Utopian Balance
Exploring the Balance Between Values, Goals and Outcomes in Biological Research
Authors note: I wrote this essay together with 2 others (Eileen & Paulina) in 2024 for a university project concerning the value judgement in science. We tried to elucidate how unpredictableness in scientific discovery is often underlooked at in research, especially in the beta sciences, and how new discovery often have unforeseeable consequences. In this essay we describe how a scientist, in for example complex and broad fields as Biology can contribute to the way we intepret new knowledge think about the needs, insteads of an end in their own research. Have fun reading!
“Man is ravaged by a plague: his opinion that he knows something”
Introduction: A Leap of Faith in Science?
In 2018, Dr. He Jiankui, a biomedical researcher from the Southern University of Shenzhen, made an unexpected appearance at the 2nd International Summit on Human Genome Editing, following the controversial leak of his work. He revealed his involvement in the birth of twin sisters, Lulu and Nana. The mere fact of their birth might not seem remarkable—twins are, after all, born every day. Yet, what set these twins apart was not their arrival but the method by which they came into existence. He Jiankui employed the powerful tool of genome editing on their parents’ reproductive cells, targeting the CCR5 gene. This gene encodes a protein that allows the HIV virus to attach to human immune cells and it was deactivated to make the children immune to the virus. While the idea of providing immunity from a devastating disease may appear noble at first glance, the implications are far from straightforward. The CCR5 gene is not just involved in immune functioning—it is also tied to boosting cognitive processes such as memory and intelligence, raising questions about whether this intervention was solely therapeutic or if it ventured into the realm of human enhancement (Raposo, 2019). Reflecting on his motivations, Dr. He claimed, ‘If you see your relative or friend with a severe disease... you want to help him.’ (WCSethics, 2018). However, this statement, while compassionate, opens a floodgate to a deeper question: What could be the potential consequences?
In the realm of scientific research, we often envision ourselves as objective seekers of truth, driven by curiosity and the pursuit of knowledge. Yet, beneath this seemingly neutral process lies a complex web of values that shapes not only the direction of our inquiry but also how we interpret and act upon the outcomes. This is particularly evident in controversial cases, such as Dr. He Jiankui's genome-editing experiment, where the boundary between scientific progress and ethical responsibility becomes blurred. The use of technologies like CRISPR to alter the genetic code of future generations highlights how deeply our values influence the goals we set for research and the methods we choose to achieve them.
Dr. He Jiankui at the second international summit on human genome editing in Hong Kong, 2018
AFP/Getty Images
At the start of any research endeavor, explicit and implicit values are embedded in the objectives we formulate. These values influence how we define a problem, what qualifies as a solution, and which risks we deem acceptable, all based on our broader societal, moral, and personal frameworks. These values do not merely provide a background to 4 scientific work; they actively shape the path of scientific discovery. Whether we aim to cure disease, enhance human capacities, or reduce suffering, our goals are steeped in value judgments about what is desirable and good. However, once the research is underway, especially when it concludes, we confront outcomes that may diverge from our original intentions. These unexpected outcomes challenge the assumptions we began with and force us to reconsider the consequences. The case of genome editing, for instance, raises essential questions: Can we fully anticipate the consequences of our actions, and how should we balance potential benefits against unknown risks? While we might predict some dangers, the complete range of consequences—both intended and unintended—fully manifest only after the research is complete. The question is how to balance the value-driven intentions we start with against the 'predicted risks' we try to foresee. Ultimately, the unpredictability of actual outcomes requires a critical re-evaluation of our initial intentions. After all,
‘Every new technology bears witness to the integrity of the scientist’ (Merton, 1942).
