False assumptions about sex and gender are deeply ingrained in the medical system, leading to binary, static, and concordant interpretations. Machine learning researchers must be aware of these assumptions to avoid perpetuating them. This perspective piece highlights three common mistakes made by researchers when dealing with sex/gender data: "sex confusion," which refers to the failure to understand what sex in a dataset signifies; "sex obsession," which assumes that sex assigned at birth is the relevant variable for most applications; and "sex/gender slippage," which conflates sex and gender even when only one is known. The authors then discuss how these pitfalls manifest in machine learning studies based on electronic health record (EHR) data. EHR data is widely used for various purposes, including retrospective analysis of patient outcomes and developing risk prediction algorithms. However, the misuse or misinterpretation of sex/gender data can lead to biased results and inadequate care for transgender individuals. To avoid methodological pitfalls, researchers are advised to work with data experts who have experience handling such data. Collaboration with clinicians can provide insights into how the data is used in clinical practice. Additionally, clear descriptions of how sex/gender data were collected and utilized should be provided in research papers. It is crucial to acknowledge any limitations or changes in variables over time. Incorporating gender carefully is another important consideration. Gender encompasses multiple dimensions such as identity, relations, roles, and institutionalized aspects. Researchers should not conflate sex and gender or assume that sex represents an absolute truth while disregarding individual gender identities. Overall, this perspective piece emphasizes the need for machine learning researchers to engage thoughtfully with questions of sex/gender in their research and algorithm development processes. By avoiding methodological pitfalls and considering the complexities of sex/gender, researchers can produce more inclusive research outcomes that better serve all patients, including transgender individuals.
- False assumptions about sex and gender in the medical system
- Three common mistakes made by researchers dealing with sex/gender data: "sex confusion," "sex obsession," and "sex/gender slippage"
- Pitfalls of misusing or misinterpreting sex/gender data in machine learning studies based on electronic health record (EHR) data
- Importance of working with data experts and collaborating with clinicians to handle such data properly
- Clear descriptions of how sex/gender data were collected and utilized should be provided in research papers
- Acknowledgment of limitations or changes in variables over time
- Careful incorporation of gender, considering multiple dimensions beyond just biological sex
- Need for machine learning researchers to engage thoughtfully with questions of sex/gender for inclusive research outcomes that serve all patients, including transgender individuals.
In the medical system, people sometimes make wrong assumptions about sex and gender. Researchers can make mistakes when they study data about sex and gender, like getting confused or too focused on it. Using this data in machine learning studies can be tricky because it might be misused or misunderstood. It's important for experts in data and doctors to work together to handle this kind of information correctly. When researchers write about their studies, they should explain how they collected and used the data about sex and gender. They should also mention any limitations or changes in the information over time. It's also important to think about different aspects of gender, not just biological sex, when studying this topic. Machine learning researchers should think carefully about questions related to sex and gender so that their research can help all patients, including transgender people."
Definitions
- Assumptions: Beliefs or ideas that you think are true without having proof.
- Sex: The physical differences between males and females.
- Gender: The roles, behaviors, activities, and expectations that society considers appropriate for males or females.
- Pitfalls: Problems or dangers that can happen.
- Misusing: Using something incorrectly or in a way that is not intended.
- Misinterpreting: Understanding something wrongly or giving it the wrong meaning.
- Electronic health record (EHR) data: Information about a person's health stored electronically.
- Collaboration: Working together with others on a project or task.
- Variables: Things that can change in an experiment or study.
False Assumptions about Sex and Gender in Machine Learning Research
Sex and gender are two of the most important variables that researchers must consider when conducting machine learning studies. Unfortunately, false assumptions about sex and gender are deeply ingrained in the medical system, leading to binary, static, and concordant interpretations. This can lead to biased results and inadequate care for transgender individuals if not handled properly. In this perspective piece, we discuss three common mistakes made by researchers when dealing with sex/gender data: “sex confusion”, “sex obsession”, and “sex/gender slippage”. We then provide advice on how machine learning researchers can avoid these pitfalls when working with electronic health record (EHR) data.
Sex Confusion
The first mistake is referred to as “sex confusion” which occurs when researchers fail to understand what sex in a dataset signifies. For example, some datasets may use terms like male or female without specifying whether it refers to biological sex or gender identity. It is important for researchers to be aware of such nuances so that they do not misinterpret the data or draw incorrect conclusions from their analysis.
Sex Obsession
The second mistake is known as “sex obsession” which assumes that sex assigned at birth is the relevant variable for most applications. This assumption ignores other factors such as gender identity or expression which may be more pertinent than biological sex in certain contexts. As a result, research outcomes could be skewed if only one variable is used without considering other aspects of an individual's identity or experience.
Sex/Gender Slippage
The third mistake is called “sex/gender slippage” which conflates sex and gender even when only one is known. For instance, some datasets may contain information about an individual's biological sex but not their gender identity or expression; however, this does not mean that they should be assumed to have a particular gender identity based solely on their biological sex alone. Such assumptions can lead to inaccurate results since different individuals may identify differently regardless of their assigned sexes at birth..
Avoiding Methodological Pitfalls When Working With EHR Data
Given the potential implications of misinterpreting or mishandling data related to sex/gender variables in machine learning research projects involving EHRs (electronic health records), there are several steps that researchers should take in order to ensure accurate results:
- Work with Data Experts: Researchers should collaborate with experts who have experience handling such data.
- Collaborate With Clinicians: Working closely with clinicians can provide insights into how the data will be used clinically.
- Provide Clear Descriptions: Research papers should include clear descriptions regarding how the collected data was utilized.
- Acknowledge Limitations & Changes Over Time: It is also important for authors to acknowledge any limitations or changes over time regarding specific variables.
In addition, incorporating gender carefully into research projects should also be considered carefully since it encompasses multiple dimensions including identity relations roles institutionalized aspects etcetera Researchers should therefore strive towards avoiding any conflation between sexes genders while also being mindful of individual identities expressions etcetera Doing so will help produce more inclusive research outcomes better serve all patients including those who identify as transgender.
Conclusion
Overall this perspective piece emphasizes the need for machine learning researchers engage thoughtfully with questions related to both sexes genders while developing algorithms utilizing electronic health record (EHR) datasets By avoiding methodological pitfalls considering complexities associated with sexes genders researchers can create more inclusive research outcomes better serve all patients including those who identify as transgender
