Algorithmic Accessibility

Artificial intelligence has transformed digital marketing, powering everything from personalized product recommendations to automated email campaigns. But there's a critical question many marketers aren't asking: are these AI systems designed with neurodivergent users in mind?

For the 15-20% of the population who are neurodivergent – including autistic individuals, those with ADHD, dyslexia, and other cognitive differences – AI algorithms often create unintentional barriers rather than opportunities for engagement. This represents not only a significant accessibility issue but also a massive missed market opportunity for brands.

This article explores how AI-powered marketing tools can be reimagined to better serve neurodivergent users, with practical examples, expert insights, and actionable strategies for more inclusive algorithmic design.

The Current Algorithmic Landscape: Designed for Neurotypical Users

Most AI marketing tools operate on datasets and assumptions that reflect neurotypical patterns of engagement, communication, and decision-making. This creates several significant issues for neurodivergent users:

Problem 1: Content Recommendation Engines Miss Special Interests

Autistic individuals often have deep, focused interests in specific topics. However, most content recommendation algorithms interpret prolonged engagement with a narrow topic as a temporary interest spike that should be balanced with variety.

Real-world example: A major streaming platform's algorithm will start "diversifying" recommendations after detecting sustained viewing in a specific category, assuming the user wants variety. For an autistic viewer deeply engaged with documentaries about a special interest, this algorithm actively works against their natural viewing preferences, creating frustration.

Problem 2: Personalization Based on Neurotypical Emotional Responses

AI tools frequently use sentiment analysis to gauge user responses, but these systems are typically trained on neurotypical emotional expression patterns.

Real-world example: A popular email marketing platform uses AI to analyze subject line effectiveness based on emotional impact. The algorithm favors emotionally charged language that may be overwhelming or confusing to autistic readers who process emotional language differently. Marketers following these AI recommendations may inadvertently create content that alienates neurodivergent audiences.

Problem 3: User Experience Optimization Based on Majority Behaviors

Heat mapping and user experience AI tools typically optimize for the "average user," whose behavior reflects neurotypical patterns.

Real-world example: A major ecommerce platform's AI-driven layout optimization tool prioritizes elements that capture attention from the majority of users. This can result in sensory-overwhelming designs with movement, contrasting colors, and unpredictable elements that create significant barriers for users with sensory processing differences.

Reimagining AI for Neurodivergent Accessibility

Creating more accessible AI isn't just ethically sound—it's also good business. Here are specific ways AI marketing tools can evolve to better serve neurodivergent users:

1. Inclusive Algorithm Training Data

Current problem: Most AI systems are trained on datasets that underrepresent or completely exclude neurodivergent interaction patterns.

Solution: Deliberately include neurodivergent users in training datasets.

Implementation example: Pinterest has begun incorporating diverse user interaction patterns in their recommendation engine development. They discovered that what they had previously flagged as "outlier" behavior (intense focus on specific categories for extended periods) was actually representative of many neurodivergent users. By adjusting their algorithm to recognize these patterns as valid rather than anomalous, they improved user satisfaction across all user groups.

Action for marketers: When selecting AI marketing tools, ask vendors about the diversity of their training data. Request information specifically about inclusion of neurodivergent interaction patterns.

2. User-Controlled Algorithmic Preferences

Current problem: Most AI systems make assumptions about user preferences rather than providing transparent controls.

Solution: Offer explicit preference settings that allow users to control their algorithmic experience.

Implementation example: Spotify has implemented "fine-tuning" controls that allow users to explicitly tell the algorithm when they want more content similar to what they're currently enjoying versus when they want variety. This feature was originally designed for general personalization but has proven especially valuable for neurodivergent users who often prefer predictability and pattern-based recommendations.

Action for marketers: Advocate for and implement clear user controls in your AI-powered marketing tools. Create options for users to indicate whether they prefer:

• High predictability vs. novelty
• Deep exploration of specific topics vs. breadth
• Literal vs. figurative language in communications

3. Sensory-Aware Content Delivery

Current problem: AI-powered content delivery rarely accounts for sensory processing differences.

