5.2.1. Comparing medical education & engineering design education

Education, in the scope of medical education and engineering design education, includes tasks from admission into an educational program to acting as a faculty member in educational programs. Bias has been described and identified in medical school admissions (Griffin & Wilson Reference Griffin and Wilson2010; Capers et al. Reference Capers, Clinchot, McDougle and Greenwald2017). Prompted by a lack of diversity in medical schools, Capers et al. (Reference Capers, Clinchot, McDougle and Greenwald2017) investigated racial bias in medical school admissions and found that admissions committee members demonstrated an implicit white preference (Capers et al. Reference Capers, Clinchot, McDougle and Greenwald2017). The admission committee members included faculty and students. The findings showed that regardless of gender or role, all committee members demonstrated this white preference bias implicitly but not explicitly. While the format of the admission process in engineering design differs from medical school admissions, bias may also exist in the application and admittance process of engineering programs. An implicit racial bias may exist in the admissions procedures or admissions committees of engineering programs, impacting the diversity of engineering designers. The implicit white preference racial bias was found to be one of the factors that influence diversity in medical school. Capers et al. (Reference Capers, Clinchot, McDougle and Greenwald2017) noted that the academic class admitted after the Implicit Association Test (IAT) intervention was the most diverse in the school’s history, supporting the influence of racial bias on diversity in education.

The reported white preference by admission committees is reflected in the interviews conducted for admission into medical programs (Griffin & Wilson Reference Griffin and Wilson2010). During the admissions process for medical school, applicants undergo multiple mini-interviews. Griffin & Wilson (Reference Griffin and Wilson2010) found that the interviewers show leniency interviewer bias toward interviewees based on the personality and gender of the interviewee (Griffin & Wilson Reference Griffin and Wilson2010) by being more permissive and sparing toward the interviewee (Griffin & Wilson Reference Griffin and Wilson2010). This bias is reflected in the interviewer’s evaluations of the interview even though the bias demonstrated was small. Evaluations such as these may occur in interviews of applicants for engineering programs. Though interviewing may not occur in some programs, leniency bias can also show up in the review of applicant materials. Interviewer leniency bias for interviewees of similar gender and personalities can cause engineering programs to lack diversity based on gender as well as mindsets. Engineering and STEM programs are known for a lack of diversity and inclusion (Briggs Reference Briggs2017; Jones et al. Reference Jones, Williams, Whitaker, Yingling, Inkelas and Gates2018; Botella et al. Reference Botella, Rueda, López-Iñesta and Marzal2019). For 2019, the American Society of Engineering Education reported that 18.1% of tenured/tenure-track STEM faculty positions were held by women and 2.5% were held by African Americans (Roy Reference Roy2020). Accounting for leniency interviewer bias and implicit preference bias may shine a light on ways to achieve a more diverse population of engineers (Wulf Reference Wulf1999; Du & Kolmos Reference Du and Kolmos2009; Daly et al. Reference Daly, Seifert, Yilmaz and Gonzalez2016). Biases in the engineering programs not only impact the students but also the faculty (Moss-Racusin et al. Reference Moss-Racusin, Sanzari, Caluori and Rabasco2018). Gender and racial bias, as well as leniency bias toward a specific gender, impact women and racially marginalized engineers, affecting their sense of belonging and readiness for participation (Moss-Racusin et al. Reference Moss-Racusin, Sanzari, Caluori and Rabasco2018). Therefore, it is important to be aware of any biases that occur in the admissions process of engineering design programs, but also any bias that occurs among faculty in those programs.

