Accessibility is no longer just a compliance requirement for publishers; it is a business necessity in today’s digital-first world. As the demand for digital content continues to grow, publishers must ensure that images, charts, diagrams, illustrations, and other visual assets are accessible to everyone, regardless of their abilities.

This not only promotes inclusivity but also helps publishers reach a broader audience and expand the reach of their offerings. However, creating accurate, meaningful alt text at scale is one of the most challenging tasks of accessible publishing.

Traditional manual approaches can deliver quality, nonetheless they are difficult to scale across large backlists and continuous publishing programs. Moreover, they can lead to higher production costs, slower turnaround times, inconsistent reviewer interpretations, and increasing accessibility backlogs.

This is where Human-in-the-Loop AI model can make a huge difference. Rather than positioning AI and human expertise as two competing approaches, publishers can blend their strengths to create a more scalable, precise, and effective accessibility workflow.

What Is Human-in-the-Loop AI?

Human-in-the-loop AI is an approach in which artificial intelligence does high-volume, repeatable tasks whereas human experts provide oversight where judgment, contextual understanding, or specialist knowledge is required.

For alt text, this means AI can analyze images and generate initial descriptions, while human subject matter experts review outputs, verify accuracy, and make corrections whenever necessary. The main objective is not to replace human expertise, but to direct human attention toward the areas where it creates the utmost value.

A practical example is Lumina Datamatics’ Arty.AI platform. Its workflow combines computer vision, large language models, AI-powered quality assurance, and human expertise. The platform analyzes visual elements, generates contextual alt text, evaluates outputs, and routes flagged content to human reviewers.

This approach creates a more intelligent model for AI alt text generation: automate what can be automated, while retaining human oversight where it matters most.

Why Is Alt Text Important for Publishers?

For publishers, alt text is a crucial aspect of accessible digital content and is closely connected to Web Content Accessibility Guidelines (WCAG)-compliance requirements. Publishers gradually manage thousands or even millions of visual assets across books, journals, educational resources, research publications, and digital content platforms.

Poorly described or inaccessible images can create barriers for readers who solely rely on screen readers and other assistive technologies. At the same time, manually creating descriptions for every image can impose a lot of pressure on publishing teams.

This makes image alt text for publishers both an accessibility priority and an operational challenge.

An effective workflow needs to distinguish between various types of images. Decorative images, for example, may not require alt text, while functional images need descriptions that convey their purpose. Complex visuals may require a short alt text alongside a longer description. The Arty.AI framework incorporates these distinctions into its approach to WCAG alignment.

The result is a more purposeful approach: accessibility is not about describing every visual in the same way. It’s about providing the right information to the reader in the right format.

Why Isn’t AI-Generated Alt Text Enough?

Although AI has made substantial progress in understanding images, but not every image can be accurately described through automated analysis alone. Standard images are relatively straightforward for AI systems to interpret. However, scientific illustrations, medical diagrams, charts, maps, chemical structures, mathematical notation, and multi-part figures may require deeper contextual and domain-specific knowledge and understanding.

Moreover, there is the question of context. An effective alt text should complement surrounding content rather than simply describing everything visible in an image. It must also consider the purpose of the image and accessibility requirements.

An efficacy study in autonomous alt text generation conducted on Lumina Datamatics’ Arty.AI platform demonstrates why human review remains valuable. In an initial dataset of approximately 8,000 alt texts, 31% required no edits, while the remaining 69% received changes ranging from minor stylistic modifications to substantial corrections. Some edits improved already-correct descriptions, while others addressed predictable AI limitations involving complex images and specialist terminology.

Therefore, the answer is not to abandon AI but to build a workflow in which AI-generated content is intelligently assessed and human expertise is selectively applied.

How Does Human Review Improve Accessibility?

Human review always adds a layer of contextual judgment that AI cannot always reliably provide. In an effective AI-led workflow, reviewers will not have to rewrite every description from scratch. Instead, they can focus on verifying factual accuracy, completeness, context, and specialist terminology.

The Arty.AI platform’s efficacy study describes a three-step framework that reinforces this process. First, the underlying AI architecture is improved through domain-specific taxonomic precision, ensemble models, and stronger WCAG alignment. Second, an AI-powered QA agent evaluates generated alt text against parameters such as reading difficulty, contextual relevance, writing style, language bias, word length, and unsupported characters. Outputs that fall outside acceptable thresholds are flagged for human review.

Third, reviewer training ensures that human intervention focuses on substantive issues rather than personal stylistic preferences. The framework distinguishes substantive edits such as correcting nouns, verbs, relationships, or information, from stylistic edits that change phrasing without changing the meaning.

This creates a key feedback loop. Human corrections can reveal where AI needs improvement, while AI reduces the amount of routine work that requires human attention.

Which Publishers Benefit From AI-Assisted Alt Text?

Publishers managing large and diverse volumes of visual content can benefit largely from AI-assisted alt text. They are particularly:

• Academic and STM (Scientific, Technical, and Medical) publishers: Working with scientific illustrations, medical diagrams, charts, maps, chemical structures, and other complex visuals.
• Educational publishers: Producing textbooks and learning resources that depend heavily on diagrams, figures, and data visualizations.
• Trade publishers: Managing large backlists and high volumes of fiction and nonfiction content.
• Research publishers: Handling specialist content where accuracy and domain-specific terminology are essential.
• Digital-first publishers: That need scalable accessibility workflows across multiple formats and platforms.

For these organizations, the challenge is not just generating more alt text. It is creating high-quality descriptions consistently while managing volume, cost, compliance, and turnaround time.

Conclusion

The future of accessibility will increasingly depend on intelligent collaboration between AI systems and human experts and not just AI working alone.

The experience documented in Lumina Datamatics’ AI maturity journey whitepaper demonstrates the potential of this approach. The efficacy study conducted on Arty.AI platform combines computer vision, large language models, AI-powered QA, and targeted human intervention to progressively increase automation while maintaining quality. The platform’s first-pass alt text acceptance rate improved from 31% to 73%.

This model points toward a bigger opportunity for publishers: using AI accessibility solutions not simply to produce content faster, but to create smarter accessibility operations.

The secret to better alt text isn’t choosing between AI and humans. It’s knowing how to make them work better together to deliver substantial outcomes.