Solution: Incorporate sensory profile options into content delivery algorithms.

Implementation example: Microsoft's Xbox accessibility team has developed an AI system that can calibrate game content based on sensory preference profiles. Users can indicate sensitivity to flashing lights, sudden sounds, or high-contrast visuals, and the system adjusts content delivery accordingly. This same approach could revolutionize marketing content delivery.

Action for marketers: Include optional sensory preference settings in your digital marketing platforms. Allow users to indicate preferences for:

• Static vs. animated content
• Audio on/off by default
• Color contrast preferences
• Text density preferences

4. Pattern Recognition That Values Neurodivergent Engagement

Current problem: AI often misinterprets neurodivergent engagement patterns as disinterest or confusion.

Solution: Expand pattern recognition to include neurodivergent engagement signals.

Implementation example: Google's accessibility team has developed AI models that recognize diverse reading patterns, including those common among dyslexic users. Rather than interpreting certain reading patterns (like rereading sentences multiple times) as confusion or disinterest, the system recognizes these as legitimate processing approaches and adjusts content delivery accordingly.

Action for marketers: Review your analytics interpretation. What are you currently classifying as "bounce" or "disengagement" that might actually represent different engagement patterns? Consider developing alternative success metrics that account for neurodivergent interaction styles.

5. Communication Style Adaptation

Current problem: AI-powered communication tools (chatbots, email generators, etc.) typically use neurotypical communication patterns.

Solution: Develop AI communication tools that can adapt to different communication preferences.

Implementation example: A pioneering customer service platform called Conduit has developed an AI system that can detect when users prefer direct, literal communication versus more conversational styles. The system adjusts its language patterns accordingly, providing clear, concise instructions without idioms or figurative language when that pattern is detected.

Action for marketers: Train your AI communication tools (like chatbots) to offer communication style options. Allow users to select whether they prefer:

• Direct, literal instructions
• Step-by-step processes
• Visual/text/audio communication options
• Explicit rather than implied information

Case Study: Inclusive AI in Action

Let's look at this in the real world.

How Wistia Rebuilt Their Video Analytics for Neurodiversity

Video marketing platform Wistia provides an excellent example of how rethinking AI through a neurodiversity lens can benefit all users.

Their standard video analytics used AI to interpret viewer engagement, classifying behaviors like rewatching sections multiple times as "confusion points" that content creators should simplify or clarify. However, after consulting with neurodiversity experts, they realized this interpretation missed important nuances.

For many neurodivergent viewers, rewatching content multiple times might indicate:

• Deep interest in the specific information
• A different processing style that benefits from repetition
• A thorough approach to information consumption

Wistia rebuilt their AI analysis system to offer multiple interpretations of viewing patterns, providing content creators with a more nuanced understanding of how different viewers might be engaging with their content. The result was a more sophisticated analytics platform that benefited all users, not just neurodivergent ones.

Key takeaway: Reframing "problems" as "differences" led to innovation that improved the product for everyone.

Practical Implementation: Making Your Marketing AI More Accessible

Now here are some great ways to apply these principles to your own marketing projects.

1. Audit Your Current AI Tools

Start by evaluating the AI-powered marketing tools you currently use:

• Do they offer user controls for algorithmic preferences?
• Can users indicate communication style preferences?
• Do they account for different sensory needs?
• Are recommendation engines flexible enough to accommodate focused interests?

2. Ask the Right Questions When Selecting AI Tools

When evaluating new AI marketing tools, add these questions to your RFP process:

• What populations were included in your training data?
• Have you specifically considered neurodivergent users in your algorithm development?
• What accessibility features are built into your AI systems?
• Do you have neurodivergent individuals on your development team?