Another area in education where bias may exist is the curriculum (Dijkstra, Verdonk, & Lagro-Janssen Reference Dijkstra, Verdonk and Lagro-Janssen2008; Schofferman Reference Schofferman2015; Benoit et al. Reference Benoit, Travis, Swan Sein, Quiah, Amiel and Gowda2020). Dijkstra et al. (Reference Dijkstra, Verdonk and Lagro-Janssen2008) recognized that gender bias existed in the field of medicine, and therefore investigated if it was reflected in medical textbooks (Dijkstra et al. Reference Dijkstra, Verdonk and Lagro-Janssen2008). It was found that medical textbooks show gender bias, lacking gender-specific body and social environment information relevant to good medical practice. Textbooks act as resources for learning, but they also act as datasets used to make informed medical decisions and conclusions. In engineering design, bias may exist in the design textbooks used. Textbooks are used to teach engineering design and also act as a resource that provides tools for executing engineering design (Pahl & Beitz Reference Pahl, Beitz and Wallace1996; Otto & Wood Reference Otto and Wood2000; Dieter & Schmidt Reference Dieter and Schmidt2013). A bias in textbooks could lead to biased design practices and a limited perspective when conducting design. A gender bias may exist in engineering design textbooks as well, and this should be assessed in further detail.

In addition to textbooks, Benoit et al. (Reference Benoit, Travis, Swan Sein, Quiah, Amiel and Gowda2020) demonstrated that bias exists in medical training (Benoit et al. Reference Benoit, Travis, Swan Sein, Quiah, Amiel and Gowda2020). When discussing normal and healthy bodies in medical training, white bodies and features were often used which could imply that different bodies are not healthy. For example, in a lecture on gum health, pink gums were provided as an example of healthy gums. However, healthy gums can also be brown or purple in people with darker skin tones. Benoit et al. (Reference Benoit, Travis, Swan Sein, Quiah, Amiel and Gowda2020) sought to provide a framework on how to remove such implicit biases from the curriculum. Though engineering design may not use human bodies as the basis of healthy and unhealthy in the way it is used in the medical field, similar scenarios may occur in the engineering design curriculum when referring to customers or end-users of a design solution. When teaching students to design for customers or intended users, it is important to not bias their approaches by providing example design solutions that reflect certain populations. For example, the curriculum should not only provide example design problems and solutions that reflect traditional engineering space because that may bias designers to focus solely on providing a specific and familiar solution but may not fit well within the intended user’s environment or expectations. This was the case with crash test dummies for car collision safety testing (Gupta Reference Gupta2021). The test dummy represented an average size white male, resulting in safety data that was inaccurate for drivers of different body types, particularly women. This may show up in methods taught to collect information on the end-user, or methods taught to evaluate the final design, making it important to investigate implicit bias present in design methods. Design methods are crucial in guiding the designer in the design process, so understanding the bias present in the methods can highlight if and why bias exists in design outcomes.

Schofferman (Reference Schofferman2015) also demonstrated that bias may exist in medical education with the source of bias being industry-funded medical education curriculums (Schofferman Reference Schofferman2015). Continuing medical education (CME) programs that are financially supported by industries have the potential to subconsciously bias leadership professionals and educators. This bias can be caused or enhanced by marketing strategies used because industry participants funded by CME programs are more likely to use the sponsoring company’s product in training. This can occur at CME programs as well as at CME conferences. Being exposed to specific products in training impacts the participant’s or attendee’s choice of drugs and devices used in practice, potentially affecting patient care. A similar scenario may occur in engineering design education regarding industry-funded engineering design programs, projects or courses. Industry-funded programs, courses and projects often use content and tools provided by the industry funder. The educational institution may have limited control of the project focus, materials taught, and the design tools and equipment used. Though students often seek out these industry-funded programs or projects, this involvement can unintentionally bias the student designers. Pinzur (Reference Pinzur2020) also addressed bias in industry-sponsored educational programs, specifically looking at a specialized society that educates medical professionals: the American Orthopaedic Foot & Ankle Society (AOFAS) (Pinzur Reference Pinzur2020). Pinzur detailed that within AOFAS educational program presentations, presenters avoided highlighting competitors’ products, showing a selection bias in the educational information presented. Before material was presented, there was also selection bias in the papers or presentations accepted, with research being rejected if the committee members disagreed with the treatment advocated, or the device or implant used. Pinzur’s study highlights how different education avenues can be biased as well as unintentionally teach bias.

