#### WHAT TO KNOW: AI-GENERATED HIGHLIGHTS

##### 01. GenAI in healthcare risks deepening health inequities due to biases from non-diverse development teams that create the tools and by non-inclusive training data

##### 02. Klick’s Strategy team advocates for training GenAI with diverse inputs and values to combat bias and support equity

##### 03. Cross-cultural marketing via GenAI can help reduce disparities by providing inclusive strategies and marketing content

##### 04. Ethical GenAI requires diverse data sets, targeted collection, and culturally aware teams to truly enhance health equity

---

#### EXECUTIVE SUMMARY

The increasing use of generative AI
(GenAI) in healthcare exacerbates the
risk of perpetuating biases, which can
intensify existing health inequities. The
biases in GenAI are often linked to a
lack of diversity in the development
teams and training data. Recognizing
this challenge, Klick’s Strategy team
emphasizes the importance of “good
parenting” in the development of GenAI
tools, promoting exposure to diverse
cultures and instilling values of equity
and inclusion.

Cross-cultural marketing is essential for
addressing healthcare disparities, but it
remains underutilized in pharmaceutical
marketing due to awareness gaps
and budgetary concerns. GenAI tools
can help bridge the gaps and lower
the barriers, leading to more culturally
informed marketing strategies.

The future success of GenAI in promoting
health equity depends on concerted
efforts to diversify training data, conduct
targeted data collection, and build
diverse development teams. With ethical
development and application, GenAI
tools hold the potential to significantly
advance inclusive marketing and
improve health equity. However, this
positive outcome is conditional on
overcoming substantial challenges in the
development process, requiring careful
optimism and proactive measures.

Image 1

#### INTRODUCTION 

As GenAI technologies become deeply  integrated into every aspect of life, the  biases they perpetuate in healthcare risk  exacerbating an already unequal healthcare  landscape. For example, [an algorithm widely  used by hospital networks used healthcare  spending as a proxy for healthcare needs and, in doing so, grossly underestimated the  needs of Black people vs. white people.](https://www.scientificamerican.com/article/racial-bias-found-in-a-major-health-care-risk-algorithm/)

The well-documented bias in GenAI tools  has sparked widespread concern over the  negative effects of these technologies. For  example, an examination of AI-generated  images of “professionals” illustrated how they  reinforce pernicious stereotypes: they tend  to depict high-paying occupations as white  males and low-paying occupations as people  of color, and they underrepresent women in  both categories versus actual labor statistics.

Many attribute this bias to a lack of diversity  in the technology companies responsible for  their creation and in the data sets that the tools  are trained on. Today’s GenAI tools are in their  infancy, and the companies that build them are  akin to parents. [How they “raise” these growing  children will determine whether they become  adults who replicate and reinforce bias or  become champions for equity.](https://www.bloomberg.com/graphics/2023-generative-ai-bias/) 

The Strategy team at Klick is deeply  committed to health equity and believes the  question is more than just an ethical challenge.  We will explore what “good parenting” would  look like in the context of training GenAI tools.  Are we exposing our “children” to a wide  variety of cultures, beliefs, and practices? Are  we instilling the importance of key values like  fairness, equity, and inclusion? How could  GenAI be used to help mitigate the ingrained  biases in AI-based systems and serve as an  instrument for advancing health equity and  cultural inclusivity? 

Bias in GenAI and healthcare-system inequity  are examples of the unintentional harm  that results when systems are developed  without centering inclusivity. GenAI bias  and healthcare bias are pernicious enough  independently, but their combined effect— biased algorithms operating on biased data  sets—amplifies the damage multiplicatively. 

However, what if these documented problems  presented an opportunity to show exactly  where the healthcare industry needs to focus?  Author, feminist, and civil rights activist Audre  Lorde famously said, “...the master’s tools will  never dismantle the master’s house,” but what  if they actually could? This paper explores  how we might flip AI bias on its head. 

In the realm of life sciences commercialization,  (as the market-facing expression of healthcare  companies’ commitment to health equity),  cross-cultural marketing can play an essential  role in addressing inequities within healthcare.  Effective cross-cultural marketing initiatives  can help historically underserved patient  populations learn about the health issues  that impact them, accelerate their path to  diagnosis, and promote greater adherence to  health-preserving treatments.  

To execute authentically, marketers must start  by deeply understanding the attitudes, beliefs,  behaviors, and unmet needs of their entire patient  population, especially those underdiagnosed  and undertreated. A comprehensive strategic  approach to cross-cultural marketing considers  all the facets of identity that impact individuals’  interactions with the healthcare system and  determines their health outcomes, such as  gender, race, ethnicity, socio-economic status,  and sexual identity. Ultimately, successful  cross-cultural marketing involves translating  this knowledge into insight-driven efforts like  inclusive and accessible creative, culturally  tailored patient journeys, and activation plans  and cross-cultural media initiatives. 


