The Conversation

Taxpayers spend 22% more per patient to support Medicare Advantage – the private alternative to Medicare that promised to cost less

Published

10 months ago

November 26, 2024

theconversation.com – Grace McCormack, Postdoctoral researcher of Health Policy and Economics, University of Southern California – 2024-11-26 07:38:00

Grace McCormack, University of Southern California and Erin Duffy, University of Southern California

Medicare Advantage – the commercial alternative to traditional Medicare – is drawing down federal health care funds, costing taxpayers an extra 22% per enrollee to the tune of US$83 billion a year.

Medicare Advantage, also known as Part C, was supposed to save the government money. The competition among private insurance companies, and with traditional Medicare, to manage patient care was meant to give insurance companies an incentive to find efficiencies. Instead, the program’s payment rules overpay insurance companies on the taxpayer’s dime.

We are health care policy experts who study Medicare, including how the structure of the Medicare payment system is, in the case of Medicare Advantage, working against taxpayers.

Medicare beneficiaries choose an insurance plan when they turn 65. Younger people can also become eligible for Medicare due to chronic conditions or disabilities. Beneficiaries have a variety of options, including the traditional Medicare program administered by the U.S. government, Medigap supplements to that program administered by private companies, and all-in-one Medicare Advantage plans administered by private companies.

Commercial Medicare Advantage plans are increasingly popular – over half of Medicare beneficiaries are enrolled in them, and this share continues to grow. People are attracted to these plans for their extra benefits and out-of-pocket spending limits. But due to a loophole in most states, enrolling in or switching to Medicare Advantage is effectively a one-way street. The Senate Finance Committee has also found that some plans have used deceptive, aggressive and potentially harmful sales and marketing tactics to increase enrollment.

Baked into the plan

Researchers have found that the overpayment to Medicare Advantage companies, which has grown over time, was, intentionally or not, baked into the Medicare Advantage payment system. Medicare Advantage plans are paid more for enrolling people who seem sicker, because these people typically use more care and so would be more expensive to cover in traditional Medicare.

However, differences in how people’s illnesses are recorded by Medicare Advantage plans causes enrollees to seem sicker and costlier on paper than they are in real life. This issue, alongside other adjustments to payments, leads to overpayment with taxpayer dollars to insurance companies.

Some of this extra money is spent to lower cost sharing, lower prescription drug premiums and increase supplemental benefits like vision and dental care. Though Medicare Advantage enrollees may like these benefits, funding them this way is expensive. For every extra dollar that taxpayers pay to Medicare Advantage companies, only roughly 50 to 60 cents goes to beneficiaries in the form of lower premiums or extra benefits.

As Medicare Advantage becomes increasingly expensive, the Medicare program continues to face funding challenges.

In our view, in order for Medicare to survive long term, Medicare Advantage reform is needed. The way the government pays the private insurers who administer Medicare Advantage plans, which may seem like a black box, is key to why the government overpays Medicare Advantage plans relative to traditional Medicare.

Paying Medicare Advantage

Private plans have been a part of the Medicare system since 1966 and have been paid through several different systems. They garnered only a very small share of enrollment until 2006.

The current Medicare Advantage payment system, implemented in 2006 and heavily reformed by the Affordable Care Act in 2010, had two policy goals. It was designed to encourage private plans to offer the same or better coverage than traditional Medicare at equal or lesser cost. And, to make sure beneficiaries would have multiple Medicare Advantage plans to choose from, the system was also designed to be profitable enough for insurers to entice them to offer multiple plans throughout the country.

To accomplish this, Medicare established benchmark estimates for each county. This benchmark calculation begins with an estimate of what the government-administered traditional Medicare plan would spend on the average county resident. This value is adjusted based on several factors, including enrollee location and plan quality ratings, to give each plan its own benchmark.

Medicare Advantage plans then submit bids, or estimates, of what they expect their plans to spend on the average county enrollee. If a plan’s spending estimate is above the benchmark, enrollees pay the difference as a Part C premium.