In engineering design education, this bias may occur at industry-sponsored conferences or educational programs. Stakeholders may limit sharing content that does not align with the industry funder, limiting the shared knowledge within the large engineering design community. Conferences and education programs offered at conferences are often the places in the engineering design community where students, researchers and educators can network. If the places that act as collaborative educational spaces for engineering design are prone to be biased, this may influence existing biases designers have as well as unintentionally limit the expansion of engineering design. It is important to understand that bias exists in industry-sponsored education programs, on both the small and the large scale. As detailed before, in engineering design education, bias has not been explored as extensively as in medical education. Bias studied in medical education highlights what may influence or induce bias in engineering design education as well as the gaps in engineering design education bias research.

5.2.2. Comparing medical practice & engineering design practice

The second domain explored is the practice of medicine or engineering design. The scope of this study covers actions, tasks and events that occur when practicing medicine or executing engineering design. The process of engineering design can be divided into subsequent phases as seen in Figure 1. Using the established phases in Figure 1, bias in design practice was pinpointed. The establishing the need phase is when the designers begin to collect information on the intended user or intended design space to identify the need, problem or scope of the project. In this stage, a lot of information searching, collection and processing occurs, similar to when medical providers take a past medical history or search for information to diagnose a disease. Medical research has identified many biases that may also be found to influence actions and tasks that may show up in the first stage of the design process. In the establishing needs phase, the designer is oftentimes interacting directly with the intended end-user, or indirectly by collecting information from resources on the end-user. Bias research in the field of medicine shows that provider bias influences this similar interaction in medical practice: physician–patient interaction and treatment recommendations (Sieverding et al. Reference Sieverding, Schatzkin, Shen and Liu2018). Sieverding et al. (Reference Sieverding, Schatzkin, Shen and Liu2018) found that fertility care medical providers exhibited a bias toward patients with certain marital statuses, interfering with the approach used to collect patient information and recommended management, and ultimately affecting the quality of care (Sieverding et al. Reference Sieverding, Schatzkin, Shen and Liu2018). This bias may be seen in engineering design as a designer bias toward the intended end-user. Designers may have preconceived notions when collecting information on the intended user directly or indirectly, affecting the interpretation of the information collected as well as their perceived design solution. In addition to designers exhibiting bias because of the end-users’ demographic status, designers may exhibit bias because of the end-users’ race (Laidley et al. Reference Laidley, Domingue, Sinsub, Harris and Conley2019).

Laidley et al. (Reference Laidley, Domingue, Sinsub, Harris and Conley2019) found that providers’ racial bias influences their diagnosis of the patient (Laidley et al. Reference Laidley, Domingue, Sinsub, Harris and Conley2019). Despite patients being siblings or having similar backgrounds, their skin tone was found to be a predictor of hypertension, with darker skin tone patients having a greater likelihood of being diagnosed with hypertension (Laidley et al. Reference Laidley, Domingue, Sinsub, Harris and Conley2019). While the intended users in engineering design efforts usually have a shared aspect that groups them as the target customers, projects with racial differences among target users may shine a light on any racial biases held by designers. For example, designers may be attempting to provide a design solution to end-users of a community or organization with similar racial identities as their community. However, if these designers were attempting to provide the same design solution to a community or organization of end-users with racial identities that were vastly different from their community, would their approaches change? When designers interview the end-users or use techniques to uncover end-users’ needs, there may be a bias that the designer holds that drives how the needs and expectations of the end-user are interpreted, their understanding of the problem and potentially the solution the designer suggests.

In the field of medicine, racial bias refers to reoccurring bias that negatively impacts the provider’s interpretation of the patient’s needs as well as the prescribed care (Hoffman et al. Reference Hoffman, Trawalter, Axt and Oliver2016; Plews-Ogan et al. Reference Plews-Ogan, Bell, Townsend, Canterbury and Wilkes2020a). One very important example is that racial bias exhibited by medical providers has caused these providers to inaccurately assess the patient’s level of pain (Hoffman et al. Reference Hoffman, Trawalter, Axt and Oliver2016). Racial bias plays a role in the interpretation of information as well as the synthesis of this information. In all stages of engineering design, but particularly in the establishing needs phase, racial bias impacts how the designers assess information shared by the intended user or information collected from the intended user and the design space can lead to the development of design solutions that fail. Being knowledgeable and aware of the racial bias by the designer that exists in engineering design can shine a light on how racial bias influences the first stage of the design process, which is a crucial design stage.