Therefore, in this essay, we aim to explore this complex balance between values, goals, and the uncertainty inherent in predicting the outcomes of (biological) research. We will argue for what we call 'bottlenecks' in the value judgments that guide researchers' intentions and the assessment of (predicted) consequences (Figure 1). First (I), we will explore the complexity of human values and how it complicates research decisions. Second (II), we will discuss how the goal of pursuing knowledge should not obscure the potential risks associated with it. Third (III), we will argue that the inherent complexity of biological systems makes it difficult to predict outcomes, challenging the reductionism that is often assumed in research. Finally (IV), we will assert that the evaluation of scientific outcomes is not static; our understanding about their consequences, both good and bad, can evolve over time with cultural, scientific, and social shifts. In the end, we hope to encourage the reader to adopt a more responsible and moral attitude toward scientific research that takes into account both immediate and long-term effects.
Figure 1: Summary figure illustrating the ideal or 'Utopian' balance between the intentions and outcomes of scientific research, based on a modified triptych-model.
I. The Moral Maze
Human values are extremely diverse and can deeply influence research, often in ways that are not immediately evident. Philosopher Kevin C. Elliot argues that the influence of non-epistemic values, such as ethical, social, or environmental concerns, is unavoidable in scientific research but may be helpful in achieving legitimate goals. In his book, A Tapestry of Values (2017), Elliot goes deeper into how the non-epistemic values can shape certain aspects of scientific research, from the choice of research topics and aims of investigations to the ways scientists handle uncertainty and even the language they use to describe the results. He emphasizes, however, that to ensure the appropriate influence of values, researchers must meet three key conditions, namely transparency, representativeness, and engagement. It is important for scientists to be as transparent as possible about their data, methods, and assumptions so that value influences can be examined. Moreover, values should represent important social and ethical priorities, and fostering engagement ensures that relevant stakeholders can critically assess how these values influence research decisions (Elliott, 2017).
However, the complexity of human values can make it challenging to achieve these ideal conditions. Scientists often engage in research due to personal reasons, including career advancement or addressing problems that concern their own lives or communities. Larger entities, such as companies or political organizations, that surround the scientists also introduce their own economic, political, or religious values, which may further influence research. This diversity of values can create obstacles in research decision-making as the competing motivations often make it difficult to set clear and objective research goals.
On the other hand, philosopher Helen Longino argues that the diversity of values strengthens objectivity by helping to eliminate possible biases and stereotypes in science (Longino, 1995). In contrast, philosopher Kristen Intemann critiques Longino's focus on value diversity, suggesting that we should prioritize including individuals from diverse social backgrounds in decision-making. Intemann believes that this approach, rather than emphasizing value diversity, is more representative of broader societal perspectives and ultimately helps those who are often marginalized (Intemann, 2017).
When researchers fail to meet Elliot's conditions for incorporating values into science, the influence of those values can become problematic. Lack of transparency or engagement in critical evaluations can allow undesirable values to compromise scientific integrity, as shown in the historical case of Vavilov and Lysenko. Although both aimed to improve agriculture in their country, their differing values resulted in completely different approaches. Vavilov, a geneticist dedicated to evidence-based science, devoted his life to studying plant genetics to improve agricultural strategies. Lysenko, on the other hand, was strongly driven by anti-genetic, political motivations that disregarded scientific evidence and transparency. According to his fellow agricultural scientists, his work "suffered from errors, exaggerations, and perhaps fraud", yet it could not be properly evaluated as any criticism was brutally repressed. Ultimately, Lysenko's rise in the Soviet hierarchy led to the imprisonment or execution of several prominent geneticists, including Vavilov, and delayed scientific progress, causing widespread harm (Elliott, 2017).
Not all research driven by personal values leads to negative outcomes. Philosopher Philip Kitcher has suggested that such values could foster scientific progress by stimulating cognitive diversity and motivating researchers to pursue theories that may otherwise be neglected (Kitcher, 1990). The story of Theodora Colborn, renowned for revolutionizing our understanding of environmental pollution, illustrates how personal values can lead to scientific discoveries. Her deep concerns for the environmental and public health encouraged her to study the effects of endocrine-disrupting chemicals – a topic that had been largely overlooked by others at the time: "She would not have pursued a new career as an environmental scientist— let alone engage in hours of detective work to pore over research articles on the light of Great Lakes wildlife— if it were not for her strong environmental values". Although some critics argued that she "<...> sometimes leaped ahead of scientific evidence and drew stronger conclusions that they thought the evidence warranted", Colborn was always transparent about her research, acknowledged its limitations, and was open to criticism from others (Elliott, 2017). Importantly, while her strong environmental values encouraged her to draw bold conclusions, they were also in alignment with broader societal well-being, as she prioritized alerting the public to potential dangers.