Image 

#### Why Isn’t Cross-Cultural Marketing  More Prevalent in Pharma? 

Although cross-cultural initiatives have become  increasingly common in consumer marketing,  they are still the exception rather than the  norm in pharmaceutical marketing. There are  several reasons for the gap. One factor is a lack  of awareness about the depth of healthcare  inequity and a lack of knowledge about the  best practices associated with cross-cultural  marketing. Even within therapeutic areas  that are known to disproportionately affect  communities of color, such as heart failure and  kidney disease, marketing efforts often rely on  executional elements like casting choices and  direct translations.  

Perhaps more importantly, finite budgets and  the perceived complexity of cross-cultural  efforts act as significant barriers. A marketer  might have the desire to address the needs  of underserved patient groups but lack the  necessary knowledge and skill set to drive such  initiatives. The same marketer faces a challenge  in hiring a separate team with specialized  expertise as it adds cost and risks, creating a  distinct and unintegrated workstream. These  perceived challenges hinder marketers from  realizing their desire to address health equity  issues, so they continue with business as usual.


#### How Can GenAI Tools Help? 

GenAI tools could play a pivotal role in overcoming these barriers by providing better access  to the insights required to guide more authentically inclusive marketing efforts. When “well  parented”—in other words trained on data sets that either include representative samples of  historically underrepresented groups or ones that have been fine-tuned to adjust for bias— advanced tools could provide marketers with the cultural understanding and quantitative data  to better understand diverse patient populations and their specific needs.  

By leveraging GenAI tools that have been “well parented” marketers could be automatically exposed to market research they may not have thought  to consult, including relevant insights and practical strategies for cross-cultural marketing they  can apply to their work. It’s critical to note that human oversight and ethical considerations are  imperative to ensure the responsible and unbiased use of these technologies. With those in place,  GenAI tools could help marketers bridge the knowledge and skill gap, enabling them to navigate  this complex terrain more efficiently and effectively. As a result, pharmaceutical companies could  be better equipped to cater to underserved patient groups and achieve more inclusive and  impactful marketing campaigns.


### USE CASES FOR GENAI TOOLS TO IMPROVE INCLUSIVITY IN MARKETING

Image 5

#### NOW: Text Analysis of Patient  Materials—The Current State 

Making patient materials easily understandable  empowers them to make well-informed  decisions about their health, helping them  actively participate in their healthcare journey.  It also plays a crucial role in promoting  health literacy by adjusting complex medical  terminology to the patient’s comprehension  level. Clear and accessible materials  contribute to patient safety by ensuring  that vital information is comprehensible and  promotes inclusivity in healthcare by catering to  individuals with diverse backgrounds, education  levels, and language proficiency.  

Unfortunately, overcomplicated language and  confusing explanations in patient materials  often flummox even native speakers. Here’s  where GenAI text analysis tools have the  potential to deliver, identify, and analyze  complex medical terminology, jargon, and  technical language, and suggest alternative,  simpler explanations. They can assess the  overall level of patient materials and provide  feedback on reading difficulty, helping  authors simplify the text to align with the  audience’s health literacy level, and deliver  recommendations on making the content more  empathetic and patient-centered. Healthcare  marketers who leverage these tools can  create materials that are easier for patients  to understand and contribute to better health  outcomes, improved adherence, and increased  patient empowerment.


Image 6

### NOW: Image Analysis 

With startling regularity, [ads appear on the  market that are offensive for their insensitive  and stereotypical representation of historically  underserved groups](https://www.youtube.com/watch?v=c3Rc_RK4crE). (As long-time cross cultural marketers, we have a digital folder full of  them!) Since the topics addressed in healthcare  marketing are often a matter of life and death,  avoiding such faux pas is even more critical.  Can GenAI tools provide a solution? Have their  “parents” raised them to do so? 

The potential to use GenAI tools for image  analysis to assess campaign and asset  imagery for underrepresentation and  misrepresentation of historically underserved  groups remains untapped. As highlighted by  the Bloomberg article cited in the introduction,  the lack of diversity in the data sets used  to train text-to-image AI tools like Stable  Diffusion leads to the generation of images  that exemplify a world of amplified bias, i.e.,  even more biased in terms of gender, race,  and ethnicity than reality itself. To return to  our analogy, GenAI “infants” demonstrate the  (noxious) impact of nurture over nature—it’s not  how they inherently are, it’s how they’ve been  “raised”. As computer scientist and AI expert  Dr. Joy Buolamwini aptly notes, “With the  adoption of AI systems, at first I thought we  were looking at a mirror, but now I believe  we’re looking into a kaleidoscope of distortion.  [Because the technologies we believe to be  bringing us into the future are actually taking  us back from the progress already made.](https://www.npr.org/2023/11/28/1215529902/unmasking-ai-facial-recognition-technology-joy-buolamwini)”

Unless these “children” are exposed broadly
and deeply to data about historically
underrepresented and underserved
populations and trained to utilize them
to improve equity, this will continue to
be the case.