Another cognitive bias that can occur in the first stage of the engineering design process is utilization bias. Aberegg et al. (Reference Aberegg, Arkes and Terry2006) identified that physicians demonstrate a bias leading them to forgo new beneficial information because of their need to not abandon their current practices even if their practices are not as beneficial (Aberegg et al. Reference Aberegg, Arkes and Terry2006). Though the design process is iterative, and designers gather new information throughout the design process, a bias like this can be harmful if exhibited in the early stage of gathering information and establishing the needs. In design, this may be expressed as a hindrance to designers when learning new methods to collect information or new approaches to evaluating information, or even when discovering new areas to collect information that can benefit their understanding of the end-user, the needs and project scope. It is important to understand if this bias exists among designers, how it is expressed and what effects it has on a successful design.

Two biases that exist in medical practice that may surface in the analysis of task phase are hindsight bias and weight bias (Persky & Eccleston Reference Persky and Eccleston2010; Arkes Reference Arkes2013). Hindsight bias is the tendency to exaggerate the extent to which a past event could have been predicted (Arkes Reference Arkes2013; Saposnik et al. Reference Saposnik, Redelmeier, Ruff and Tobler2016). Arkes’ (Reference Arkes2013) literature review on the consequences of hindsight bias in medical decision-making found that hindsight bias affects learning and also leads to overconfidence when practicing medicine, which can result in malpractice (Arkes Reference Arkes2013). Medical decision-making and the analysis of task phase have several paralleling tasks. Hindsight bias and overconfidence in the analysis of task phase can lead to the designer’s misinterpretation of the design problem and poor execution of design. Interpersonal biases may also influence medical decision-making. Persky and Eccleston (Reference Persky and Eccleston2010) found that physician recommendations differed based on the weight of the patient, suggesting that physicians demonstrate a weight bias (Persky & Eccleston Reference Persky and Eccleston2010). Though the weight of a patient is assumed to be correlated to a person’s health in the medical field, in design, weight or any visual appearance of the target user may impede the designer’s decision-making. Designers may unconsciously exhibit an interpersonal bias toward their end-users based on the end-user’s appearance or specific features, essentially an appearance or ability bias. This bias may be noted in the questions used to solicit information for the design project as well as in how the designer interprets that information. The bias may also show up when developing a product based on biased data sets such as weight and height data of a population (Nordqvist Reference Nordqvist2022). Weight is an important factor to consider when developing an ergonomic change, but if BMI tables are used as a standard to determine chair limits and constraints based on user weight, it will result in a biased design solution because BMI tables are inherently biased. An interpersonal bias that stems from someone’s appearance can hinder the execution of the analysis of task phase if the designer is not aware that they hold such bias. This bias prevents the designer from understanding the design space or user needs fully.

The last four design phases are the conceptual design phase, embodiment design phase, detailed design phase and implementation design phase. All four phases have different objectives, but each phase shares similar tasks such as ideation, evaluation, selection, documentation, analysis and other cognitive tasks. One bias that occurs in medical design, testing, regulation and product use that may also occur in engineering design is gender bias. Hutchison (Reference Hutchison2019) found that there was a gender bias in the design and testing of hip implants due to the consideration and use of only male bodies and male-centered kinesiology (Hutchison Reference Hutchison2019). The gender-biased hip implant design led to increased implant failures for female patients (Hutchison Reference Hutchison2019). This bias directly relates to engineering design in practice because medical practitioners participated in designing the hip implant. In engineering design, gender bias may unknowingly occur if the metrics used to evaluate, test and demonstrate designs only use male participants. It is important to take into account the different gender demographics of the indented users when designing, to minimize design failure. Gender bias may be seen in the metrics used to evaluate the performance and functions of the design solution. Taking into account any gender biases in the final design stages can ensure the functionality of the final design solution for all the intended users.

