The shift presents researchers with an important question beyond merely how to use AI: who defines the limits, objectives, and rules governing its use? This is a question that encompasses technology, ethics, institutional governance, and public policy, and that was discussed in a recent Science Arena report..

AI as a driver of productivity and methodological change

Generative AI and data analytics tools are already part of everyday scientific practice. They assist with tasks that might previously have required weeks of human work, such as structuring texts, organizing reference databases, or identifying patterns in large volumes of data, helping to speed up processes and expand exploratory capacities.

However, this technological integration goes beyond efficiency alone—it is changing the methods of knowledge production. 

In fields such as bioinformatics, genomics, and big data analysis, AI is almost indispensable, said veterinarian Rebeca Scalco, a PhD candidate in digital pathology and bioinformatics at the University of Bern, Switzerland, in an interview with Science Arena.

“The AI tool SciSpace, for example, helps explain complex articles, but it is still the researcher who interprets or critically analyzes them,” she says.

Governance of scientific AI: an institutional necessity

While many educational efforts and ethical recommendations focus on individual best practices, such as declaring or recording when and how AI is used, the broader discussion revolves around governance and institutional policy. 

In recent years, several universities and research centers worldwide, including Brazilian institutions such as the University of Campinas (UNICAMP) and the Federal University of Bahia (UFBA), have begun creating formal guidelines on the use of AI, with a focus on:

• Transparency and accountability, requiring researchers to document and justify the role of AI in their results.
• Data protection and confidentiality, preventing the exposure of sensitive information on platforms outside the institution’s oversight.
• Preservation of the researcher’s authorship and intellectual agency, ensuring that AI does not compromise the originality and integrity of scientific work.

These guidelines are not just recommendations. Many universities are discussing how to incorporate AI into their internal research regulations, with specialized committees and official documents to guide students, faculty, and research groups. 

AI, public policy, and global regulation

On an international level, the United Nations Educational, Scientific and Cultural Organization (UNESCO) has issued global recommendations encouraging the development of ethical frameworks for AI in science and society, emphasizing that the technology needs to be used in line with human rights, data protection, and shared ethical values. 

In some regions, such as the European Union, specific legislation is already being created to define risks, transparency requirements, and oversight mechanisms. 

In other countries, draft legislation addresses governance, civil liability, and ethical principles that can guide the use of AI in scientific research and technological innovation. 

Challenges and conflicts: autonomy, corporate power, and scientific sovereignty

The debate about AI governance is not neutral, Scalco warns. “Academia and civil society need to be involved in regulation, ensuring that the public interest remains central, especially in fields like health,” she says.

Most advanced AI tools and platforms are controlled by private corporations (the so-called big tech companies) with global economic interests, sparking concerns about who controls the technology that is now influencing the production of scientific knowledge. 

This conflict raises additional questions:

• Should science relinquish sensitive, long-term data to external platforms? 
• What criteria should academic institutions adopt to protect their researchers and research subjects? 
• How can the use of AI be balanced with preservation of the critical thinking, creativity, and methodological rigor that characterize science? 

The role of researchers in formulating governance

The ethical governance of AI in science is not solely the responsibility of committees or regulatory bodies. 

“AI tools should be treated like ‘good interns,’ eager to learn. But just as a doctoral student would never submit an intern’s unreviewed text for publication, nor should they submit work done with AI without proper verification,” says Scalco. In this sense, AI should be seen not merely as a tool, but as an agent of structural change, the ethical and political implications of which need to be understood, debated, and incorporated into institutional systems and scientific training.

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Since then, much has changed. Most importantly, machines have evolved from simply executing commands to learning from them, storing and even improving knowledge. Another major leap came with the increase in access to AI, which is now in the hands of the general public and a part of everyday tasks.

“I see the democratization of AI as a powerful shift—the tool is now in the hands of the users for the first time,” says veterinarian Rebeca Scalco, who is studying a PhD in digital pathology and bioinformatics at the University of Bern, Switzerland.

Specializing in areas such as predictive modeling, bioinformatics applied to animal health, and new genetic sequencing technologies, Scalco uses machine learning and data analysis in her research.

More recently, she decided to share what is known about AI in the form of products and services for scientists.

This includes two ebooks offering tips and solutions to researchers looking to make use of AI technologies and automate writing and data analysis processes. Scalco has also begun offering mentoring and courses on the subject.