For healthcare marketers to be able to utilize
GenAI tools as a safeguard against their own
unconscious biases and ingrained stereotypes,
developers who produce them would need
to become more thoughtful and inclusive
“parents,” collecting a diverse dataset of
images that encompasses individuals from
different ethnicities, races, genders, and body
types. A large multimodal model like GPT-4V
could be used to analyze a wide and diverse
array of images to create descriptions, then
categorize them based on those descriptions.
To create robust AI models, human experts in
the loop can verify accuracy.

__Automated Checks for Potential Bias__

The model could be utilized to analyze images,
detecting instances of underrepresentation
or misrepresentation of certain groups. This
analysis could identify if the images reflect
biases such as [improper labeling](https://www.youtube.com/watch?v=QxuyfWoVV98),tokenism, or
harmful stereotypes, safeguarding marketers
from cultural missteps. With increasing scrutiny
on GenAI tools and the data sets they are
trained on, one could optimistically predict that
such “well-raised” tools will be available soon.

__FUTURE:__ 

Video and Multimodal Experiences
All of the potential described above for
GenAI tools to act as bias interrupters will be
available for video in the foreseeable future, and will be able to analyze the video, the on-
screen text, and the accompanying voiceover. Given the primacy of video communication
in health, politics, the arts, etc., this will be a
powerful development.


#### CRITICAL WORK TO ENABLE GENAI-POWERED CROSS-CULTURAL STRATEGY AND MARKETING

As mentioned above, cross-cultural marketing  is a way for healthcare brands to begin  addressing inequities in their therapeutic  areas. At Klick, we want to see a future where   GenAI tools, no longer in their infancy but  thoughtfully and deliberately evolved, could   make cross-cultural approaches more widely   accessible to the healthcare industry and  
accelerate the advancement of health equity. 

__Diverse Training Data __

AI models learn from the data they are trained   on. Healthcare companies and marketers   need to advocate for collecting and utilizing   large and diverse training data for GenAI tools   and only employ tools that prioritize ethical   considerations, including data privacy, fairness,   and transparency. With those protocols   securely in place—and potentially monitored   or r[egulated](https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/)—GenAI tools could provide  healthcare marketers with critical insights about  diverse patient populations. Using diverse   training data would expose AI models to  different experiences and perspectives related  to healthcare, allowing the models to generate   more accurate insights and predictions about   various patient populations. They could help   healthcare marketers understand the unique   social determinants, preferences, and culturally   based attitudes of different communities,   equipping them to tailor their marketing   strategies and messaging to better resonate with   historically underserved patient populations. 


Image 7

AI tools could also help identify healthcare  access and delivery gaps for these populations.  Unlike the infamous algorithm previously cited  that used spending on healthcare as a proxy  for patients’ healthcare needs, thus significantly  underestimating the unmet medical needs in  the Black community, AI tools could be trained  to do just the opposite. Through data analysis,  AI models could detect disparities in healthcare  outcomes and potentially shed light on potential  solutions. This would empower healthcare  marketers to develop targeted campaigns and  initiatives to bridge those gaps and improve  healthcare access and outcomes. 

__Targeted Data Collection__

Targeted data collection is another crucial  component of “good parenting” by tech  developers—it’s a proactive approach to  consciously gather information from historically  underrepresented groups. It will be an important  way to correct for the biases currently  engrained in AI tools. Developers can ensure  that their AI models learn directly from a diverse  range of individuals by conducting surveys,  interviews, or user studies specifically designed  for these populations. This approach is crucial  because it will begin to capture the unique  insights, experiences, and preferences of  underrepresented groups, thus avoiding biases  and improving AI systems’ overall fairness and  accuracy. Developers can create more inclusive  and representative models by actively seeking  out diverse data. 

Additionally, this approach could help correct the current gaps in marketers’ understanding   of historically underserved populations and  
provide valuable insights into their needs and  
perspectives. 