Ultimately, the diversity of human values makes it difficult to predict their effects on research outcomes. While personal, social, and ethical values can foster progress, they also introduce layers of complexity, where competing interests must be weighed carefully. Acknowledging the presence of these values and critically assessing their impact can help to reduce the risks of unwanted consequences in scientific research.
II. The Dark Side of Discovery
The pursuit of knowledge, while crucial to scientific progress, should not overshadow the potential risks of scientific discovery. Philosophers such as Karl Popper, Philip Kitcher, and Thomas Kuhn have all emphasized the importance of solving scientific problems ('puzzle solving') and pushing the boundaries of knowledge (Kitcher, 2001; Kuhn, 1962; Popper, 2005). Kuhn, in particular, argues that 'normal science' progresses so quickly because scientists are continuously solving puzzles that 'only their lack of ingenuity should keep them from solving'. He notes that many great scientific minds have dedicated their careers to tackling challenging puzzles that no one has solved or solved so well yet (Kuhn, 1962). However, while the pursuit of knowledge is a powerful motivator for scientists, it also brings inherent risks.
Numerous cases in science history have already shown that scientists can be blinded by their motivations to pursue knowledge and neglect the (potential) risks associated with it. As shown by the studies performed in the mid-twentieth century (e.g., the Tuskegee syphilis study, Nazi experiments in concentration camps, multiple cases described by Beecher, and others), the pursuit of scientific knowledge was often used as a justification for some of the most brutal and unethical abuses of human subjects (Beecher, 1966; Brandt, 1978; Weindling et al., 2016). Similar observations can be made in the documented animal studies, where the ends were used to justify the needs (Rollin, 2007). Nowadays, there are strict regulations operating research that aim to prevent such destructive behaviors. However, although these regulations have improved ethical standards in research, unethical practices can still occur.
Furthermore, there are risks associated with research outcomes that go beyond direct harm to humans or animals. For example, there were a lot of debates about research on cognitive abilities between different groups in society, suggesting that findings from such studies are likely to be misused to perpetuate societal biases. According to Elliot, who discussed philosopher Kitcher's work in his book, research into cognitive abilities is nearly always subjected to misuse in ways that disfavor marginalized groups. Elliot further points out that any findings indicating cognitive differences between certain genders or racial groups are likely to be interpreted asymmetrically in cultures with implicit or explicit biases. On the other hand, even if the researchers are unable to identify cognitive differences, their work is unlikely to reduce prejudice or alter societal biases (Elliott, 2017). Overall, while such studies could contribute to our scientific knowledge, the potential harms seem to greatly outweigh any potential benefits.
To mitigate the negative outcomes of the pursuit of knowledge, societal and ethical concerns should be firmly established in scientific research. Philosopher Heather Douglas suggests that scientists must assess their methods and objectives in light of ethical and societal values, emphasizing that the pursuit of knowledge should not come at the expense of neglecting those values: 'Aiming at truth does not by itself tell us which truths are worth knowing. We need our social and ethical values to help shape that judgment' (Douglas, 2023). This perspective, along with Kitcher's concerns about cognitive research, emphasizes the need for scientists to remain aware of the broader implications of their work.
Overall, while the pursuit of knowledge is a strong driver for scientific progress, it must be approached carefully, taking into account the potential risks and ethical concerns involved. Historical cases demonstrate how this pursuit can result in serious harm when ethical considerations are overlooked. Philosophers such as Philip Kitcher and Heather Douglas highlight that researchers should reflect on how their scientific findings will affect society. Ultimately, by raising awareness and critically analyzing ethical and societal implications, we can reduce the risk of negative outcomes linked to the pursuit of knowledge.
