In today's automated world, artificial intelligence (AI) is transforming how businesses operate, including alt text generation—creating text descriptions for images to ensure accessibility for people using screen readers. While AI can process large volumes of data quickly, businesses often face challenges when relying on unsupervised AI to generate alt text for extensive product catalogs. Unsupervised AI works without human oversight, identifying patterns in raw data, which can be useful for scaling but often leads to inaccuracies in industries like e-commerce. This post explores those challenges and how our AI-powered tool combines automation with human expertise to deliver accurate, detailed, and accessible alt text.
Limitations of Unsupervised AI
While unsupervised AI has impressive processing capabilities, it often falls short when tasked with generating accurate alt text for large product catalogs. Several limitations make this approach problematic when applied in practice.
Difficulty in Understanding Nuanced Product Details
One of the primary challenges of using unsupervised AI is its inability to grasp nuanced product details. AI models typically rely on image recognition algorithms to generate alt text, but these algorithms struggle with more complex or subtle aspects of products. For example, an unsupervised AI might describe a watch as simply "a watch," missing out on critical information such as the brand, material (leather or metal), or even the intricate design of the dial.
In industries like fashion or home décor, where unique product features often drive purchasing decisions, the lack of nuance can result in alt text that is too vague to be useful. For instance, a luxury handbag may have distinguishing stitching patterns or a unique color gradient that a human can easily recognize and describe, but which may be entirely missed by AI.
Risks of Generating Inaccurate or Generic Alt Text
Another key issue is the risk of unsupervised AI generating inaccurate or overly generic alt text. In e-commerce, accuracy is vital not just for user experience but also for compliance with accessibility standards. Unsupervised AI can easily misinterpret images—labeling a blue shirt as black or failing to differentiate between similar products like different models of smartphones. Inaccuracies like these can lead to confusion and frustration for users relying on assistive technologies, negatively impacting their experience.
Moreover, because unsupervised AI lacks context, it may generate generic descriptions such as "image of a product" or "photo of an item" rather than meaningful details that provide value to the end user. This not only diminishes the accessibility of the site but also harms the brand by making its content appear impersonal and out of touch with users' needs.
Inability to Grasp Context or Industry-Specific Terminology
Context is everything when it comes to alt text. Unsupervised AI often struggles to apply the appropriate context, especially when dealing with industry-specific terminology or products that require detailed descriptions. For example, in a catalog of electronic components, a human expert would know to differentiate between terms like "capacitor" and "resistor" based on the image and context, but an AI model might incorrectly use these terms interchangeably.
The lack of contextual understanding also extends to brand-specific requirements. For example, a clothing retailer may use terms like "athleisure" or "couture," which are difficult for AI to interpret correctly without specific programming. As a result, businesses that rely solely on unsupervised AI risk creating alt text that doesn’t reflect their brand’s unique voice or product line.
Why Human Expertise Matters
The limitations of unsupervised AI make a compelling case for integrating human expertise into the alt text generation process. Humans play an essential role in refining the output of AI models, ensuring that the generated alt text is not only accurate but also meaningful and compliant with accessibility standards.
Ensuring Accurate, Brand-Specific Descriptions
Humans have the ability to provide context that AI lacks. For example, in a fashion catalog, a human reviewer can ensure that the AI correctly identifies fabric types, color palettes, or even cultural references in clothing design. This human input is critical in producing alt text that aligns with a brand’s specific language and aesthetic.
Maintaining Compliance with Accessibility Standards
Alt text must meet specific criteria to be compliant with regulations such as the Web Content Accessibility Guidelines (WCAG). Human oversight ensures that the descriptions meet these standards, avoiding potential legal risks and ensuring that the content is accessible to all users. Humans can also ensure that descriptions avoid biases, providing a more inclusive experience for diverse user groups.
Addressing Biases and Avoiding Misleading Information
AI models are trained on existing data, which can often contain biases. For example, an AI trained predominantly on Western fashion catalogs may have difficulty accurately describing traditional clothing from other cultures. Humans are better equipped to spot these biases and provide corrections, ensuring that the alt text reflects accurate, unbiased representations.
Combining AI with Human Oversight
At our company, we recognize that the best approach to alt text generation involves blending the speed and scalability of AI with the contextual understanding and nuance that human experts provide. This hybrid model allows us to tackle the limitations of unsupervised AI while leveraging its strengths for large-scale catalog processing.
Improving Accuracy Through Contextual Refinement
A11y Alt generates initial alt text based on image recognition, but human experts step in to refine and enhance this text. For example, if the AI generates the description "a black dress," a human expert can add valuable context by specifying the fabric, cut, and occasion (e.g., "a black satin evening dress with a halter neck"). This process ensures that each description is detailed, accurate, and useful to the end user.
Identifying Patterns of Error or Bias in AI Outputs
By combining human oversight with AI, we can also identify patterns of error or bias in the AI-generated content. For instance, if the AI consistently struggles with identifying certain types of products, our team can intervene to provide corrections, improving the model's future performance. This iterative process helps improve the overall quality and reliability of the AI's outputs.
Collaborating to Create Accessible, User-Friendly Alt Text
Ultimately, our hybrid approach ensures that the alt text generated is not only accurate but also enhances the user experience for individuals with disabilities. Human experts ensure that each description is meaningful and aligned with accessibility standards, while AI enables us to scale this process efficiently across large catalogs.
Conclusion
The challenges of using unsupervised AI for large product catalogs highlight the critical role of human expertise in generating high-quality alt text. While AI, like our A11y Alt tool, excels in scalability and efficiency, it requires human oversight to ensure accurate, context-aware descriptions. A11y Alt bridges this gap by combining powerful AI-driven automation with expert refinement, resulting in alt text that is not only detailed and compliant but also tailored to specific brand needs. With A11y Alt, businesses can confidently meet accessibility standards while enhancing user experience, making their products more accessible to all.