Intersectionality and Testimonial Injustice in Medical Records (2023)

Abstract: Detecting testimonial injustice is an essential element of addressing inequities and promoting inclusive healthcare practices, many of which are life-critical. However, using a single demographic factor to detect testimonial injustice does not fully encompass the nuanced identities that contribute to a patient’s experience. Further, some injustices may only be evident when examining the nuances that arise through the lens of intersectionality. Ignoring such injustices can result in poor quality of care or life-endangering events. Thus, considering intersectionality could result in more accurate classifications and just decisions. To illustrate this, we use real-world medical data to determine whether medical records exhibit words that could lead to testimonial injustice, employ fairness metrics (e.g. demographic parity, differential intersectional fairness, and subgroup fairness) to assess the severity to which subgroups are experiencing testimonial injustice, and analyze how the intersectionality of demographic features (e.g. gender and race) make a difference in uncovering testimonial injustice. From our analysis we found that with intersectionality we can better see disparities in how subgroups are treated and there are differences in how someone is treated based on the intersection of their demographic attributes. This has not been previously studied in clinical records, nor has it been proven through empirical study.

## introduction in medical settings, decisions can have life-critical consequences @xcite @xcite @xcite , making it essential to ensure that machine learning tools use there are fair. this fairness is often measured with common fairness metrics such as demographic parity @xcite and equal opportunity @xcite . however, these tools do not consider the intersectionality of the subjects under considera-tion @xcite . that is, by focusing solely on factors such as race, gender, or socioeconomic status, we ignore the nuances related to individuals with unique experiences shaped by having multiple features sensitive to marginalization. we theorize that how various aspects of an individual intersect and contribute to their experiences, via intersectionality, could make instances of injustice more overt -and in some cases may be the sole approach for identifying such instances. intersectionality recognizes that power relations based on factors such as race, class, and gender are not mutually exclusive and can interact with each other, affecting all aspects of the social world @xcite . therefore, it is important to consider intersectionality when evaluating the fairness of machine learning tools in medical settings. in clinical settings, it is particularly important that care providers (e.g. physicians) properly acknowledge what their patients are hoping to convey to them in a way that does not diminish what the patient is saying. moreover, it is imperative for care providers to accurately relay their understanding of their patients' experiences, as others will be dependent upon their previous understandings and evaluations, often recorded in notes, to assist with overseeing and providing care for that patient @xcite . we have seen that when this does not occur, there are higher instances of death amongst certain marginalized groups @xcite . with the rise in using machine learning tools to help make decisions on medical plans and treatments, who often only interact with the notes provided to them and not the actual patient, it is vital they are able to properly see patients. this visibility should be clear despite previous attempts at burying their words behind instances of injustices which hides them as a speaker. here, we focus on a particular form of injustice -testimonial injustice. testimonial injustice occurs when someone is assigned less credibility due to prejudices about them @xcite . the aim of our study is to examine how testimonial injustice in medical records is affected by the intersectionality of gender and race. these two observable attributes have historically led to marginalization in various societal settings, such as education @xcite , housing @xcite , and healthcare @xcite . in fact, some forms of marginalization may only be evident in those with multiple marginalized identities -for instance, a black police woman may not experience the same level of power and privilege as a white male police officer @xcite . neglecting to consider the various contributing identities of an individual may further marginalize them. therefore, it is important to consider intersectionality when identifying and addressing injustices in order to result in more accurate classifications and decisions. there has been a small amount of work done to understand testimonial injustice in medical records and to our knowledge no prior work on how intersectionality might affect the emergence of testimonial injustice, even in life-critical medical settings. this motivates our contributions to this work: (1) the importance of intersectionality has been spoken about but has not been shown before (particularly in the medical setting). thus, we perform an empirical study to show there is a difference in how subgroups are treated in medical settings, but this can only be revealed in intersectional views. (2) practitioners continue to use singular-feature fairness metrics in medical settings. thus, we provide proof that we should not be using these metrics to detect instances of injustice. this proof has not been provided before, not even in medical settings. thus, we (3) perform an empirical study to show traditional fairness metrics (i.e. demographic parity) are inefficient when judging people's experiences in healthcare because they produce different results when the entirety of a person is considered. (4) lastly, not all metrics fit each situation -even in similar settings. therefore, we analyze if different intersectional fairness metrics might reveal differences in how we recognize intersectionality. previous studies have shown that both black patients and female patients are more likely to experience testimonial injustice in the medical field, as evidenced by the use of biased language in their records @xcite . however, these stud-ies have not examined the specific impact of intersectionality, or how being simultaneously black and female might affect testimonial injustice. our work seeks to address this gap by examining the impact of the intersection of ethnicity (black, asian, latino, and white) and gender (male and femalethough we acknowledge in modern society, there is recognition of genders beyond the traditional binary options, the dataset used here only includes these two genders) on testimonial injustice in medical records. ## related works despite the increased use of machine learning tools and a growing focus on intersectionality in the medical community @xcite , there have been limited efforts to understand how intersectionality can impact outcomes in medical settings. since various healthcare professionals rely on medical records to make treatment decisions and give proper care, it is crucial that such records are written appropriately @xcite . the authors of @xcite found that even when race is removed from patients' records, models could detect the race of the patient -even when humans could not. furthermore, they discovered that models trained on these records (i.e. which race has been removed from) still maintain biases in treatment recommendations. though they only remove race in their work, this further affirms that there are differences in how patients are spoken about in their records based on demographic features, emphasizing the need to study what can occur if we look at multiple demographic features as we do here. in their work, p goddu et al. explored how stigmatizing language in a patient's medical record can shape the attitudes of physicians-in-training towards the