With a bachelor’s degree and a master’s from the Federal University of Pelotas (UFPel) in Rio Grande do Sul, the veterinarian first encountered advanced AI tools while doing a research fellowship at the University of California.

The project was focused on human medicine, and her role at the neuropathology lab of the institution’s Alzheimer’s Disease Research Center was to monitor brain dissections and explain causes of death.

Encountering digital pathology

It was through this role that she encountered digital pathology, a technique in which histological slides containing tissue samples are digitized and examined as high-resolution images, eliminating the need for a microscope.

“With digitalization, it is possible to apply algorithms without affecting the physical material,” explains Scalco. “Since pathology is essentially based on patterns, it is a perfect fit for machine learning.” At around the same time, ChatGPT emerged.

How to use artificial intelligence ethically in your research

Tools for every step of the process

Scalco uses several AI technologies in her academic routine. ChatGPT, she says, has become indispensable for structuring texts, reviewing clarity and fluency in English, generating code snippets (in Python and R), and preparing lesson plans and presentations.

For literature review, she uses SciSpace, which makes it easier to read complex articles, and Anara, which synthesizes information and generates explanations based on original sources.

She also uses ResearchRabbit and Inciteful to visualize citation networks and identify research trends.

“Sometimes, I use Google Notebook LM to organize notes and summaries. Each tool serves a different purpose, but ChatGPT, combined with a good literature support platform, is the basis of my day-to-day work,” she says.

Ethics and limits: AI as an assistant 

The use of AI in academia is now commonplace for thousands of researchers. The topic, however, still divides opinion. While some welcome it with enthusiasm, others fear it trivializes the scientific process. Scalco recognizes both sides and advocates for a balanced position.

“These tools should be treated like good interns, eager to learn. But just as a doctoral student would never submit an intern’s unreviewed text for publication, nor should they submit work done with AI without proper verification,” she warns.

Recent cases illustrate misuse:

• The journal Neurosurgical Review, published by Springer Nature, had to retract dozens of articles that had been generated by AI without declaring it.
• A book from the same publisher about machine learning was also withdrawn after the discovery of false references—so-called AI “hallucinations.”

The answer, Scalco says, is relatively simple:

According to the researcher, some Brazilian universities, such as the University of Campinas (UNICAMP) and the Federal University of Bahia (UFBA), have made progress on this topic. “These institutions have published clear guidelines on the ethical use of AI in research and academic work.”

AI as a science accelerator

When used methodically, AI can be a powerful ally in research. Scalco highlights applications such as drafting texts, translating documents, screening articles, reviewing consistency, summarizing content, and generating initial insights.

“The tool SciSpace, for example, helps explain complex articles, but it is still the researcher who interprets or critically analyzes them,” she points out.

In fields such as bioinformatics, genomics, and big data, AI is practically indispensable.

“This would be impossible with classical statistics. AI does not replace researchers, but it does expand our ability to see ‘hidden’ relationships behind millions of data points,” she says.

Regulatory and geopolitical issues

Despite the progress, Scalco acknowledges that most AI platforms are controlled by large corporations with their own economic interests and limited regulation, especially in Brazil.

“When sharing sensitive data, we must ask ourselves: who is behind this technology? What are the risks? Is it really worth handing over years of research to a private company without any guarantees?” she asks.

The European Union has taken the lead with the 2024 AI Act, which establishes risk categories, transparency requirements, and human oversight.

The United Nations Educational, Scientific, and Cultural Organization (UNESCO) has issued a global recommendation on the ethical use of AI, adopted by almost 200 countries.

In Brazil, Bill 2338/2023 (known as the AI Bill), which is currently being debated in the senate, addresses governance, civil liability, and fundamental rights.

“These rules are not neutral. They are the result of disputes and lobbies,” says Scalco.

“That is why academia and civil society need to be involved in regulation, ensuring that the public interest remains central, especially in fields like health,” she argues.

For Scalco, the message is clear: AI works for the researcher and not the other way around. “People are delegating too much. AI is powerful, but it must not nullify us. Never give up on your critical thinking, your perspective, and your power to reflect,” she urges.

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The survey heard from 505 journalists from 82 countries (including Brazil) between March and July 2022. Participants answered a questionnaire about professional ethics and topics such as neutrality, scientific uncertainties, fraud, retractions, and the embargo system, which allows for early access to papers that have not yet been published.

Uncertainties and neutrality

For 74% of respondents, scientific discoveries should be treated as temporary and subject to future refutation. One Brazilian journalist highlighted:

“There is no official truth in science; the search for any conceivable approach to truth demands a context of free inquiry.”

