscientists. We read the same newspapers, cheer for the same sports teams and enjoy the same hobbies as others.

All of these obviously “cultural” parts of our lives are going to affect how scientists approach our jobs and what we consider “common sense” that does not get questioned when we do our experiments.

Beyond individual scientists, the kinds of studies that get conducted are based on what questions are deemed relevant or not by dominant societal norms.

For example, in my Ph.D. work in neuroscience, I saw how different assumptions about hierarchy could influence specific experiments and even the entire field. Neuroscience focuses on what is called the central nervous system. The name itself describes a hierarchical model, with one part of the body “in charge” of the rest. Even within the central nervous system, there was a conceptual hierarchy with the brain controlling the spinal cord.

My research looked more at what happened peripherally in muscles, but the predominant model had the brain at the top. The taken-for-granted idea that a system needs a boss mirrors cultural assumptions. But I realized we could have analyzed the system differently and asked different questions. Instead of the brain being at the top, a different model could focus on how the entire system communicates and works together at coordination.

Every experiment also has assumptions baked in – things that are taken for granted, including definitions. Scientific experiments can become self-fulfilling prophecies.

For example, billions of dollars have been spent on trying to delineate sex differences. However, the definition of male and female is almost never stated in these research papers. At the same time, evidence mounts that these binary categories are a modern invention not based on clear physical differences.

But the categories are tested so many times that eventually some differences are discovered without putting these results into a statistical model together. Oftentimes, so-called negative findings that don’t identify a significant difference are not even reported. Sometimes, meta-analyses based on multiple studies that investigated the same question reveal these statistical errors, as in the search for sex-related brain differences. Similar patterns of slippery definitions that end up reinforcing taken-for-granted assumptions happen with race, sexuality and other socially created categories of difference.

Finally, the end results of experiments can be interpreted in many different ways, adding another point where cultural values are injected into the final scientific conclusions.

Settling on science when there’s no objectivity

Vaccines. Abortion. Climate change. Sex categories. Science is at the center of most of today’s hottest political debates. While there is much disagreement, the desire to separate politics and science seems to be shared. On both sides of the political divide, there are accusations that the other side’s scientists cannot be trusted because of political bias.

Consider the recent controversy over the U.S. Centers for Disease Control and Prevention’s vaccine advisory panel. Secretary of Health and Human Services Robert F. Kennedy Jr. fired all members of the Advisory Committee on Immunization Practices, saying they were biased, while some Democratic lawmakers argued back that his move put in place those who would be biased in pushing his vaccine-skeptical agenda.

If removing all bias is impossible, then, how do people create knowledge that can be trusted?

The understanding that all knowledge is created through cultural processes does allow for two or more differing truths to coexist. You see this reality in action around many of today’s most controversial subjects. However, this does not mean you must believe all truths equally – that’s called total cultural relativism. This perspective ignores the need for people to come to decisions together about truth and reality.

Instead, critical scholars offer democratic processes for people to determine which values are important and for what purposes knowledge should be developed. For example, some of my work has focused on expanding a 1970s Dutch model of the science shop, where community groups come to university settings to share their concerns and needs to help determine research agendas. Other researchers have documented other collaborative practices between scientists and marginalized communities or policy changes, including processes for more interdisciplinary or democratic input, or both.

I argue a more accurate view of science is that pure objectivity is impossible. Once you leave the myth of objectivity behind, though, the way forward is not simple. Instead of a belief in an all-knowing science, we are faced with the reality that humans are responsible for what is researched, how it is researched and what conclusions are drawn from such research.

With this knowledge, we have the opportunity to intentionally set societal values that inform scientific investigations. This requires decisions about how people come to agreements about these values. These agreements need not always be universal but instead can be dependent on the context of who and what a given study might affect. While not simple, using these insights, gained over decades of studying science from both within and outside, may force a more honest conversation between political positions.

Sara Giordano, Associate Professor of Interdisciplinary Studies, Kennesaw State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Paul Winters, University of Notre Dame and Amir Jina, University of Chicago

For farmers, every planting decision carries risks, and many of those risks are increasing with climate change. One of the most consequential is weather, which can damage crop yields and livelihoods. A delayed monsoon, for example, can force a rice farmer in South Asia to replant or switch crops altogether, losing both time and income.