Most plans’ spending estimates are below the benchmark, however, meaning they project that the plans will provide coverage that is equivalent to traditional Medicare at a lower cost than the benchmark. These plans don’t charge patients a Part C premium. Instead, they receive a portion of the difference between their spending estimate and the benchmark as a rebate that they are supposed to pass on to their enrollees as extras, like reductions in cost-sharing, lower prescription drug premiums and supplemental benefits.

Finally, in a process known as risk adjustment, Medicare payments to Medicare Advantage health plans are adjusted based on the health of their enrollees. The plans are paid more for enrollees who seem sicker.

Two sets of stacked boxes sit below a vertical bar labeled Risk-Adjusted Benchmark. A vertical line bisecting the boxes is labelled what Medicare would actually spend on an enrollee in traditional Medicare — The government pays Medicare Advantage plans based on Medicare’s cost estimates for a given county. The benchmark is an estimate from the Centers for Medicare & Medicaid Services of what it would cost to cover an average county enrollee in traditional Medicare, plus adjustments including quartile payments and quality bonuses. The risk-adjusted benchmark also takes into consideration an enrollee’s health.

Samantha Randall at USC, CC BY-ND

Theory versus reality

In theory, this payment system should save the Medicare system money because the risk-adjusted benchmark that Medicare estimates for each plan should run, on average, equal to what Medicare would actually spend on a plan’s enrollees if they had enrolled in traditional Medicare instead.

In reality, the risk-adjusted benchmark estimates are far above traditional Medicare costs. This causes Medicare – really, taxpayers – to spend more for each person who is enrolled in Medicare Advantage than if that person had enrolled in traditional Medicare.

Why are payment estimates so high? There are two main culprits: benchmark modifications designed to encourage Medicare Advantage plan availability, and risk adjustments that overestimate how sick Medicare Advantage enrollees are.

Two sets of stacked boxes with dotted arrows on the left side of each labeled Medicare Advantage Plan Bid sit below vertical bars labeled Benchmark and Risk-Adjusted Benchmark. — High risk-adjusted benchmarks lead to overpayments from the government to the private companies that administer Medicare Advantage plans.

Samantha Randall at USC, CC BY-ND

Benchmark modifications

Since the current Medicare Advantage payment system started in 2006, policymaker modifications have made Medicare’s benchmark estimates less tied to what the plan spends on each enrollee.

In 2012, as part of the Affordable Care Act, Medicare Advantage benchmark estimates received another layer: “quartile adjustments.” These made the benchmark estimates, and therefore payments to Medicare Advantage companies, higher in areas with low traditional Medicare spending and lower in areas with high traditional Medicare spending. This benchmark adjustment was meant to encourage more equitable access to Medicare Advantage options.

In that same year, Medicare Advantage plans started receiving “quality bonus payments” with plans that have higher “star ratings” based on quality factors such as enrollee health outcomes and care for chronic conditions receiving higher bonuses.

However, research shows that ratings have not necessarily improved quality and may have exacerbated racial inequality.

Even before fully taking into account risk adjustment, recent estimates peg the benchmarks, on average, as 8% higher than average traditional Medicare spending. This means that a Medicare Advantage plan’s spending estimate could be below the benchmark and the plan would still get paid more for its enrollees than it would have cost the government to cover those same enrollees in traditional Medicare.

Overestimating enrollee sickness

The second major source of overpayment is health risk adjustment, which tends to overestimate how sick Medicare Advantage enrollees are.

Each year, Medicare studies traditional Medicare diagnoses, such as diabetes, depression and arthritis, to understand which have higher treatment costs. Medicare uses this information to adjust its payments for Medicare Advantage plans. Payments are lowered for plans with lower predicted costs based on diagnoses and raised for plans with higher predicted costs. This process is known as risk adjustment.

But there is a critical bias baked into risk adjustment. Medicare Advantage companies know that they’re paid more if their enrollees seem more sick, so they diligently make sure each enrollee has as many diagnoses recorded as possible.