III. The Unpredictable Dance of Life
In addition to complex influences of values, the inherent complexity of biological systems further complicates the prediction of all possible research outcomes and thus the potential risks. Life is based on events that range from molecular changes that occur in nanoseconds to populational changes taking millions of years. Many biological systems are incredibly complex. One system can contain numerous interconnected components and interactions. The interactions among lower-level components often affect those at a higher level, resulting in systems within a system network. Systems biology has revolutionized ecology, population biology, and evolutionary studies, and it is gradually making advances in biochemistry, development, genetics, and whole-plant biology. However, molecular biology has only recently begun to adopt a systems perspective as a result of enormous developments in genomics. Understanding the complexity of biological systems represents the greatest intellectual and experimental challenge yet faced by any biologist (Trewavas, 2006).
To illustrate the intricate tapestry of biological systems, let’s explore the dynamic interplay of genes, cells, and organs. A single organ, such as the heart or liver, has a coordinated complexity. It consists of various cell types, each with distinct functions and properties. For instance, the heart contains cardiomyocytes for contraction, endothelial cells for lining blood vessels, and fibroblasts for structural support. These cell types must coordinate their activities with extreme precision to ensure the organ’s proper function. Beyond their collective contribution, individual cells are thriving centers of activity, performing numerous life-sustaining tasks such as regulation of gene expression, protein synthesis, and the maintenance of balanced energy metabolism and cell signaling. These activities are governed by complex regulatory networks, which involve a myriad of players, namely transcription factors, signaling molecules, and epigenetic modifications. Thanks to these regulators, cells can respond to environmental cues, such as changes in nutrient availability or stress, and maintain a stable internal environment, also known as homeostasis.
Moreover, the interactions between cells extend beyond the boundaries of a single organ. Cells communicate with one another through a variety of signaling mechanisms, allowing them to coordinate their activities and respond to systemic changes. For example, hormones secreted by endocrine glands can influence the behavior of cells in distant organs.
To manage biological complexity, scientists frequently adopt a reductionist approach, focusing on specific components or subsystems. This allows for a more manageable analysis but necessitates careful synthesis of findings to understand the system as a whole. While understanding individual components is crucial, it is equally important to recognize the systemic interactions and emergent properties that arise from these interactions. Overlooking the broader context can lead to incomplete or even inaccurate conclusions. For instance, predicting the behavior of a complex ecosystem requires not only knowledge of individual species but also their interactions, habitat conditions, and climate factors. Epistemic limitations, such as uncertainty, technological constraints, and methodological challenges, can hinder our ability to fully comprehend complex systems (Elliott-Graves, 2020). However, decades of scientific research and technological advancements have greatly improved our understanding of biological systems, allowing more accurate predictions and interpretations of scientific findings. For example, the development of DNA based technology in the 20th century dramatically expanded our ability to study genetic information, leading to breakthroughs in fields like medicine and biotechnology (Heather & Chain, 2015). Overall, although reductionist approaches help to manage biological complexity, a lot of translational work is required to integrate findings, which may not always reflect the full reality. As a result, the broader consequences of scientific discoveries in biology are often difficult to predict.
The introduction of antibiotics into clinical practice was undoubtedly one of the most significant medical advancements of the twentieth century, but also a great example of unforeseen outcomes (Hutchings, 2019). Physician-scientist Alexander Fleming postulated the existence of penicillin, a molecule produced by specific molds that either kills or inhibits the growth of certain kinds of bacteria. At that time, he was working on a culture of disease-causing bacteria when he noticed spores of a green mold, Penicillium rubens, in one of his culture plates (Tan & Tatsumura, 2015). The growth of the bacteria was inhibited by the presence of this mold. Fleming hypothesized that mold must secrete an antibacterial substance, which he named penicillin. However, he struggled to extract and purify penicillin for medical use due to technical limitations. In the 1940s, Howard Florey and Ernst Chain successfully developed methods to produce penicillin in sufficient quantities for medical use on a larger scale. During World War II, the urgent need for treatment against bacterial infections led to widespread use of penicillin, which saved countless lives and became crucial in treating infections. By 1945, penicillin was widely available. Its discovery constituted a turning point in medicine, starting the age of antibiotics. However, what scientists did not anticipate at that time was the potential for bacterial resistance. It was only after the widespread use of penicillin and other antibiotics in the mid-20th century that cases of resistant bacteria began to emerge. These resistant strains were able to survive and reproduce, leading to the development of antibiotic-resistant infections. Today, antibiotic resistance is a major public health concern, which serves as a reminder that even the most valuable discoveries can have long-lasting and unintended consequences (Hutchings, 2019).