Regarding coverage of new vaccines, the majority of journalists from Latin America (59%), Europe and Russia (67%), and North America (89%) said that reporting should prioritize sources that support vaccination as a preventive measure.

“Vaccination for disease prevention is so effective that to cast doubt on that overall effectiveness is irresponsible,” said a journalist from Australia. “That said, science journalists should report responsibly on individual vaccines, including to address the balance of efficacy versus side effects and risks.”

In contrast, journalists from sub-Saharan Africa (80%) and North Africa and the Middle East (64%) said that they seek a greater balance, including sources that question the relationship between climate change and human activities.

Fraud and retractions

When asked about retractions (when scientific articles are withdrawn after publication due to fraud or errors), 65% of journalists said they would report that an article they covered had been retracted, either by updating their original report or by adding an editorial note.

However, 21% stated that they would only do so in cases of serious fraud.

Furthermore, 80% of respondents said that journalists should cover cases of scientists accused of fraud, especially if they are later found innocent.

“Whoever reported the fraud in the first place should follow it up and inform their audience about the development,” said a journalist from Spain.

“Journalism is not Twitter [now X], where it seems that there is no responsibility for what is said. The journalist must be responsible for what he says, be honest in order to be fair to those who are being judged,” emphasized the Spanish journalist.

See the main highlights of the survey below (click on the headings below to learn more):

Profile of the survey participants

Professional practices

Ethics in journalism

Coverage of controversial topics

Challenges and ethical issues

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Inspired by the technology industry, which recruits programmers to test the vulnerabilities of their products, ERROR seeks to check academic articles that have been cited at least 30 times per year since they were published.

The project is offering reviewers a payment of 1,000 Swiss francs (about US$1,100) per article, with a bonus for every error they find. For a review to be conducted, there has to be an agreement with the authors, who are also remunerated for accepting and paid bonuses if errors are not identified.

In an interview with Science Arena, Malte Elson, a psychologist from the University of Bern and head of the project, talks about ERROR’s first steps, its reliability, and how authors and reviewers are compensated.

Science Arena – What motivated you to create the ERROR project and what are its objectives?

Malte Elson – One of the purposes of science is to discover truths—about the universe, human beings, chemical elements, or whatever you want. Given this, it is strange that science has no formal mechanism for detecting and correcting errors after publication. Traditional peer review is not designed explicitly to detect errors; reviewers often do not have access to more detail or research material (such as raw data, code, etc.) than any other reader. And even if they did, it’s quite conceivable that they often do not have time to delve deeper.

Other professions, particularly in the technology industry, have used ‘bug bounties’ for decades to reward freelancers who find and report critical flaws in products such as code or hardware. It is cheaper to pay for error detection than not to do it.

ERROR was launched in February 2024. How has it been received?

We have about 15 articles under review, of which one has completed the review cycle. The reviewer found a few minor errors. These errors did not materially affect the conclusions of the article, but the author, Jan Wessel, has contacted the journal about a possible correction, which is very commendable.

Overall, the reception has been very positive, but of course we still do not know for sure if the community will accept a program like ERROR.

How many reviewers are initially part of the project and what kind of profile do they have?

We have funding to do at least 100 reviews over four years. We will start in the fields of psychology, social science, and broader behavioral sciences. Then we will move on to medicine and possibly other areas.

We will need reviewers with very diverse experiences and skills, but who share some characteristics. The people who are most successful at detecting errors are probably naturally curious. They have to approach the material they are analyzing—such as procedures, computer code, or data—in a unique way, thinking about what could likely have gone wrong.

Many will also have the technical skills to read someone else’s code or understand statistical analyses that are not always meticulously documented.

What are the criteria for selecting articles?

We will only review articles that have reached a certain level of “importance.” The logic is to make the most efficient use of our resources. Of course, no scientific article should contain errors, but our reasoning is that errors in important papers that get cited many times can have consequences on much of the literature, so their discovery has higher priority.

Defining or measuring importance, however, is not a simple task. In this initial phase of the ERROR project, we are using a very simple rule: the articles must have been cited at least 30 times per year since they were published.

Could financial incentives for authors who agree to have their articles reviewed constitute a scientific bias?

Any new incentive creates opportunities for bias. We are well aware of this, but we believe that the benefits of encouraging error detection outweigh the risks. With ERROR, we are trying to counter potential biases by implementing a transparent system of checks and balances.

