Experience Bias in Design: Uncovering & Addressing the Issues

Addressing Experience Bias in Digital Design

A recent discussion with a major technology firm centered on whether their commitment to human-centered design effectively prevents experience bias. The concise response is that it likely does not.

When referring to experience bias, the focus isn't on individual cognitive biases. Instead, it pertains to the digital interface itself – encompassing design and content. Most applications and websites are constructed based on the perspectives and capabilities of their creators, or tailored for a select group of key users.

Understanding the Core Issue

If users are unfamiliar with established design patterns, possess limited digital literacy, or lack the necessary technological access, the experience can be considered biased against them. This creates inherent barriers to usability and inclusivity.

A viable solution involves adopting a strategy where organizations develop multiple design iterations. These versions should be specifically customized to cater to the requirements of a diverse user base.

The Challenges Within Empathetic Design

While investments in empathetic design are crucial, as highlighted in my conversation with the technology company, certain underlying issues must be acknowledged. These are often overlooked by those who launch and oversee design operations.

One significant challenge is that UX and design teams frequently receive narrowly defined target user specifications from strategic or business departments. This is where experience bias often originates.

The Role of Business Priorities

If a user group isn't prioritized by the business, the design team won't be granted the authorization or resources to develop experiences tailored for them. Consequently, even with a focus on human-centered design or design thinking, iterations are often based on user profiles driven by commercial considerations.

This approach frequently fails to align with a comprehensive understanding of diversity, encompassing factors like culture, race, age, income, ability, and language.

Limitations of Traditional Design Processes

Another critical point is that human-centered design often presumes that all UX, services, and interfaces are designed by humans. If the goal is to mitigate experience bias through customized variations based on user needs, this manual UI approach is insufficient.

Especially considering the potential lack of diversity within the teams responsible for creation, a fundamental shift in design processes is required. Alternatively, leveraging machine learning and automation in the creation of digital experiences becomes essential – a necessary step towards achieving experience equity.

Understanding and Resolving Experience Bias

Successfully addressing experience bias necessitates first identifying its potential presence. Several key questions can assist in pinpointing areas where bias may exist within your digital platforms:

Content Clarity: Is the Information Accessible to Everyone?

Numerous applications necessitate specialized technical knowledge, employ industry-specific terminology, or presume a certain level of user expertise.

Consider financial services or insurance websites – often, an understanding of their specific terms and industry jargon is assumed. As traditional support from agents or bankers diminishes, digital experiences must effectively translate complex information for users.

UI Usability: Does the Interface Accommodate Diverse Abilities?

Can individuals with disabilities navigate the interface using assistive technologies? Is the user expected to invest significant effort in learning the UI? A user’s navigational needs can vary greatly depending on their abilities and context.

For instance, designs intended for an aging population should prioritize clear text and minimize subtle visual cues. Conversely, younger demographics often respond well to color-coding and established design patterns. Reflect on the problematic COVID-19 vaccine websites that placed the burden of navigation and appointment booking on the user – or the inconsistent navigation across different banks for similar tasks. While startups once boasted simple UIs, the addition of features often leads to complexity, even for experienced users, as evidenced by Instagram’s evolution over the past five years.

Ecosystem Integration: Are Users Responsible for Seamlessly Connecting Multiple Experiences?

Our online lives rarely revolve around a single website or application; we utilize a collection of tools to accomplish our daily tasks. Most digital businesses aim to retain users within their own ecosystem, often overlooking the other tools a user might be employing.

When dealing with health concerns, individuals may need to interact with insurance providers, hospitals, doctors, and banks. New college students may navigate multiple school systems, alongside vendors, housing services, and financial institutions. Users are frequently blamed for difficulties arising from integrating disparate experiences.

Inherited Biases: Are You Relying on Systems That Generate Content, Utilize Pre-existing Design Patterns, or Employ Machine Learning for Personalization?

If so, how do you guarantee these approaches deliver appropriate experiences for your target user? Utilizing content, UI elements, and code from other systems can inadvertently introduce existing biases. The proliferation of AI-powered content and copy generation tools exemplifies this – any bias present in these systems will be incorporated into your experience.

To foster more inclusive and equitable digital ecosystems, new design and organizational processes are essential. While AI tools will undoubtedly play a significant role in future front-end design and content creation, five immediate steps can be taken by any organization:

Integrate Digital Equity into DEI Initiatives: Many organizations have diversity, equity, and inclusion goals, but these often don’t extend to their digital products for customers. A common issue, observed in both large corporations and digital startups, is a lack of clear accountability to diverse users throughout the organization.

Departments often compete for impact and proximity to the customer. The initial step for digital experiences or products is to define and prioritize diverse users at the business level. A clear mandate from senior leadership to establish a definition of digital and experience equity will enable each department to align its efforts. Without management support and funding, design or product teams cannot effect meaningful change, and the C-suite must be held accountable for prioritizing this.