Access to reliable, timely weather forecasts can help farmers prepare for the weeks ahead, find the best time to plant or determine how much fertilizer will be needed, resulting in better crop yields and lower costs.

Yet, in many low- and middle-income countries, accurate weather forecasts remain out of reach, limited by the high technology costs and infrastructure demands of traditional forecasting models.

A new wave of AI-powered weather forecasting models has the potential to change that.

By using artificial intelligence, these models can deliver accurate, localized predictions at a fraction of the computational cost of conventional physics-based models. This makes it possible for national meteorological agencies in developing countries to provide farmers with the timely, localized information about changing rainfall patterns that the farmers need.

The challenge is getting this technology where it’s needed.

Why AI forecasting matters now

The physics-based weather prediction models used by major meteorological centers around the world are powerful but costly. They simulate atmospheric physics to forecast weather conditions ahead, but they require expensive computing infrastructure. The cost puts them out of reach for most developing countries.

Moreover, these models have mainly been developed by and optimized for northern countries. They tend to focus on temperate, high-income regions and pay less attention to the tropics, where many low- and middle-income countries are located.

A major shift in weather models began in 2022 as industry and university researchers developed deep learning models that could generate accurate short- and medium-range forecasts for locations around the globe up to two weeks ahead.

These models worked at speeds several orders of magnitude faster than physics-based models, and they could run on laptops instead of supercomputers. Newer models, such as Pangu-Weather and GraphCast, have matched or even outperformed leading physics-based systems for some predictions, such as temperature.

AI-driven models require dramatically less computing power than the traditional systems.

While physics-based systems may need thousands of CPU hours to run a single forecast cycle, modern AI models can do so using a single GPU in minutes once the model has been trained. This is because the intensive part of the AI model training, which learns relationships in the climate from data, can use those learned relationships to produce a forecast without further extensive computation – that’s a major shortcut. In contrast, the physics-based models need to calculate the physics for each variable in each place and time for every forecast produced.

While training these models from physics-based model data does require significant upfront investment, once the AI is trained, the model can generate large ensemble forecasts — sets of multiple forecast runs — at a fraction of the computational cost of physics-based models.

Even the expensive step of training an AI weather model shows considerable computational savings. One study found the early model FourCastNet could be trained in about an hour on a supercomputer. That made its time to presenting a forecast thousands of times faster than state-of-the-art, physics-based models.

The result of all these advances: high-resolution forecasts globally within seconds on a single laptop or desktop computer.

Research is also rapidly advancing to expand the use of AI for forecasts weeks to months ahead, which helps farmers in making planting choices. AI models are already being tested for improving extreme weather prediction, such as for extratropical cyclones and abnormal rainfall.

Tailoring forecasts for real-world decisions

While AI weather models offer impressive technical capabilities, they are not plug-and-play solutions. Their impact depends on how well they are calibrated to local weather, benchmarked against real-world agricultural conditions, and aligned with the actual decisions farmers need to make, such as what and when to plant, or when drought is likely.

To unlock its full potential, AI forecasting must be connected to the people whose decisions it’s meant to guide.

That’s why groups such as AIM for Scale, a collaboration we work with as researchers in public policy and sustainability, are helping governments to develop AI tools that meet real-world needs, including training users and tailoring forecasts to farmers’ needs. International development institutions and the World Meteorological Organization are also working to expand access to AI forecasting models in low- and middle-income countries.

AI forecasts can be tailored to context-specific agricultural needs, such as identifying optimal planting windows, predicting dry spells or planning pest management. Disseminating those forecasts through text messages, radio, extension agents or mobile apps can then help reach farmers who can benefit. This is especially true when the messages themselves are constantly tested and improved to ensure they meet the farmers’ needs.

A recent study in India found that when farmers there received more accurate monsoon forecasts, they made more informed decisions about what and how much to plant – or whether to plant at all – resulting in better investment outcomes and reduced risk.

A new era in climate adaptation

AI weather forecasting has reached a pivotal moment. Tools that were experimental just five years ago are now being integrated into government weather forecasting systems. But technology alone won’t change lives.

With support, low- and middle-income countries can build the capacity to generate, evaluate and act on their own forecasts, providing valuable information to farmers that has long been missing in weather services.

Paul Winters, Professor of Sustainable Development, University of Notre Dame and Amir Jina, Assistant Professor of Public Policy, University of Chicago

This article is republished from The Conversation under a Creative Commons license. Read the original article.