In addition to design, testing and implementation, gender bias can also inform the selection of design concepts. Stålnacke et al.’s (Reference Stålnacke, Haukenes, Lehti, Wiklund, Wiklund and Hammarström2015) study identified that males had a significantly higher chance of being prescribed physiotherapy and radiological examination than female patients when presenting with chronic musculoskeletal pain (Stålnacke et al. Reference Stålnacke, Haukenes, Lehti, Wiklund, Wiklund and Hammarström2015). This task is similar to the designer collecting information and selecting the best concept for the intended user or customer. Designers need to be aware of any gender bias or other interpersonal bias that exists when selecting concepts. One way designers can explore if a gender bias exists in this regard is to assess past projects for any trends that may suggest a gender bias.

Another bias that can influence selection or decision-making in the design process is racial bias. Green et al.’s (Reference Green, Carney, Pallin, Ngo, Raymond, Iezzoni and Banaji2007) study on the decision to treat patients found that physicians’ implicit racial bias plays a significant role in the likelihood of not treating black patients (Green et al. Reference Green, Carney, Pallin, Ngo, Raymond, Iezzoni and Banaji2007). This implicit bias may show up in engineering design unknowingly, especially when designing for users of different racial backgrounds which often occurs in humanitarian design or global development (Avgerou Reference Avgerou2010). Engineering design is primarily conducted by white males, so there is a possibility that design thinking and decision-making may be biased when designers are designing with or designing for people of marginalized racial groups.

Medical research suggests confirmation bias may exist in these design stages (Mendel et al. Reference Mendel, Traut-Mattausch, Jonas, Leucht, Kane, Maino, Kissling and Hamann2011). Confirmation bias is the tendency to confirm a favored hypothesis (Tschan et al. Reference Tschan, Semmer, Gurtner, Bizzari, Spychiger, Breuer and Marsch2009; Elston Reference Elston2020; Nelius et al. Reference Nelius, Doellken, Zimmerer and Matthiesen2020). Mendel et al. (Reference Mendel, Traut-Mattausch, Jonas, Leucht, Kane, Maino, Kissling and Hamann2011) found that psychiatrists and medical students exhibited confirmation bias in their information search leading to diagnostic inaccuracies (Mendel et al. Reference Mendel, Traut-Mattausch, Jonas, Leucht, Kane, Maino, Kissling and Hamann2011). This situation in medicine is similar to the establishing the needs phase as well as the conception selection phase in engineering design. Confirmation bias in a designer may impact the information the designer searches trickling down to the methods used to develop and implement the design solutions. Confirmation bias may also influence the designer’s ability to change their selection, decision or understanding, all being important actions throughout the design process.

In the final design phase, the implementation phase, the final design solution is transferred to the intended user by producing the product to scale, commercialization and mass production if needed. Documentation associated with the final design is also developed as a supplemental resource for users as well as other designers. Phillips, Wassersug, & McLeod (Reference Phillips, Wassersug and McLeod2012) study identified that documentation bias in supplemental pharmaceutical literature was linked to patients being less informed on drug side effects (Phillips et al. Reference Phillips, Wassersug and McLeod2012). Some of the drivers for this documentation bias were due to the funding sources for the work. Documentation bias can unknowingly occur in engineering design as funding sources or stakeholders may unintentionally limit the information included in the final documentation. This bias can systematically influence what information is conveyed, available or made explicit to the end-user and other designers. Potential conflicts of interest may exist and unknowingly bias the content in the resulting documentation or supporting literature.

Though the design process can be divided into six different phases, most tasks are not exclusive to a single design phase. For example, decision-making, selection and even interviewing can occur in multiple phases of the design process. Biases that occur during medical decision-making are biases that can occur in all phases of the engineering design process. Hershberger et al. (Reference Hershberger, Markert, Part, Cohen and Finger1996) investigated cognitive bias in medical decision-making and found that both physicians and medical students demonstrate cognitive bias, but physicians were less susceptible (Hershberger et al. Reference Hershberger, Markert, Part, Cohen and Finger1996). Student and professional designers may also exhibit cognitive bias in design decision-making. Decision-making tasks in design include selection, synthesis, evaluation, production, specification and many additional tasks that require design thinking. It is important to understand if cognitive bias plays a role in these tasks as it does in the field of medicine. It is also important to understand the variance of cognitive bias based on experience level or other designer traits.