This can include legal activities like reviewing enrollee charts to ensure that diagnoses are recorded accurately. It can also occasionally entail outright fraud, where charts are “upcoded” to include diagnoses that patients don’t actually have.

In traditional Medicare, most providers – the exception being Accountable Care Organizations – are not paid more for recording diagnoses. This difference means that the same beneficiary is likely to have fewer recorded diagnoses if they are enrolled in traditional Medicare rather than a private insurer’s Medicare Advantage plan. Policy experts refer to this phenomenon as a difference in “coding intensity” between Medicare Advantage and traditional Medicare.

Human figure with arrows to two boxes. Left box has two plus symbols labelled recorded diagnoses and one dollar sign. Right box has five symbols and three dollar signs. — The same person is likely to be documented with more illnesses if they enroll in Medicare Advantage rather than traditional Medicare – and cost taxpayers more money.

Samantha Randall at USC, CC BY-ND

In addition, Medicare Advantage plans often try to recruit beneficiaries whose health care costs will be lower than their diagnoses would predict, such as someone with a very mild form of arthritis. This is known as “favorable selection.”

The differences in coding and favorable selection make beneficiaries look sicker when they enroll in Medicare Advantage instead of traditional Medicare. This makes cost estimates higher than they should be. Research shows that this mismatch – and resulting overpayment – is likely only going to get worse as Medicare Advantage grows.

Where the money goes

Some of the excess payments to Medicare Advantage are returned to enrollees through extra benefits, funded by rebates. Extra benefits include cost-sharing reductions for medical care and prescription drugs, lower Part B and D premiums, and extra “supplemental benefits” like hearing aids and dental care that traditional Medicare doesn’t cover.

Medicare Advantage enrollees may enjoy these benefits, which could be considered a reward for enrolling in Medicare Advantage, which, unlike traditional Medicare, has prior authorization requirements and limited provider networks.

However, according to some policy experts, the current means of funding these extra benefits is unnecessarily expensive and inequitable.

It also makes it difficult for traditional Medicare to compete with Medicare Advantage.

Traditional Medicare, which tends to cost the Medicare program less per enrollee, is only allowed to provide the standard Medicare benefits package. If its enrollees want dental coverage or hearing aids, they have to purchase these separately, alongside a Part D plan for prescription drugs and a Medigap plan to lower their deductibles and co-payments.

Page from text document — Medicare Advantage plans offer extras, but at a high cost to the Medicare system – and taxpayers. Only 50-60 cents of a dollar spent is returned to enrollees as decreased costs or increased benefits.

AP Photo/Pablo Martinez Monsivais

The system sets up Medicare Advantage plans to not only be overpaid but also be increasingly popular, all on the taxpayers’ dime. Plans heavily advertise to prospective enrollees who, once enrolled in Medicare Advantage, will likely have difficulty switching into traditional Medicare, even if they decide the extra benefits are not worth the prior authorization hassles and the limited provider networks. In contrast, traditional Medicare typically does not engage in as much direct advertising. The federal government only accounts for 7% of Medicare-related ads.

At the same time, some people who need more health care and are having trouble getting it through their Medicare Advantage plan – and are able to switch back to traditional Medicare – are doing so, according to an investigation by The Wall Street Journal. This leaves taxpayers to pick up care for these patients just as their needs rise.

Where do we go from here?

Many researchers have proposed ways to reduce excess government spending on Medicare Advantage, including expanding risk adjustment audits, reducing or eliminating quality bonus payments or using more data to improve benchmark estimates of enrollee costs. Others have proposed even more fundamental reforms to the Medicare Advantage payment system, including changing the basis of plan payments so that Medicare Advantage plans will compete more with each other.

Reducing payments to plans may have to be traded off with reductions in plan benefits, though projections suggest the reductions would be modest.

There is a long-running debate over what type of coverage should be required under both traditional Medicare and Medicare Advantage. Recently, policy experts have advocated for introducing an out-of-pocket maximum to traditional Medicare. There have also been multiple unsuccessful efforts to make dental, vision, and hearing services part of the standard Medicare benefits package.