An example of targeted data collection is  Latimer, otherwise known as “The Black ChatGPT,” which builds on current AI techs like Meta’s Llama 2 model and GPT-4, the large  language model that powers ChatGPT, but  adds in books, oral histories, and local archives  from Black and brown communities that are  not well-represented in the data sets those  models were trained on. Another example is  Mozilla’s Common Voice project, which was  created to address the lack of representation  of non-English speakers, people of color,  disabled people, women, and LGBTQIA+ people  in voice recognition data sets. For GenAI tools  to become enablers of health equity-focused  
marketing, initiatives like these must become the  norm, not exceptions.  

__Augmenting Data with External Sources __

Another “good parenting” approach to data  acquisition that will be important in training  GenAI tools is augmenting data with external  sources. In this approach, developers enhance  existing data sets by incorporating additional  information. This can mean integrating data  from different data sets or including data  obtained from publicly available sources,  including government databases, social  media platforms, or third-party data providers.  


Image 8

This can enrich the existing dataset, provide  additional context, or fill in critical missing  information (e.g., historically underrepresented  and underserved populations). 

Some beneficial augmentation sources from a  health equity perspective: 

• *Academic research*, particularly studies  addressing the healthcare attitudes,  beliefs, behaviors, and unmet needs of  underserved populations, with a wide  lens across the impacts of gender, race,  ethnicity, socio-economic status, sexual  identity, geography, etc.  

• *Sociological studies on the socio-cultural  factors that influence under-resourced  communities and their health outcomes*,  to equip AI systems with a more nuanced  understanding of diverse populations’  behaviors and preferences  

• *Community-driven platforms, such as  forums, social media groups,* and online  communities, allow individuals to express  their unfiltered thoughts, experiences,  and concerns. Analyzing data from these  platforms can give AI systems real-time  information and perspectives from the  communities that healthcare marketers  need to better understand 

• *Reports and studies conducted by  organizations focused on diverse*  
*populations* (examples include leading  advocacy organizations like UnidosUS, the  National Urban League, and the NIH, which  collects large, diverse healthcare data sets  in its All of Us research initiative) could also  contribute to augmenting AI’s knowledge.  These organizations often conduct research in their communities and collect  data to better advocate for their rights. By  leveraging their findings, AI algorithms could  access up-to-date information and develop  a more comprehensive understanding of  the challenges and opportunities faced by  underrepresented groups. 


__Building and Collaborating with Diverse Teams__

At the beginning of this paper, we referenced  the lack of diversity within tech companies  as a frequently cited cause of the biases  within AI tools. [The reverse is also true.](https://hbr.org/2020/10/to-build-less-biased-ai-hire-a-more-diverse-team) By  bringing together individuals from diverse  backgrounds, especially those with personal  knowledge and lived experience, teams can  benefit from their distinct perspectives and  insights. Diverse team members play a crucial  role in identifying blind spots and potential  biases that arise during the design and  development of AI systems. They can provide  valuable human input on potential biases and  the cultural/social contexts that AI systems  cannot fully understand on their own. These  insights could help ensure that AI systems  are built to be more inclusive and consider  the needs (especially those that are unmet),  values, and perspectives of all individuals. In  short, diverse teams are the “parents” that AI  tools, now in their infancy, require to develop  into mature tools that can serve as powerful  equity drivers.  


#### CAUTIOUS OPTIMISM AND CAVEATS

The effects of bias in GenAI tools are pernicious  and wide-reaching, as some examples in this  paper have illustrated. If AI tools are in their  infancy, these “infants”—with all their blind  spots and lack of perspective—yield enormous  clout. We’ve expressed optimism about the  opportunity for the tech industry to be thoughtful  and responsible “parents” to these “infants” and  evolve them into “well-educated adults” that  advance inclusion and health equity.  

But that outcome is not a given and faces  headwinds such as the tremendous and  potentially prohibitive computing resources  required for data transparency and the  dependency that creates on well-funded  tech giants; the opaque and confusing paths  through which algorithms can entrench  historical inequities; and the need for a  potentially non-scalable amount of highly  contextual human judgment to reliably  interrupt bias within GenAI tools. However,  the stakes are high, and the potential is great. 

---

*We welcome your questions and feedback. Please contact: __Michael Chambers__ SVP, Opportunity Creation at mchambers@klick.com*


This POV outlines how generative AI can be leveraged to create marketing materials that promote health equity, emphasizing the importance of cross-cultural strategies in reaching diverse patient populations. Our detailed analysis explores the importance of diverse development teams and training datasets, as well as the potential for GenAI tools to lower the barriers to cross-cultural marketing strategies. It underscores the fact that health equity outcomes are contingent on targeted proactive measures to address current gaps and biases. This approach underlines Klick’s commitment to pioneering more equitable healthcare solutions.

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