In summary, biological systems are vast and complex, making it difficult to predict all possible outcomes before the research unfolds. While reductionist methods – breaking complex systems into smaller components – are helpful for studying them, they do not automatically translate into the systems-level perspective. Epistemic factors and technical limitations further complicate biological research predictions, yet making predictions remains essential for reducing the risk of unintended outcomes. The problem of antibiotic resistance illustrates how difficult it is to anticipate all the possible outcomes in biology and that it is important to consider both direct and indirect effects of scientific findings.
“The humanitarian, like the missionary, is often an irreducible enemy of the people he seeks to be friend… Arrogance, fanaticism, meddlesomeness may then masquerade as philanthropy”
IV. Unforeseen Futures: The Consequence Dilemma
In the previous bottleneck, we examined how the inherent complexity of biological systems poses significant challenges for predicting consequences in research. Similarly, the process of assessing the potential outcomes of scientific research is far from easy. These research assessments are shaped by the context in which the research is conceived - cultural norms, ethical considerations, and the current state of scientific understanding. What we perceive as risk at this early stage is influenced by our existing knowledge frameworks, which are themselves limited and may be shaped by the biases of the moment.
Scientists often carry out their work in controlled environments, focusing on whether their results are repeatable. These experiments tend to have clear, predetermined goals, resulting in what we call 'static discoveries'. However, due to the objective nature of scientific inquiry, scientists are often limited in understanding causality, especially when it comes to predicting outcomes that have not yet occurred. This limitation arises because the initial novelty of this ‘static discovery’ is confined within the established space of existing ideas and theories - at least when viewed through the lens of Kuhn’s theory of scientific paradigms. Trying to predict the consequences of an invention or discovery before it happens could be rather self-defeating, as the invention would then exist at the time the prediction is made rather than at the time we want to foresee it.
To illustrate our point, imagine two boxes. One represents the pre-research phase, and the other one is the post-research phase (Figure 2). The pre-research phase offers a degree of freedom, allowing researchers to imagine scenarios, outcomes, and possibilities that are not yet constrained by the data – a possibility to think outside the box. This speculative thinking allows for creative leaps and opens the intellectual space for novel approaches. However, the predicted risks considered at this stage are more abstract.
Once the research has been completed, the landscape changes and risks can be assessed more concretely, now informed by the evidence that has been gathered. At this point, the scope of prediction becomes narrower and more fixed as empirical data provides a clearer picture of potential consequences. This makes it easier to accurately predict the associated risks. However, the post-research phase also brings certain limitations. While evidence enables more reliable assessments, it simultaneously confines the researcher’s thinking within the boundaries of what has already been discovered. This 'consequence dilemma' becomes a Catch-22: both an enabler and a constraint.
Figure 2: The ‘Consequence Dilemma’ model illustrates the challenge of predicting risks in research.
Furthermore, our understanding of the world and the values through which we interpret it change over time. The philosopher Mark Risjord argues that human action, especially when it involves creativity, is inherently unpredictable. Any attempt to predict such behavior seems to contradict the very nature of innovation, which must be both radically new and grounded in principle rather than mere speculation. As Risjord succinctly puts it, “creativity precludes prediction” (Risjord, 2014). A risk perceived as significant at one point may be reinterpreted in the light of new knowledge or shifting cultural values. What was once considered a minor or negligible risk may later be seen as far more significant. As history demonstrates, scientific discoveries often lead to consequences that could not have been predicted within the intellectual frameworks of their time. For example, the widespread use of antibiotics has led to antibiotic resistance, as discussed above.