Most importantly, it is not the reviewer who decides whether something constitutes an error or not. Instead, the reviewers prepare a report in which they describe their concerns. The authors are given the opportunity to respond, and then a recommender (similar to the role of an editor in a standard peer review by a journal) evaluates the arguments of both parties to decide what should happen.

The reviews and author responses are published together on our website, including any data or code generated as part of the review.

Then, if necessary, we can make adjustments. This is a dry run, not a global implementation of a proven system.

How do you expect authors to react to having their articles examined by the project’s reviewers?

I think it’s safe to assume that the types of authors who agree to participate in ERROR are different from the types of authors who do not. They may have certain personality traits or perhaps they already have greater job stability. Maybe they work in a lab where openness and constructive criticism are already commonplace and encouraged.

Furthermore, authors who suspect that their work would not stand up to scrutiny by an expert reviewer would likely be less inclined to participate. The ERROR project is not intended to provide an estimate of the “true” error rate in scientific articles.

Rather, it was designed to test whether an error detection model would work in principle and whether it is worth the cost.

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Immunologist Helder Nakaya, a researcher at Hospital Israelita Albert Einstein and professor at the University of São Paulo (USP), was faced with this challenge when invited onto a board to assess 26 candidates.

“To make life easier I developed a code capable of doing the work automatically,” says Nakaya, who uses programming language in his research.

To use the Conflitulus Lattes program, developed by Nakaya and his team, the user must first download the résumés of all candidates and examiners from the Lattes platform, clicking on the XML button to download the compressed file in .zip format.

Compacted or other files may be uploaded onto Conflitulus Lattes after registering.Compacted or other files may be uploaded onto Conflitulus Lattes after registering.

The tool cross-references résumés and conducts initial screening, listing all coauthorships and projects in common between each examiner and candidate, and provides a graph demonstrating possible conflicts.

For a board of 3 examiners and 20 candidates, the full process takes around 10 minutes.

Nakaya developed the program at the Computational Systems Biology Laboratory (CSBL) at the University of São Paulo’s Innovation Center (INOVA USP). Work on the application was supported by Hospital Israelita Albert Einstein, and the São Paulo Research Foundation (FAPESP). “My laboratory develops ‘friendly’ online computer tools, so I decided to transform that into something useful for people,” says Nakaya, the head of CSBL.

Data cross-referencing

“The system uses genome algorithms that convert information such as name and date of birth into DNA sequences, using the letters A, T, C, and G,” explains bioinformatics specialist José Deney Alves de Araújo, CEO of Brazilian startup D2DNA, which specializes in healthcare data analysis, and a former doctoral student of Nakaya who developed the data cross-checking technology.

According to Araújo, the system generates a DNA sequence for each résumé, which is treated as the DNA of a virus or bacteria, with mutations such as errors, deletions, and insertions. An algorithm known as BLAST, used to compare biological sequence data such as amino acids or DNA, identifies similar sequences, even those with errors.

“We align the DNA sequences—in other words the names of the researchers and their publications—to check for any coincidences,” says Araújo.

Free time

Nakaya published a calculation on his Instagram account of how much time per day is left for a researcher to do science. After teaching, extension, and administration, there was an average of two hours remaining to run experiments, create hypotheses, write articles, and debate results.

“Technology should help us to free up time for the really important things, whether that’s doing science or something pleasurable and personal,” argues Nakaya, Chief Scientific Officer (CSO) of D2DNA. To illustrate this philosophy, the Conflitulus Lattes app estimates savings on hours of work—which already exceeded 24,000 hours, with almost 8,400 résumés cross-referenced to the time this report was published.

Nakaya estimates that cross-referencing a hundred Lattes résumés could save 10 or more hours of work. “Current entrance exams attract more candidates, and people publish more articles, which makes cross-referencing information difficult,” emphasizes Nakaya.

According to the immunologist, the tool has been praised by deans, associate deans, and professors who organize examining boards. Nakaya states that a single user ran the tool 30 times, and cross-referenced almost 1,500 résumés. “We currently have almost 2,000 users registered across Brazil.”

One of the limitations of Conflitulus Lattes is the requirement to download résumés one by one—this is because the Lattes site uses the authentication tool Captcha, which blocks bot access.

Nakaya argues that if the Brazilian National Council for Scientific and Technological Development (CNPq) allowed direct access, then the site could use data direct from its bases, which would make for a quicker, less laborious task for the user. “This would eliminate the need to download the files—you would just need the list of names,” says Nakaya.