Elston (Reference Elston2020) provided a review of cognitive bias, specifically confirmation bias, in medical decision-making (Elston Reference Elston2020). Confirmation bias in the medical context is the tendency to give greater weight to data that supports a preliminary diagnosis while neglecting contradictory evidence (Elston Reference Elston2020). This bias may occur in all stages of the design process. When collecting project or end-user information in engineering design, a confirmation bias may impede the designer’s judgment if they are refusing to collect any information that may not support their findings. This may also occur with concept selection or even evaluation and synthesis of new information regarding conceptual design. The designer may disregard any information that suggests that the selected concept is not the best solution or fail to even consider such information due to a confirmation bias. Age plays a role in exhibiting confirmation bias, with older individuals being more likely to exhibit this bias. It is important to understand where confirmation bias is seen in design, its impacts, as well as the variance among designers based on experience as well as age, gender and race.

Saposnik et al.’s (Reference Saposnik, Redelmeier, Ruff and Tobler2016) systematic review of the cognitive bias research in medical decision-making highlighted common cognitive biases that occur during management, treatment, diagnosis and/or prognosis (Saposnik et al. Reference Saposnik, Redelmeier, Ruff and Tobler2016). These biases include anchoring, availability bias, blind obedience, commission bias, confirmation bias, diagnostic bias/premature closing, framing effect, omission bias, overconfidence, tolerance to risk and satisfying bias (Saposnik et al. Reference Saposnik, Redelmeier, Ruff and Tobler2016). Though these biases occur and were studied in medical scenarios, they may also occur in engineering design scenarios. Management refers to how tasks within the scope are handled and regarded and is directly comparable to the management of tasks throughout the engineering design process. How is information handled? How is work within design teams distributed and executed? All these are ways where bias in the management of the design process can come into play. Management can be influenced by outcome bias, framing effect, anchoring or confirmation bias. It is important to understand this concept in the management of and within design projects. Medical decision-making, such as treatment and diagnosis, can be extrapolated to various design stages. Diagnosis is the interpretation of information to determine the presence or absence and quality of a disease in a patient. In engineering design, this can be compared to data interpretation from the end-user, in the early stages or prototype testing results and feedback from the final design stages. Overconfidence, outcome bias, anchoring or confirmation bias, and information bias have all been described in medical diagnosis, and may potentially occur in similar engineering design cognitive tasks. Treatment is the action that follows diagnosis. The emphasis is how the medical professional decides to provide a solution for their diagnosis. This may mimic the engineering design route the designer selects to use in the early design stages or even the approach used to produce and implement the final design. Framing effect, feedback bias and outcome biases can impact treatment in medical decision-making, so it likely impacts similar tasks in engineering design. Prognosis is the medical decision-making task of predicting the likelihood of a certain outcome of a disease or ailment. Though not as evident, prognosis is comparable to designers predicting the success or feasibility of a concept based on knowledge or with the use of tools to aid design. Simply, prognosis is the action of predicting an outcome. Though there are many design tools to assist in the design process and provide indicators that help make decisions, often design progresses to the next stages because of a designer’s prediction of a solution or concept’s success. If not successful, the designers investigate the problem and implement changes accordingly. The iterative nature of the design process partially relies on the prediction of the designers. Though tools and external resources have been developed to mitigate the human error of prediction, it is important to understand how cognitive bias in prediction shows up in engineering design.