Although all older people require regular dental care and many of them require hearing aids, providing these benefits to everyone enrolled in traditional Medicare would not be cheap. One approach to providing these important benefits without significantly raising costs is to make these benefits means-tested. This would allow people with lower incomes to purchase them at a lower price than higher-income people. However, means-testing in Medicare can be controversial.

There is also debate over how much Medicare Advantage plans should be allowed to vary. The average Medicare beneficiary has over 40 Medicare Advantage plans to choose from, making it overwhelming to compare plans. For instance, right now, the average person eligible for Medicare would have to sift through the fine print of dozens of different plans to compare important factors, such as out-of-pocket maximums for medical care, coverage for dental cleanings, cost-sharing for inpatient stays, and provider networks.

Although millions of people are in suboptimal plans, 70% of people don’t even compare plans, let alone switch plans, during the annual enrollment period at the end of the year, likely because the process of comparing plans and switching is difficult, especially for older Americans.

MedPAC, a congressional advising committee, suggests that limiting variation in certain important benefits, like out-of-pocket maximums and dental, vision and hearing benefits, could help the plan selection process work better, while still allowing for flexibility in other benefits. The challenge is figuring out how to standardize without unduly reducing consumers’ options.

The Medicare Advantage program enrolls over half of Medicare beneficiaries. However, the $83-billion-per-year overpayment of plans, which amounts to more than 8% of Medicare’s total budget, is unsustainable. We believe the Medicare Advantage payment system needs a broad reform that aligns insurers’ incentives with the needs of Medicare beneficiaries and American taxpayers.

This article is part of an occasional series examining the U.S. Medicare system.

Past articles in the series:

Medicare vs. Medicare Advantage: sales pitches are often from biased sources, the choices can be overwhelming and impartial help is not equally available to all

Grace McCormack, Postdoctoral researcher of Health Policy and Economics, University of Southern California and Erin Duffy, Research Scientist and Director of Research Training in Health Policy and Economics, University of Southern California

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

The post Taxpayers spend 22% more per patient to support Medicare Advantage – the private alternative to Medicare that promised to cost less appeared first on theconversation.com

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The Conversation

How is paint made?

Published

1 week ago

September 8, 2025

Staff Editor

theconversation.com – Dawn Rogala, Paintings Conservator and Program Manager, Smithsonian Institution – 2025-09-08 07:07:00

Protective paint sprayed onto a steel plate in a factory will have a different recipe than paint used in an art class.
gilaxia/E+ via Getty Images

Dawn Rogala, Smithsonian Institution and Gwénaëlle Kavich, Smithsonian Institution

Curious Kids is a series for children of all ages. If you have a question you’d like an expert to answer, send it to curiouskidsus@theconversation.com.

How is paint made? – Atharva, age 11, Bengaluru, India

Did you ever mix dirt and water when you were playing outside? You made a paint. Did you draw shapes on the ground with your muddy hands? You made a painting.

Paint is made by combining a colorful substance – a pigment – with another material that binds the color together and helps spread that color onto surfaces such as paper, fabric or wood. Pigments can be found everywhere – in rocks and minerals, plants or insects. Some colors are made by scientists in laboratories.

Long ago, artists made their own paints by mixing pigments with natural materials such as water, oil or egg yolk to hold the colors together in a paste. Artists today can still make their own paints, or they can order them from factories that mix, package and ship paint all over the world. Paint companies use large, industrial machines to grind pigments and binders together; these commercial paints include synthetic materials and preservatives to control the paint’s behavior and to help paint last longer in tubes or cans.

Paints and coatings do many jobs beyond just coloring paper in an artist’s studio. They are also used as protective coatings to shield houses and cars from the sun or the cold, or as a barrier between boats and the water that surrounds their wood, metal or plastic parts. Where and how a paint will be used influence how it’s made and with what ingredients.

an open box of watercolor paints with splatters of color on the case — Watercolor sets like this one used by artist Alma Thomas can be found in art classrooms around the world.
Anacostia Community Museum, Smithsonian Institution, Gift of David Driskell, CC BY

Choosing the right materials

A lot of questions need to be answered before materials are chosen for a paint.