Prioritize Diversity Within Design and Development Teams: Extensive discussion has surrounded this topic, but it remains crucial to emphasize that teams lacking diverse perspectives will create experiences reflecting their own privileged backgrounds and abilities.

It’s also vital to seek individuals with experience designing for diverse users. How is your organization modifying its hiring process to enhance diversity within design and developer groups? Who are you partnering with to source diverse talent? Are your DEI goals merely checkboxes circumvented when hiring preferred candidates? Do your agencies have robust diversity programs and expertise in inclusive design?

Google’s initiatives serve as valuable examples: it has redirected funding for machine learning courses from predominantly white institutions to a more inclusive range of schools, provided free access to TensorFlow courses, and offered free tickets to BIPOC developers for events like Google I/O.

Re-evaluate Your Testing Methods and Participants: User testing is frequently limited to the most profitable or important user segments. But how does your site function for an aging population or users who exclusively access the internet via mobile devices?

A key distinction between equity and equality in experience lies in developing and testing a variety of experiences. Design teams often test a single design and refine it based on user feedback (if testing occurs at all). Creating design variations that consider the needs of older users, mobile-only users, and individuals from diverse cultural backgrounds allows you to connect designs to digital equity goals, even if it requires more effort.

Transition from a Single Design for All to Launching Multiple Experience Versions: The conventional approach to digital design and product development is to create a single version of an experience based on the needs of the primary user base. A future with multiple iterations of apps and sites tailored to diverse users challenges the current resource allocation and workflow of most design organizations.

However, this shift is essential for prioritizing experience equity. Consider these questions: Does your site/product/app offer a version with simplified text and larger fonts for older audiences? Can mobile-only users complete tasks, or would they need to switch to a desktop computer, particularly for lower-income households?

This extends beyond responsive website design or A/B testing. Design teams should aim to launch multiple focused experiences directly linked to prioritized diverse and underserved users.

Leverage Automation to Generate Content and Copy Variations for Each User Group: Even with design variations and diverse user testing, content and UI copy are often afterthoughts, especially as organizations scale, leading to jargon-filled or overly polished, meaningless content. How does using existing marketing copy within an app impact user understanding?

If the solution to experience bias is variation in front-end design based on individual needs, automation can significantly accelerate this process. We are witnessing an explosion of new AI tools poised to revolutionize UI and content creation. The numerous copy-driven AI tools emerging in the past year, while primarily aimed at content creators, could be adapted within large brands to dynamically generate UI copy and content based on user data. Older users might receive more textual descriptions with minimal jargon, while Gen Z users could encounter more referential copy with richer imagery.

No-code platforms offer similar opportunities – WebFlow, Thunkable, and even Canva demonstrate the potential for dynamically generated UIs. While Canva’s designs may sometimes feel generic, thousands of businesses are utilizing it to create visual content instead of hiring designers.

Many companies utilize the Adobe Experience Cloud but overlook the experience automation functions within it. Ultimately, the role of design will evolve from crafting bespoke experiences to curating dynamically generated UIs – mirroring the evolution of animation in film over the past two decades.

The Evolving Landscape of Design Variation Through AI and Machine Learning

The preceding strategies focus on modifying how organizations tackle experience bias utilizing existing technologies. However, if the future of mitigating experience bias lies in the creation of diverse design and content iterations, artificial intelligence tools will become increasingly vital.

We are currently witnessing a significant surge in AI-powered content creation platforms, such as Jarvis.ai and Copy.ai, alongside automation features integrated into design tools like Figma and Adobe XD.

Emerging AI-Driven Design Capabilities

Although technology capable of dynamically generating front-end designs and content through AI and machine learning is still developing, several noteworthy examples hint at future possibilities.

Google’s recent release of Material You, a design system for Android devices, stands out. It’s designed for extensive user customization and incorporates a strong focus on accessibility.

Users have control over elements like color, typography, and layout. Furthermore, emerging machine learning features may adapt designs based on user-specific factors, including location or the time of day.

Personalization and Automation Intersections

While initially presented as empowering users with greater customization options, a closer examination of Material You reveals substantial potential for automation at the design level.

It’s also crucial to acknowledge the ongoing efforts to establish design principles and interaction guidelines for AI-driven experiences. Microsoft’s Human-AI eXperience program, for instance, provides a foundational set of interaction principles and design patterns.

This program also includes an upcoming playbook dedicated to anticipating and designing solutions for potential failures in human-AI interactions.

A Future of AI-Generated Designs

These instances suggest a future where interactions and designs are routinely generated by AI. However, concrete real-world applications remain limited at this time.

To effectively reduce bias, a significant increase in variation and personalization within front-end designs is necessary. This requirement aligns with the emerging trends at the intersection of AI and design.

The convergence of these technologies and innovative design practices will present organizations with a unique opportunity to fundamentally transform their approach to user-centered design.

Proactive consideration of experience bias is essential now, to ensure we can address it effectively as this new era of front-end automation gains momentum. Ignoring this shift could lead to perpetuated biases.