Biases influencing decision-making not only are exhibited by individuals but can also be demonstrated by teams (Tschan et al. Reference Tschan, Semmer, Gurtner, Bizzari, Spychiger, Breuer and Marsch2009; Helzer et al. Reference Helzer, Myers, Fahim, Sutcliffe and Abernathy2020). Tschan et al.’s (Reference Tschan, Semmer, Gurtner, Bizzari, Spychiger, Breuer and Marsch2009) study suggests that confirmation bias exists in collaborative diagnosis and explicit reasoning within medical teams occurred more in groups that made the correct diagnosis (Tschan et al. Reference Tschan, Semmer, Gurtner, Bizzari, Spychiger, Breuer and Marsch2009). Collaborative diagnosis in medicine parallels team ideation and concept selection in engineering design. Confirmation bias may exist in collaborative ideation or concept selection if design teams are not explicitly stating their reasoning, potentially leading to design solutions that fail. Bias in collaborative teams can also stem from team members’ gender bias. Helzer et al.’s (Reference Helzer, Myers, Fahim, Sutcliffe and Abernathy2020) study highlighted that participants relied more on treatment advice delivered by a male physician versus a female physician (Helzer et al. Reference Helzer, Myers, Fahim, Sutcliffe and Abernathy2020). It was also demonstrated that participants’ reliance on the advice from female physicians increased based on the physician’s experience, and this was not the case for male physicians (Helzer et al. Reference Helzer, Myers, Fahim, Sutcliffe and Abernathy2020). This gender bias may occur when team members contribute information and even when instructors or managers offer advice to design teams. Designers may have a bias that prevents them from accepting helpful information due to the gender of the person providing the information. It may also be worth investigating if the experience of the person providing information influences how designers accept and use the information.

Research on bias in the medical field shows that medical providers can demonstrate bias, but some work also suggests the patient can exhibit bias. Levy and Hershey (Reference Levy and Hershey2008) study explored patient bias and found that in close-call medical decisions, patients may exhibit value-induced bias (Levy & Hershey Reference Levy and Hershey2008). Value-induced bias is when someone distorts relevant probabilities to justify decisions, ultimately wishful thinking (Levy & Hershey Reference Levy and Hershey2008). Though in engineering design, most projects are not risky close calls like in some medical scenarios, end-users may exhibit this bias when tasked with decision-making when interpreting information conveyed by the stakeholder. The motivation to justify one’s decision and preference may lead people to distort their perception of relevant possibilities. Levy’s findings suggest that it is important to investigate the bias not only among the designers but also among the end-users. Research on bias in the practice of medicine highlights potential biases when executing engineering design that has not been investigated.

Bias research in engineering design has primarily explored biases that impact the practice or execution of design (Stacey et al. Reference Stacey, Petre, Rzevski, Sharp and Buckland1996; Toh et al. Reference Toh, Strohmetz and Miller2016; Nelius et al. Reference Nelius, Doellken, Zimmerer and Matthiesen2020). Stacey et al. (Reference Stacey, Petre, Rzevski, Sharp and Buckland1996) explored design tool bias in concept selection with the implementation of a design tool that mitigates and utilizes design tool bias to aid in concept selection (Stacey et al. Reference Stacey, Petre, Rzevski, Sharp and Buckland1996). This study highlights that design tools can be biased but that also bias can be beneficial to design outcomes. Toh et al. (Reference Toh, Strohmetz and Miller2016) explored the variance of ownership bias in concept selection based on the designer’s gender (Toh et al. Reference Toh, Strohmetz and Miller2016). Though gender bias was not explored, Toh found that male designers are more likely to demonstrate ownership bias than female designers (Toh et al. Reference Toh, Strohmetz and Miller2016).

5.2.3. Comparing medical research & engineering design research

One final domain of engineering design where bias may show up is in the process of engineering design research. Many aspects of engineering design are investigated in engineering design research such as designer protocols, effects of design solutions, tools that aid design, patterns in the design process, creativity and ideation, design thinking, and even documentation during the design process(Cardella, Atman, & Adams Reference Cardella, Atman and Adams2006; Maier & Fadel Reference Maier and Fadel2009; Caldwell Reference Caldwell2011; Linsey et al. Reference Linsey, Clauss, Kurtoglu, Murphy, Wood and Markman2011; Van Bossuyt & Dean Reference Van Bossuyt and Dean2016; Kannengiesser & Gero Reference Kannengiesser and Gero2017). As previously mentioned, Hutchison (Reference Hutchison2019) identified a gender bias in the design, testing, regulation and use of hip replacement (Hutchison Reference Hutchison2019). Though this is considered a bias in design practice, it also highlights a bias that may exist in engineering design research because of the research that goes into product design. Research of design solutions consists of the development, testing and implementation of design solutions (Paleta, Pina, & Santos Silva Reference Paleta, Pina and Santos Silva2014; Pinto et al. Reference Pinto, Wong, Fennesy, Tang and Compere2016). If a gender bias exists within these stages, developing and testing with a specific user in mind can lead to failed design solutions such as the hip replacement in Hutchison’s case. This lack of inclusion highlights another bias that can show up in design research: participation bias.