“We are looking to integrate Conflitulus Lattes with other academic platforms and databases, which will provide even greater capacity to identify conflicts of interest in different situations,” says Araújo, who believes that Conflitulus Lattes has potential for use in the detection of fraud and data inconsistencies. Araújo says that during a quick check of the Lattes résumé base, he and his team found thousands of typing errors among titles in English. “These inconsistencies can cause a lot of problems, but our technology can get around that in an innovative, efficient way,” he says.

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I am pursuing this to add value to a field that is already so well explored from a regulatory point of view—such as the Research Ethics Committees (CEPs) and the Brazilian National Research Ethics Commission (CONEP)—and broad in the sense of human relations (morality, empathy, responsibility, justice, respect). Reason allows us to reach conclusions based on evidence and logical principles.

I propose that by thoroughly understanding what we are dealing with, where we want to go, and what the consequences will be, we will have a good clue as to how we can better relate to ethical and/or moral concepts in application research.

It is worth revisiting Spinoza and adding that, according to him, “all ethics present as a theory of potency, as opposed to morality, which presents itself as a theory of duty.”

I would like to emphasize here the legitimacy of curious research, which must be neutral and free of judgment, requiring (ethical) validation only in terms of its execution, but I am interested in reflecting on its implementation.

I had an exceptional PhD student, Marlon Ribeiro da Silva, a historian concerned with (social) ethics in technological developments. In his thesis, he used critical reading and reflection on classic works, such as Frankenstein by British author Mary Shelley (1797–1851), to jointly guide technology students in developing their products and processes.

The title of his thesis, “Arte, ciência e tecnologia: um coração partido e a literatura como remédio” (Art, science, and technology: A broken heart and literature as a cure), is thought-provoking and highly recommended.

The aim of first reflecting on the motivation and consequences of our academic work is avoiding unethical practices, although there is no guarantee that significant discoveries, such as nuclear fission, won’t result in atomic bombs; and of course, looking at history from the rear-view mirror is always simpler.

I have been involved in research for decades, and I see that the curiosity for scientific knowledge prevents belief without explanation—which is why they call science the “church of reason.”

When we consider the motivation and the result of using our findings, we attach the word innovation to our research. In this context, we have some tools that can help our understanding and more ethical actions, such as medical design, mission-oriented research, and implementation research.

Medical design involves participative planning to define problems, strategies, and self-critique during the production process. Low-waste energy, focused and targeted light. I compare this role to that of an architect, who studies the behavior of a home’s future residents before proposing an appropriate design.

Here, we rely on reason, identifying and analyzing scenarios in advance, mapping strengths and weaknesses, using tools in an unbiased manner, and rigorously testing hypotheses, while always involving the user in the process.

Mission-oriented research, exemplified by the lunar mission ordered by John F. Kennedy (1917–1963) in 1961, to land a man on the moon and return him safely to the earth before the decade is out, at a cost of US$257 billion (current values), left a legacy of useful artifacts such as portable computers, carbon fiber, environmental education, astronautics, and the very appreciation of science.

Implementation science involves the effective use, by many people, of previously tested and approved evidence-based solutions. Known barriers, whether structural (lack of access and resources such as time, funding, and equipment), cultural and social (resistance to change and the adoption of new practices or technologies), knowledge and skill-based (lack of adequate training or understanding), legislative and regulatory (outdated or restrictive laws and regulations), or those related to communication (ineffective dissemination regarding the benefits and procedures, post-truth) must be studied and considered in the design and implementation of our research to increase chances of adoption.

This more holistic view of ethics, which uses rationality as a basis for its decisions, must be aligned with the concepts of morality, responsibility, justice, and respect, not only so that we do no harm to society, but so that we do good, so that we do better.

Paulo Schor is an ophthalmologist, former director of technological and social innovation at the Federal University of São Paulo (UNIFESP), where he is an associate professor at the Paulista School of Medicine, and research manager for the São Paulo Research Foundation (FAPESP).

The opinion articles do not necessarily reflect the views of Science Arena and Hospital Israelita Albert Einstein.

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And then the term “science,” despite being more prevalent in certain discussions, loses its most valuable meaning, becoming synonymous with “truth.” Which, paradoxically, creates a field day for deniers and peddlers of magic solutions—as we have shown time and time again in episodes of the podcast Ciência Suja.