3. Supplement AI with Direct Feedback

Even the most sophisticated AI can't replace direct input from neurodivergent users:

• Create an accessibility feedback channel specifically for algorithmic issues
• Conduct user testing with neurodivergent participants
• Partner with neurodiversity-focused organizations for ongoing consultation

4. Customize Your AI Implementations

Most AI marketing tools allow for customization. Prioritize these adjustments:

• Add clear preference settings for communication styles
• Create options for predictability vs. variety in recommendations
• Build sensory profile options into content delivery settings
• Develop alternative success metrics that value different engagement patterns

5. Consider Hybrid Approaches

Sometimes the best solution combines AI with human oversight:

• Use AI to flag potential accessibility issues for human review
• Create override options for algorithmic recommendations
• Develop AI systems that learn from how human customer service agents interact with neurodivergent customers

The Business Case for Algorithmic Accessibility

Adapting AI systems for neurodivergent accessibility isn't just about inclusion—it makes sound business sense:

1. Market expansion: The neurodivergent community represents a significant consumer segment with substantial purchasing power.
2. Reduced abandonment: Accessible algorithms reduce frustration and cart abandonment among neurodivergent users.
3. Innovation advantage: Solving for edge cases often leads to breakthroughs that benefit all users.
4. Brand loyalty: Neurodivergent consumers show particularly strong brand loyalty to companies that meet their needs effectively.
5. Competitive differentiation: As digital accessibility becomes more regulated, companies with accessible AI will have a competitive advantage.

Examples of Accessible Algorithm Features That Benefit Everyone

The concept of the "curb-cut effect" applies powerfully to algorithmic accessibility. Just as curb cuts designed for wheelchair users benefit parents with strollers and travelers with luggage, these AI adaptations help all users:

1. Clear communication options: Options for literal, direct communication benefit international users and those in a hurry, not just neurodivergent individuals.
2. Sensory profile settings: Controls for animation and audio benefit users in quiet environments or with limited bandwidth.
3. Predictability in recommendations: Many neurotypical users also appreciate algorithm consistency rather than constant novelty.
4. Transparent user controls: Everyone benefits from understanding how their data is being used to make recommendations.
5. Multiple information formats: Offering textual, visual, and audio versions of the same content helps all users process information according to their situational needs.

Future Directions: Where Algorithmic Accessibility Is Heading

The field of accessible AI is rapidly evolving. Here are emerging trends to watch:

1. AI that Recognizes and Adapts to Individual Processing Styles

Rather than requiring users to self-identify preferences, advanced AI is beginning to recognize individual processing patterns and adapt automatically.

Example in development: Microsoft Research is developing natural language processing tools that can identify when a user's communication style suggests they might prefer literal language. The system then automatically adjusts its responses accordingly.

2. Multimodal AI Systems

Next-generation AI will seamlessly integrate text, visual, and audio processing to accommodate different information processing preferences.

Example in development: Google's Project Relate is building AI that can simultaneously present information in multiple formats, allowing users to engage with content in whatever mode works best for their cognitive style.

3. Cognitive Load Optimization

AI systems are beginning to measure and optimize for cognitive load—the mental effort required to process information.

Example in development: A startup called Cognixion is developing AI that can detect signs of cognitive overload and automatically adjust content delivery to prevent information overwhelm.

4. Collaborative Filtering That Embraces Neurodiversity

Traditional collaborative filtering assumes similar users have similar preferences. Next-generation recommendation engines will account for neurodivergent preference patterns.

Example in development: Netflix is exploring recommendation algorithms that can identify and serve "interest clusters" that might not follow typical genre patterns but reflect deeper content characteristics that appeal to specific cognitive styles.

A Call for Algorithmic Inclusion

As AI continues to reshape marketing, we have a unique opportunity—and responsibility—to ensure these powerful tools serve all users, including the neurodivergent community.

By reimagining algorithms with neurodiversity in mind, marketers can create more accessible digital experiences while simultaneously expanding their market reach and driving innovation.

The question is no longer whether we should make AI accessible to neurodivergent users, but how quickly we can transform our systems to embrace the full spectrum of human cognitive diversity. The brands that lead this transformation won't just be more inclusive—they'll be more successful.