Participation bias is when the participants of clinical trials fail to reflect the demographics of the patient populations (Murthy, Krumholz, & Gross Reference Murthy, Krumholz and Gross2004). Murthy et al. (Reference Murthy, Krumholz and Gross2004) found that elderly and racially and gender-minoritized groups were less likely to enroll in cooperative group cancer trials than white participants, men and younger patients (Murthy et al. Reference Murthy, Krumholz and Gross2004). Though clinical trial studies differ from engineering design studies, a participation bias can be a factor in design research. Many engineering design studies are conducted at the institutional level, using undergraduate or graduate-level engineers as participants in those studies. The demographics of students enrolled in these engineering departments need to be considered to understand if a participation bias occurs. Most students enrolled in these engineering departments are white, male students, thus enabling the researcher to only collect data on design thinking that reflects the perspectives of younger white males (Roy Reference Roy2019, Reference Roy2020). It is important to understand how participation in clinical studies plays a role in biasing the data we used to understand design. Race and age-based disparities in medical research participation may be the result of social and economic disparities or other systematic factors such as racism; however, one’s race is not a health risk factor. In design, particularly design research, race and sex disparities may influence design outcomes as these factors may influence decision-making in the design context (Helzer et al. Reference Helzer, Myers, Fahim, Sutcliffe and Abernathy2020).

Another research bias that occurs in the medical field is reporting bias. Reporting bias refers to any bias that impedes what and how research findings are reported and published (McGauran et al. Reference McGauran, Wieseler, Kreis, Schüler, Kölsch and Kaiser2010). Some examples of reporting bias include publication bias, time-lag bias, multiple publication bias, location bias, citation bias, language bias and outcome reporting bias (McGauran et al. Reference McGauran, Wieseler, Kreis, Schüler, Kölsch and Kaiser2010). McGauran et al.’s (Reference McGauran, Wieseler, Kreis, Schüler, Kölsch and Kaiser2010) review of reporting bias in medical research identified that many manufacturers and regulatory agencies withheld study data or attempted to suppress publications (McGauran et al. Reference McGauran, Wieseler, Kreis, Schüler, Kölsch and Kaiser2010). McGauran also found that reporting bias oftentimes revolved around the overestimation of efficacy and the underestimation of safety risks of interventions (McGauran et al. Reference McGauran, Wieseler, Kreis, Schüler, Kölsch and Kaiser2010). This study highlights how the nature and direction of the results can bias how the findings are reported. Reporting bias may occur in design research if the results go in a certain direction. Oftentimes, failed design efforts are not reported, which highlights a reporting bias in unsupportive findings. It may be beneficial to highlight how often studies go unreported because of the nature of the results. In addition to no reporting based on the nature of the results, reporting bias may also inform how research is documented. Researchers may oversell findings or withhold findings to achieve publication. This is an important phenomenon because reporting and publishing research findings are the primary way research is shared within the design community. Publishing is important for designers to understand what research is being conducted in the field but is also important for the expansion of engineering design as a field.

Delgado-Rodríguez and Llorca ( Reference Delgado-Rodríguez and Llorca 2004 ) reviewed the bias that impacts research in the medical field, identifying an extensive list of biases that influence medical research (Delgado-Rodríguez & Llorca Reference Delgado-Rodríguez and Llorca2004). These biases and the biases mentioned previously can be explored in engineering design by investigating the presence in the research pipeline. For the context of this study, the research pipeline refers to the funding, stakeholders, researchers, participants, data sets, tools, conferences, publications and other entities involved. Exploring these biases in these spaces will begin to close the gap in bias research in engineering design research.