I always joke that when I pitch Ciência Suja to someone, I immediately throw in: “but we like science!” Okay, the podcast’s title is provocative, but the idea behind it has always been to show the negative impact of cases in which science has been misrepresented or misused, and how it has progressed (or can progress) from there.

Since its debut in 2021, we have investigated topics such as the anti-vaccine movement, the tobacco industry, eugenics, denialism in the COVID-19 pandemic, and the high cesarean section rate in Brazil.

It is precisely because “we like science” that myself and the entire Ciência Suja team—shout out to Carolina Marcelino, Chloé Pinheiro, Felipe Barbosa, Pedro Belo and the Serrapilheira Institute, which believes in us—understand that it should be portrayed as it actually is.

In other words, not as watertight truths that pop out of the minds of geniuses oblivious to the world, but as a social institution replete with practices that seek to build, usually step by step, evidence, theories, and so on.

Like any social institution, science is subject to the vices of its times. Believing in pure science, untethered to society, is to simply deny the abundant evidence to the contrary.

Scientific racism, for example—a subject recently tackled on our podcast—is not a denialist movement, or something unrelated to science. It was created (and still persists) using scientific instruments, in a racist environment.

Much the same as the appalling Tuskegee Experiment, which kept Black men from being treated for syphilis to “see how it progressed naturally.” Likewise, the thousands of blood samples illegally collected from Indigenous peoples in Brazil—some to be sold to laboratories.

Along the same lines is the development of contraceptive pills on the back of abuses committed against women in Puerto Rico. Much like the HeLa cell line, which is still used to this day for various research purposes due to its “immortality,” and which was obtained without authorization from a poor Black woman who had only arrived at a health center to receive cancer treatment.

While studies using her cells took off in the second half of the twentieth century—garnering profit and fame for researchers and companies—Henrietta Lacks’s relatives continued to live in poverty.

Opioid epidemic

Shining a light on these cases is not intended to invalidate science or give ammunition to the deniers. On the contrary: with context and a journalistic approach that seeks ways to improve the problem in question, Ciência Suja aims precisely to foster the coveted scientific criticality in thought-provoking and well-scripted episodes.

For example: the current opioid epidemic in North America, which has already exceeded 700,000 deaths since its emergence in the 1990s, is fraught with nuance, but features a triggering event.

There was a claim that a powerful opioid-based painkiller, Oxycontin, was only addictive in a small minority of people, even if used continuously.

Representatives for Purdue, the pharmaceutical company behind this drug, claimed that Oxycontin was the painkiller “to start with and to stay with.” Backache or a headache that won’t go away? Take Oxycontin.

This claim was even endorsed by the American regulatory agency, based on a “study” that said only 0.03% of people who took Oxycontin developed an addiction. Very low indeed.

But… that study was actually just a letter to the editor published in the 1980s in The New England Journal of Medicine, and misrepresented by Purdue in order to maximize profits, which exceeded $35 billion from Oxycontin alone.

And what’s the difference between a letter to the editor and clinical research that actually helps determine substance dependence? There are many. And using this case, we were able to explain the main ones to our listeners.

Ultimately, the critical coverage of science that we incorporate into Ciência Suja effectively values the scientific method.

With the episode on the opioid epidemic, we hope that people will understand, minimally and critically, the drug development process, so that perhaps they will be less likely to fall for rhetoric such as the claim that COVID-19 vaccines are unsafe because they are “experimental” or have been developed too quickly.

Or that ivermectin helps treat dengue fever (yes, that’s on social media).

Science is not, in itself, egalitarian—in Brazil, a survey by the Multidisciplinary Research Group on Affirmative Action at Rio de Janeiro State University (GEMAA-UERJ), indicates that only 2.5% of the researchers in graduate programs in fields such as the Exact Sciences, Earth Sciences, and Biological Sciences are Black, Brown, or Indigenous women.

Science professionals can also propagate sexism and misogyny—a study published in January in the journal Nature indicates that half of the female researchers have experienced sexual harassment in academia.

And I’m not even going to get into the subject of predatory journals and the growing concern about article tampering.

But science, despite all this, is a valuable resource for society (perhaps one of the most valuable, in my opinion). And it is a “living” resource, capable of improving—as long as we are vigilant with it too, and understand that communication and journalism are part of this process.

Theo Ruprecht is a journalist who specializes in science and healthcare, and he is one of the creators of the Ciência Suja podcast. The podcast is supported by the Serrapilheira Institute.

Opinion articles do not necessarily reflect the views of Science Arena or Hospital Israelita Albert Einstein.

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