A more modern example might be the recently developed cerebral drug delivery system using live parasites (Yoon et al., 2024). While this is a groundbreaking advance in medical science, allowing for targeted treatment within the brain, the authors of the paper did not discuss the broader implications of their discovery. Specifically, they only focused on the potential for drug delivery without considering the possible risks (and ethical concerns) associated with introducing parasites into the human system. Given the complexity of biological systems, it is difficult to ensure that the implementation of this system in the human body will not cause any harm. For instance, what if the introduction of a live parasite could have unintended, permanent effects beyond its initial purpose? Also, is there a risk that it could eventually get exploited for human enhancement or even biological warfare?
Overall, in both science and human behavior, efforts to predict outcomes are inherently limited, suggesting that certain aspects of discovery are beyond the reach of conventional methods of prediction. The limits of the ‘Box’ within which post-research assessments are made are not completely static but will gradually evolve as human understanding of good and bad shifts. Scientific findings, while providing more concrete evidence, are always subject to reinterpretation as new questions arise, new values emerge, and the socio-political context changes. In short, values influence decisions about the amount of evidence needed to reach a conclusion, including the evidence needed to assess potential risks. As the feminist philosopher Heather Douglas puts it, "we cannot use scientific information effectively without proper scientific interpretation" (Douglas, 2007).
Conclusion: The Strive and Balance for Scientific Utopia
In this essay, we identified and explored the key bottlenecks in balancing the value judgments that guide researcher's intentions and the evaluation of (predicted) outcomes in (biological) research. The central question was why it is difficult to fully anticipate the consequences of our decisions and what we should consider when weighing potential benefits against the (unknown) risks. We argued (I) that the diversity of human values complicates research decisions and prediction of outcomes, highlighting the importance of trying to recognize those values as well as their influence on research. Following that (II), we pointed out that the pursuit of knowledge can become dangerous when it is used to justify unethical methods or detrimental outcomes of research. To address this problem, we encourage researchers to always reflect on the ethical and societal implications of their work. Furthermore (III), we discussed how the complexity of biological systems challenges the assumption of reductionism in research, making it difficult to predict all possible outcomes. Lastly (IV), we argued that the analysis of scientific outcomes is not static.
Certain aspects of discovery are beyond the reach of conventional prediction methods, as the assessment of research can be unpredictable and evolves along with cultural, scientific, and social developments. Ultimately, to address all four bottlenecks, we introduced the ‘Consequence Dilemma’ model. This model illustrates that the pre-research phase allows for greater creative freedom to explore risks and possibilities since it is unconstrained by empirical data. In contrast, in the post-research phase, the scope is narrowed by the empirical data, allowing for more accurate risk assessments but at the same time limiting exploration to what has already been discovered. This paradox, where evidence both empowers and limits scientific understanding, remains a challenge in scientific research.
In a utopian world, research goals should never outweigh the potential negative outcomes. However, since we don’t live in a utopian society, finding this balance and making universal guidelines is incredibly challenging. While achieving an ideal balance in biological research may seem impossible, the effort to strive for it remains crucial. The pursuit of balance is not about reaching a perfect state but about maintaining a sense of personal responsibility when encountering diverse perspectives and ethical disagreements.
Each researcher must cultivate their own internal balance, because the mere act of striving towards harmony, even in its absence, fosters greater awareness. This heightened awareness can help people to make more thoughtful decisions in the complex and ever-evolving landscape of scientific research. Moreover, we suggest that institutes conducting scientific research should actively foster conversations about the values that shape their investigations. By encouraging these discussions, we can gain a deeper understanding of the intrinsic motivations driving scientific research. Furthermore, scientific journals should dedicate more space to philosophical debates on biological topics, stimulating a thoughtful exploration of the ethical and societal implications of their research. In scientific papers, researchers can be given the space to formulate their own opinions about their work without it being regarded as ‘unprofessional’ or harmful to the objectivity of their work.