patient and their clinical decision-making. they found that stigmatizing language is associated with more negative attitudes and less aggressive pain management. building on this work, we examine words that may indicate testimonial injustice, which occurs when someone's statements are diminished due to stereotypes or prejudices about them @xcite . it is therefore important to identify instances of stigmatizing language in medical records and take steps to prevent them from occurring as emphasized by @xcite , the authors use a lexicon look-up to identify testimonial injustice in medical records, analyzing the use of quotation marks, evidential words, and judgmental words in the records of male and female patients who are black or white. we expand their work, including words that are negative and commonly used stigmatizing words in medical settings. we exclude the search for quotation marks, acknowledging that direct quotations may give rise to uncertainty by suggesting that the statement in question constitutes not a fact, but rather an assertion @xcite . however, we believe that our expanded lexicon will help to identify instances of testimonial injustice. further in contrast to beach et al., we consider the records of black, white, asian, and latino patients, exploring how testimonial injustice may differ across the intersection of their identities with gender. the authors found that black and female patients are most likely to experience testimonial injustice, highlighting the need to examine how different intersectional identities impact experiences of testimonial injustice in medical settings. previous research has examined the presence of epistemological bias in medical records based on sensitive attributes to detect instances of experiences injustice i.e. disparate treatment. himmelstein et al. studied diabetic patients and found that non-hispanic black patients were more likely to have stigmatizing language included in their notes than non-hispanic white patients. similarly, sun et al. investigated medical records and racial bias, discovering that black patients had a 2.54 times higher chance of negative descriptors than white patients. these studies suggest that certain demographics may experience differential treatment in medical settings, which may help explain healthcare disparities. however, these works only examined single demographic features, while we seek to investigate their intersection. we anticipate that studying the intersection of groups will more clearly reveal instances of injustice or discrepancies in treatment. the ongoing use of tools that do not consider intersectionality highlights the importance of this research @xcite . guo and caliskan developed a technique to automatically identify intersectional biases from static word embeddings. they found that their model's highest accuracy was for predicting emergent intersectional bias among african american and mexican american women. this could be attributed to these groups experiencing more overt biases that are easier to detect. this discovery motivates us to further investigate if biases are more prevalent in high-risk settings such as medical settings, especially for individuals from marginalized groups. however, it can be challenging for humans to identify when a bias is occurring since it can be subtle, as highlighted by hube and fetahu. furthermore, doctors may struggle to recognize their own use of words that cause testimonial injustice since they may be unconsciously influenced by their own biases and take them as facts @xcite . ## methods although all marginalized groups invariably experience some degree of injustice, our aim is to bridge the gap in research by highlighting the disparate treatment of subgroups in medical notes. to achieve this goal, we estimate and compare common metrics across different groups (i.e. asian men, asian women, black men, black women, latino men, latina women, white women, and white men) specifically using demographic parity, differential intersectional fairness, and subgroup fairness. ## results when examining the results for demographic parity, we solely focus on instances of race or gender, as this approach only allows for an assessment of one factor at a time. however, for differential fairness and subgroup fairness, we conduct an intersectional analysis with race and gender. for these, we look to see which groups have privilege over another, meaning one group experiences less testimonial injustice in their physicians' notes as opposed to the group they are being compared to. ## discussion when conducting experiments using demographic parity, we compared race or gender. in each case, there were no violations of demographic parity for any patient is treated based on their race or gender alone. if a practitioner takes these results for face value, they might determine there is no form of discrimination happening based on these commonly observed visible attributes. for example, when speaking to a black male patient who was stigmatized against from the demographic parity view, they would have no evidence in that setting to back their expression of their experience. however, when we look deeper, through the lens of intersectional fairness (i.e., differential fairness and subgroup fairness) at the intersection of race and gender, we can see that a male patient can still experience discrimination (i.e. black males) and so could a white patient (i.e. white females). when we look at measures that consider intersectionality, we see disparity in how people are treated based on their race and gender for every type of word we analyzed that could lead to testimonial injustice. we attribute this to: (1) being able to consider multiple aspects about a person that might only reveal themselves at the intersection of race and gender, (2) in differential fairness being able to constrain the range in which we look for violations, as opposed to only looking at it from one side as demographic parity does. to properly see injustices occurring, we must look at all angles from which they could possibly be coming from. this is because someone might only be testimonially injust toward a person who is female, others might only act unjustly because of your membership with a historically marginalized race, and so on. we contend that the better metrics to use for detecting injustices, e.g. testimonial injustice, in medical records are ones which consider intersectionality. still, we see differences in how these measures show which groups are experiencing privilege, thus we must be careful in understanding the goals of the fairness metrics we use. ## conclusions the objective of this empirical study was to investigate the potential benefits of intersectionality in detecting testimonial injustice, using medical records as a real-world application. demographic parity, differential intersectional fairness, and subgroup fairness were used to examine whether there are differences in the extent of testimonial injustice experienced by individuals based on the intersection of their demographic attributes and if intersectionality helps reveal this. our results showed (1) when we allow ourselves to use metrics that consider intersectionality, as opposed to sole factors of who a person is, we can better see disparities in how they are treated in terms of detecting testimonial injustice in medical records, (2) there are differences in how someone is treated based on the intersection of their demographic attributes (3) different intersectional fairness metrics do reveal these injustices differently. while demographic parity did not show a clear disparate impact based on gender or race, differential intersectional fairness and subgroup fairness -two intersectional fairness measures -revealed that there was disparate treatment based on both gender and race. these findings suggest that intersectionality should be considered when detecting testimonial injustice, especially in medical settings.