Who will use the paint? An artist, a house painter, an armadillo, a robot at an assembly plant?
Why is the paint being used? For museum paintings and sculptures? In designs for furniture or mailboxes?
How will the paint be applied? By brush, by spray, or some other way?
Where and when will the paint be used? Does it need to dry quickly or slowly? Will the painted surface get really cold or hot? Is the paint safe for kids to use at home or school?
What should the paint look like? Should the dried paint be shiny or matte? Should the surface be lumpy, or should it flatten and level out? Should the colors be bright or dull? Should the paint layers be opaque, transparent or almost clear? Does the paint need to hold up against scuffs and stains?

There are many different companies that design and make the wide range of paints used around the world for all these various applications. Experts at each manufacturer understand their special type of paint, how the paint materials are measured and mixed, and the best ways to store and apply the paint. A single factory can make tens of thousands of gallons of paint each day, and paint companies produce millions of tubes of paint every year.

two boards with various colors of paint dried on them along with multiple paint brushes — Artist Thomas Moran’s palettes and brushes illustrate the way an artist mixes different paints to find just the desired qualities.
Smithsonian American Art Museum, Bequest of Miss Ruth B. Moran

Using paint to learn about the past

We work at the Smithsonian’s Museum Conservation Institute, where we study and conserve the diverse collection of painted objects at the Smithsonian – from planes and spacecraft to portraits of presidents and maps covered in abstract swirls of color. Bright coatings are part of everything from the painted clothing and cultural items of Native peoples to the pots and pans used by chef Julia Child.

Art conservators and conservation scientists like us work together to study and preserve cultural heritage such as paintings and painted objects. Studying paint helps us learn about the past and protect this history for future generations.

The paint colors used on large, traditional Indian paintings called “pichwai,” for example, include pigments gathered from around the world. They can reveal information about ancient manufacturing and how communities that lived far apart exchanged goods and knowledge.

There are many techniques to investigate artwork, from looking at small pieces of paint under a microscope to using more complicated equipment to analyze materials exposed to different types of energy. For example, we can use X-ray, infrared or ultraviolet imaging to identify different pigments in a painting.

three side by side images of the same painting, but one looks very dark, one is colorful, and one is grey and white — Conservation scientists will image the same work of art, such as this Indian pichwai, using ultraviolet fluorescence (left), visible light (middle) and infrared light (right).
National Museum of Asian Art, Smithsonian Institution, Gift of Karl B. Mann, S1992.28, Department of Conservation and Scientific Research, Orthomosaics and UV Fluorescence

Research on an Alaskan Tlingit crest hat made in the 1800s looked at the molecules in paint binders, combined with 3D scanning, to help clan members replicate the hat for ceremonial use.

Unusual uses bring conservation challenges

Artists use all sorts of materials in their artwork that were designed for other purposes. Some 19th- and early 20th-century sculptures were painted with laundry bluing – a material that used blue pigment to brighten clothes during washing. In the 1950s, artists started using thin, quick-drying house paint in their paintings.

When paints are used in a way that was not part of their design, strange things can happen. Paints made to be applied in thin layers but instead are used in thick layers can wrinkle and pucker as they dry. Paints designed to stick to rough wood can curl or lift away from slick surfaces. The colors and ingredients in paint can also fade or darken over time. Some artists want these different effects in their artwork; some artists are surprised when paints don’t behave the way they expected.

Art conservators and conservation scientists use information about artists and their paints to understand why artworks are faded, broken or acting in surprising ways, and they use that knowledge to slow or stop the damage. We can even clean some kinds of damage with lasers.

The more we know about paint, the more we learn about the past lives of painted objects and how to keep those objects around for a long, long time.

Hello, curious kids! Do you have a question you’d like an expert to answer? Ask an adult to send your question to CuriousKidsUS@theconversation.com. Please tell us your name, age and the city where you live.

And since curiosity has no age limit – adults, let us know what you’re wondering, too. We won’t be able to answer every question, but we will do our best.

