Artificial intelligence (AI) is poised to fundamentally alter the ways in which we approach public health, particularly given the rise of AI-enabled personalised medicine which can not only facilitate the delivery of tailored treatments to wider segments of the community but can also make it easier to prevent illnesses from occurring in the first place. The use of AI-enabled biosensors, for example, can lead to the earlier identification of illnesses and motivate individuals to behave in ways that may improve their health (1, 2). Personalised medical applications can extend treatment to a wider range of subpopulations, which can in turn contribute to the lessening of health inequalities (3). One example is engineered biosensors that can detect and respond to specific targets under relatively low resource conditions and require simple infrastructure, enabling practitioners to monitor the health conditions of those in remote areas who may have limited access to healthcare (1). AI offers enormous value to the delivery of personalised treatments due to its abilities to analyse large quantities of genomic data, make risk predictions, and conduct real-time monitoring (3, 4).

To deliver personalised medicine, AI technologies must be underpinned by scientific knowledge stemming from fields including engineering biology, which involves the application of engineering principles to the development of biological products and services (5, 6). Data-driven molecular design, a key area of research within engineering biology, uses AI to rapidly design and predict the functions of biological molecules, which has important therapeutic applications (7). Techniques used within engineering biology, such as next-generation sequencing, cell and gene therapy, pharmacogenomic testing, and microencapsulation, are also central to advancements in personalised medicine (8). When tailored with AI, discoveries in the field of engineering biology have the potential to usher in radically new ways of delivering healthcare that are more effective, targeted, and efficient.

While AI-enabled personalised medicine can add significant value to the health system—offering benefits such as faster diagnoses, more effective drug treatments, and improved patient outcomes—it is important that those who develop and administer such treatments do so responsibly to create the highest levels of benefit for the greatest number of people, and with the least risk of harm. AI bias is a particularly challenging obstacle that can occur at all stages of the development pipeline, from algorithm design to algorithm validation and clinical implementation (9). Algorithms may perform unequally in different subpopulations if not trained on datasets which are representative of diverse demographic and genetic factors (9). In addition, AI designers may feed their own biases into algorithms which can shape their outputs, such as when different developers inconsistently assign meaning to the data on which an algorithm is trained (10).

Although AI developers are only one type of actor in the much broader healthcare landscape, it is useful to consider what actions they can take to mitigate bias in the design of AI-enabled treatments (11). Developers are generally considered to be those who design and build AI algorithms, and have input throughout the development pipeline, including data preparation, algorithm design, and both pre- and post-deployment model evaluation (12). Developers typically are data scientists, AI scientists, or AI engineers (12). As one of the key actors along the development pipeline, developers have an important role to play in mitigating bias and ensuring that algorithms are designed in an equitable manner. Yet developers are often constrained by practical challenges which limit their capacity to engage in bias mitigation activities. In this paper, we critically explore the practicality of common bias mitigation strategies by highlighting some of the challenges which developers face in designing non-biased algorithms.

Before delving into the practical challenges faced by developers, it is useful to consider the current research landscape on developer-oriented bias mitigation strategies. We conducted a systematic literature review over the past 10 years (2015–2024) exploring bias mitigation strategies proposed for implementation by AI developers (13). By “bias mitigation strategies,” we mean any actions that can be taken by developers to reduce the likelihood or severity of bias in the design of an AI algorithm. The search was performed specifically within the literature in the field of healthcare, and captured any articles discussing concepts related to bias, such as fairness, equity, inclusivity, and justice. Articles were included if they provided solutions for bias which were directly targeted at AI developers (see Figures 1, 2 for an overview of the review process).

Analysis of the 51 articles included in the review showed that bias mitigation strategies tend to be grouped around seven key themes. Many articles argue that development teams should be composed of a diverse range of individuals, including from different demographic backgrounds and with diverse areas of expertise (14, 15). Some claim that developers need more training and education on bias mitigation (16, 17). Responsibility is placed on developers to be aware of potential sources of bias and to be reflexive about their own biases (18, 19). Many highlight the importance of training algorithms on datasets that represent diverse and underserved subpopulations (20, 21). Several claim that collaborating with end users and beneficiaries could help developers identify a wider range of potential biases (22, 23). Monitoring is another key theme, with emphasis on the need for developers to regularly evaluate algorithm performance during the design and implementation stages (20, 24). Finally, transparency around algorithm performance is seen as an important part of the bias mitigation process (25, 26).

All of these strategies have value in a public health context. For example, by ensuring that the data which are used to train algorithms are representative of diverse populations, developers can help to improve community-wide health outcomes (11). Being transparent about the strengths and limitations of a given algorithm, including any potential sources of bias, is also critical, particularly given the increasingly hands-on approach that many people take towards their own healthcare (11, 27).

The bias mitigation strategies identified in the literature are useful insofar as they provide general guidelines for developers to follow when seeking to minimise bias. However, the reviewed articles generally fail to suggest how these strategies can be operationalised in real-world settings. Almost all of the reviewed articles tackle the issue of AI bias from a theoretical standpoint, with only 18% of the articles basing their arguments in empirical findings. In addition, the majority of articles (63%) address the issue of bias in healthcare as a whole, rather than within a particular field of medicine, with only one of the reviewed articles specifically addressing ways that developers can mitigate bias when using AI for public health-related purposes, namely Flores, Kim, and Young who discuss some of the actions that developers can take to minimise bias when designing algorithms for public health surveillance purposes (10). What is largely left out of their discussion, however, is a consideration of the constraints that developers face which may prevent them from fully adopting the outlined strategies. Without understanding the contexts within which developers work, it is difficult to appreciate the practical limitations that developers face in implementing these strategies in real-world settings. In the following section, we provide some examples illustrative of the challenges that developers face in undertaking bias mitigation.

Despite widespread calls for developers to access diverse data, research has shown that health datasets often lack diversity in genetic and demographic data (28, 29). This gap is problematic, as an individual’s genetic makeup, and demographic factors such as sex and age, have direct impacts on health outcomes, meaning that algorithms trained on under-representative data may not be as effective for certain subpopulations (30, 31). While developers can use techniques such as oversampling or ensemble learning to minimise bias from under-representative datasets, significant ambiguity still exists around how to deal with missing data (32). Should developers hold off on developing algorithms until they have access to better data, or should they continue to develop treatments that are known to deliver health benefits for only some segments of the community (33)? Similar questions can be asked about the merits of using synthetic data to fill gaps in existing datasets, particularly given concerns that this approach can potentially reinforce biases and undermine the consent process (34).

Developers are also constrained by decisions made upstream in the collection and storage of health data. Placing the onus on developers to access diverse data is problematic as it assumes that they can actually do so. On the other hand, those who create and manage datasets tend to be better situated than developers to shape the types of health data that are collected (35). For example, to protect data subjects’ privacy and for logistical reasons, developers may need to access data through federated learning systems which enable them to test their algorithms on a dataset without directly accessing the data itself (36, 37). The managers of the datasets, rather than the developers themselves, are thus responsible for deciding what types of data are made available. Upstream decisions around data collection therefore have significant impacts on whether a developer is able to design a representative algorithm. Questions remain about how to strike the appropriate balance between ensuring that datasets are diverse and protecting the privacy of data subjects, particularly when dealing with sensitive genetic data. Should datasets be scrubbed entirely of personal information such as ethnicity and socioeconomic status to protect patients’ privacy, or should such information be retained in order to better judge the fairness of a particular algorithm (38, 39)? Again, dataset managers are best placed to answer such questions given their responsibility over data dissemination.

Collaborating with the beneficiaries of AI-enabled treatments is another key bias mitigation strategy mentioned in the literature that comes with its own challenges. It is vital that developers produce algorithms which reflect the values and needs of the community (40). This type of outcome can be achieved by engaging with the beneficiaries of AI-enabled treatments in participatory ways, such as processes of co-design (41). Taking a diverse range of views into account can help developers to minimise their personal biases and identify sources of bias that may have been overlooked (41). Engaging the community can also help combat a common critique levelled against public health initiatives, namely that they risk devaluing individual choice in healthcare (42).

Despite the benefits of community engagement, developers may not have the time, skills, or resources to systematically collaborate with members of the public each time a new treatment is being developed (43). Other actors in the AI development pipeline may be better placed to engage with relevant publics. Healthcare practitioners, for example, have direct access to patients, making them ideally placed to provide insights on the healthcare needs and values of the wider public (44). Ethicists and social scientists can also provide insights into community values, particularly based on their empirical research, including how people want their data to be used and stored (45). Developers may thus have to rely on the insights of intermediaries who have better access to the public, particularly when they do not have the resources or capacity to undertake direct engagement themselves.