Dawn Rogala, Paintings Conservator and Program Manager, Smithsonian Institution and Gwénaëlle Kavich, Conservation Scientist, Smithsonian Institution

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

The post How is paint made? appeared first on theconversation.com

Note: The following A.I. based commentary is not part of the original article, reproduced above, but is offered in the hopes that it will promote greater media literacy and critical thinking, by making any potential bias more visible to the reader –Staff Editor.

Political Bias Rating: Centrist

The content is an educational piece explaining how paint is made and used, with no evident political agenda or bias. It focuses on scientific, historical, and artistic aspects in a neutral and informative manner, suitable for a general audience including children. There is no indication of leaning toward any political ideology or partisan perspective.

The Conversation

Scientific objectivity is a myth – cultural values and beliefs always influence science and the people who do it

Published

2 weeks ago

September 4, 2025

Staff Editor

theconversation.com – Sara Giordano, Associate Professor of Interdisciplinary Studies, Kennesaw State University – 2025-09-04 07:53:00

People are at the heart of the scientific enterprise.
Matteo Farinella, CC BY-NC

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.

The opening credits of the 1989 movie ‘Look Who’s Talking’ animated this popular narrative, with speaking sperm rushing toward the nonverbal egg to be the first to fertilize it.

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.

RFK Jr, Donald Trump and Dr. Oz seated at a table with flags behind them — It can be easier to spot built-in bias in scientific perspectives that conflict with your own values.
Jim Watson/AFP via Getty Images

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.

Alternative views on the relationship between science and culture.
Sara Giordano

Science is a human endeavor

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

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

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

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

The post Scientific objectivity is a myth – cultural values and beliefs always influence science and the people who do it appeared first on theconversation.com

Political Bias Rating: Center-Left

The content emphasizes the influence of cultural and social values on scientific research, challenging the notion of pure scientific objectivity. It highlights themes such as gender equality, critiques of traditional hierarchies, and the social construction of categories like sex and race, which are commonly associated with progressive or center-left perspectives. While it acknowledges political divides and calls for democratic, inclusive approaches to science, the overall framing aligns with a center-left viewpoint that values social context and equity in knowledge production.

The Conversation

AI is transforming weather forecasting − and that could be a game changer for farmers around the world

Published

2 weeks ago

September 3, 2025

Staff Editor

theconversation.com – Paul Winters, Professor of Sustainable Development, University of Notre Dame – 2025-09-03 07:30:00

Weather forecasts help farmers figure out when to plant, where to use fertilizer and much more.
Maitreya Shah/Studio India

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.

A farmer in a field holds a dried out corn stalk. — A farmer holds dried-up maize stalks in his field in Zimbabwe on March 22, 2024. A drought had caused widespread water shortages and crop failures.
AP Photo/Tsvangirayi Mukwazhi

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.

A woman in a red sari tosses pellets into a rice field. — A farmer distributes fertilizer in India.
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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.

A man sells grain in Dawanau International Market in Kano, Nigeria on July 14, 2023. — Many low-income countries in Africa face harsh effects from climate change, from severe droughts to unpredictable rain and flooding. The shocks worsen conflict and upend livelihoods.
AP Photo/Sunday Alamba

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.

A farmer holds dried-up maize stalks in his field in Zimbabwe on March 22, 2024. A drought had caused widespread water shortages and crop failures.
AP Photo/Tsvangirayi Mukwazhi

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

Why AI forecasting matters now

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

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

Tailoring forecasts for real-world decisions

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

A new era in climate adaptation

The post AI is transforming weather forecasting − and that could be a game changer for farmers around the world appeared first on theconversation.com

Political Bias Rating: Centrist

The content presents a factual and balanced discussion on the use of AI in weather forecasting to aid farmers, particularly in low- and middle-income countries. It emphasizes technological innovation, international collaboration, and practical benefits without promoting a specific political ideology. The focus on climate change and development is handled in a neutral, solution-oriented manner, reflecting a centrist perspective that values science and global cooperation.