Implicit within much of the literature on developer-oriented bias mitigation strategies is the assumption that developers maintain oversight and control over their algorithms throughout all stages of the development process (44). Yet in some cases, it is unreasonable to expect individual developers to be responsible for biases that become apparent once an algorithm has been clinically deployed. It is well recognised that bias which was not present during the design stage can emerge during model deployment (9). End users, for example, may overrely on a model’s findings, leading to automation bias (46). These biases may even feed back into the algorithm if it has been programmed to learn from end users’ interpretations (46). One way of mitigating these biases is to ensure that developers maintain oversight of their models during the implementation stage to ensure that they are working as desired and any new or existing biases are identified and removed (44). Yet in practise, developers may only be engaged in upstream model design and may not be involved in the commercialisation of a given AI-enabled treatment, making it impossible for them to rectify such biases.

Finally, developers may be hampered by time and resource constraints that prevent them from fully understanding how an algorithm has been designed. Designing a bespoke algorithm from scratch is an expensive and time-consuming process and may not be necessary if an existing model that can be adapted is readily available (47). In such circumstances, developers may not be fully aware how an algorithm has been designed, making it difficult to detect whether any biases have been built into the model (48). Expecting developers to be aware of such biases can be problematic, particularly when algorithm owners are not forthcoming about how a given algorithm has been designed and on which subpopulations it has been tested. Greater clarity about the appropriate attribution of responsibility in such cases is urgently needed. Should developers who take advantage of existing algorithms be responsible for earlier design choices which lead to bias, or should algorithm owners be held accountable for the downstream implications of their model? Most importantly, how can bias be identified and mitigated in these typical types of development processes?

With health system innovations increasingly being tied to AI, public health outcomes will be increasingly determined by how algorithms, and treatments enabled by these algorithms, are developed. As key actors in the AI development pipeline, developers have important roles to play in developing treatments which are not only effective but are designed in an equitable manner to minimise the potential for harm. Developers are not the only actors responsible for the ethical development and use of AI-enabled healthcare treatments. Scientists, healthcare practitioners, companies, and regulators, among others, all play important parts in the responsible use of AI. However, developers do have key roles due to their capacity to shape the direction of AI-enabled healthcare and by directing their efforts in ways that reflect the values of the wider community. As AI becomes ubiquitous, and is deployed in the context of more personalised forms of medicine, it is vital that developers of these kinds of AI-enabled treatments are aware of the unique challenges that these technologies pose and mitigate the biases that come with their use whenever possible.

Many questions remain about just how feasible it is for developers to implement many of the bias mitigation strategies which are commonly cited in the literature. Actions occurring both upstream and downstream in the development process can have significant impacts on a developer’s ability to provide the basis for non-biased treatments. Developers are often limited by time and resource constraints which may prevent them from undertaking certain bias mitigation activities. They may also lack oversight during the implementation stage. Developers should therefore be encouraged to engage with other actors along the development pipeline, such as healthcare practitioners, who can aid them in incorporating the values and needs of the community into their work. Ambiguity also remains about the appropriate course of action that developers should take in the context of more novel methods such as the use of synthetic data to supplement gaps in existing datasets.

While we have outlined some of the challenges which developers face in mitigating bias in the development of AI-enabled treatments, more research is needed to test the utility and practicality of bias mitigation strategies in real-world settings. More research is required on uses of AI within public health domains where treatments will become increasingly personalised and where patients will have the opportunity to be more actively engaged in their own healthcare. Greater clarity is also needed around the extent of developers’ responsibility and how our notions of responsibility should be shaped by the practical limitations associated with bias mitigation. Are developers still responsible for biases that emerge during model deployment, even if they no longer have oversight over the implementation stage? Should developers be responsible if an algorithm performs poorly on certain subpopulations if data on such populations are absent in the first place? These types of questions must be answered for developers to appropriately direct their efforts to foster reduction of bias in ways that align with their expected responsibilities.

Questions around responsibility are further complicated by the lack of clarity around how developers themselves should be defined. Developers are generally viewed as a homogeneous group of AI experts who are responsible for handling the technical aspects of algorithm design. Yet there are many instances where a scientist may use existing algorithms to develop a given treatment without themselves being experts in algorithm design or having engaged the services of someone traditionally considered to be an AI developer. Scientists may also be hesitant to label themselves as developers, even though their work may involve the customisation of existing algorithms to suit their needs. More research is thus needed to unpack the different types of AI developers and the impact that different development pipelines have in terms of attributing individual responsibilities. Having greater understanding of the wide range of ways in which AI is being used in the public health domain will ultimately enable us to more effectively target bias mitigation strategies to mitigate effects for specific types of AI use. Taking into account the practical challenges that developers may face will enable us to develop methods to overcome these challenges and better assign responsibility for bias mitigation in ways that more accurately reflect the contexts within which developers work.