AccessibleDeepAgent

A Comprehensive Accessibility Framework for Generative AI

AccessibleDeepAgent is a complete, production-ready accessibility toolkit addressing the top accessibility challenges in generative AI systems. Built for the Humane Intelligence Accessibility Bias Bounty, it provides modular, platform-agnostic accessibility features that can be integrated into any AI application.

🎯 What's New: Expanded Accessibility Features

We've dramatically expanded AccessibleDeepAgent with 5 new comprehensive accessibility modules addressing ALL major accessibility challenges in generative AI:

🆕 New Accessibility Agents

VisualAccessibilityAgent - Alt text, color contrast, screen readers, color blindness
CognitiveAccessibilityAgent - Text simplification, reading levels, summaries, cognitive load
ContentBiasDetector - Bias detection, inclusive language, fairness analysis
MultimodalOutputAgent - TTS, audio descriptions, structured formats, Braille
NeurodiversitySupportAgent - Autism, ADHD, dyslexia, sensory support

📓 Complete Demo Notebook

Try all features instantly in our Accessibility Features Demo Notebook - works in Google Colab, Jupyter, and any environment!

Overview

AccessibleDeepAgent serves three primary purposes:

Comprehensive Accessibility Toolkit: 5 production-ready agents covering visual, cognitive, fairness, multi-modal, and neurodiversity accessibility
DeepAgent Framework: Research-grade multi-tool reasoning agent for benchmarks (ToolBench, GAIA, API-Bank, etc.)
ADK (Accessibility Development Kit): Neuroadaptive accessibility system with emotion AI bias mitigation

What Makes This Different?

✅ Complete Coverage - Addresses ALL major accessibility challenges ✅ Production-Ready - Fully tested, documented, ready for integration ✅ Platform-Agnostic - Works with any AI model or platform ✅ Demonstrable - Complete Jupyter notebook with real examples ✅ Research-Backed - Built on accessibility research and WCAG standards ✅ Modular - Use just what you need, or integrate everything

Top Accessibility Challenges Addressed

1. Visual Barriers 🎨

❌ Problem: AI-generated images lack alt text
✅ Solution: Automated alt text generation with quality scoring (3 detail levels)
❌ Problem: Poor color contrast in AI outputs
✅ Solution: Real-time WCAG AA/AAA contrast checking
❌ Problem: Color-blind users can't distinguish AI visualizations
✅ Solution: Color blindness simulation for 4 types (protanopia, deuteranopia, tritanopia, achromatopsia)

2. Cognitive Barriers 🧠

❌ Problem: Complex language excludes users with cognitive disabilities
✅ Solution: Automatic text simplification to target reading levels
❌ Problem: Information overload from lengthy AI responses
✅ Solution: Smart summarization (brief, medium, detailed) and chunking
❌ Problem: No way to assess content complexity
✅ Solution: Reading level analysis (Flesch-Kincaid) and cognitive load prediction

3. Bias and Fairness ⚖️

❌ Problem: AI perpetuates gender and demographic bias
✅ Solution: Real-time bias detection with inclusive alternatives (50+ terms)
❌ Problem: Emotion AI discriminates against alexithymic users
✅ Solution: Bidirectional verification (40% false negative reduction)
❌ Problem: Stereotypical associations in AI outputs
✅ Solution: Pattern matching for stereotypes with recommendations

4. Multi-Modal Access 🎙️

❌ Problem: Content only available in one format
✅ Solution: Export to TTS (with SSML), audio descriptions, JSON/XML/Markdown, Braille
❌ Problem: Screen readers struggle with AI-generated content
✅ Solution: Optimization and semantic structure analysis
❌ Problem: No print-friendly formats
✅ Solution: Dyslexia-friendly formatting with spacing and font recommendations

5. Neurodiversity 🧩

❌ Problem: Idioms and ambiguity confuse autistic users
✅ Solution: Literal language conversion and explicit structure
❌ Problem: Long text blocks overwhelm ADHD users
✅ Solution: Chunking with focus aids and time estimates
❌ Problem: Complex text is hard for dyslexic users
✅ Solution: Font recommendations, spacing optimization, formatting guides
❌ Problem: Sensory overload from animations/colors
✅ Solution: Risk assessment and mitigation strategies

Key Features

🎨 Visual Accessibility Agent

from adk.agents.visual_accessibility_agent import VisualAccessibilityAgent

visual_agent = VisualAccessibilityAgent()

# Generate alt text at 3 detail levels
alt_text = await visual_agent.generate_alt_text(
    "A bar chart showing quarterly sales growth",
    detail_level="medium"
)

# Check WCAG color contrast
contrast = await visual_agent.check_color_contrast(
    foreground_color=(0, 0, 0),
    background_color=(255, 255, 255),
    text_size=14.0
)
# Returns: contrast_ratio, meets_aa, meets_aaa, wcag_level

# Simulate color blindness
colorblind = await visual_agent.simulate_colorblindness(
    color_rgb=(220, 20, 60),
    colorblind_type="deuteranopia"
)

# Optimize for screen readers
sr_check = await visual_agent.optimize_for_screen_reader(
    content,
    has_headings=True,
    has_alt_text=True
)

Features:

Alt text generation (brief, medium, detailed) with quality scoring
WCAG 2.1 AA/AAA color contrast verification
Color blindness simulation (4 types)
Screen reader compatibility analysis
Visual complexity assessment

🧠 Cognitive Accessibility Agent

from adk.agents.cognitive_accessibility_agent import CognitiveAccessibilityAgent

cognitive_agent = CognitiveAccessibilityAgent()

# Analyze reading level
analysis = await cognitive_agent.analyze_reading_level(text)
# Returns: grade_level, reading_ease, category, interpretation

# Simplify to target level
simplified = await cognitive_agent.simplify_text(
    complex_text,
    target_level="middle_school"
)

# Generate summaries
summary = await cognitive_agent.generate_summary(
    long_text,
    summary_type="brief",
    max_sentences=2
)

# Predict cognitive load
load = await cognitive_agent.predict_cognitive_load(
    content,
    has_images=5,
    has_interactions=3
)

# Convert to plain language (federal guidelines)
plain = await cognitive_agent.convert_to_plain_language(text)

# Chunk content for progressive disclosure
chunks = await cognitive_agent.chunk_content(text, chunk_size=200)

Features:

Text simplification with reading level targeting
Reading level analysis (Flesch-Kincaid, Reading Ease)
Content summarization (brief, medium, detailed)
Cognitive load prediction and recommendations
Progressive disclosure chunking
Plain language conversion

⚖️ Content Bias Detector

from adk.agents.content_bias_detector import ContentBiasDetector

bias_detector = ContentBiasDetector()

# Comprehensive bias detection
result = await bias_detector.detect_bias(
    ai_content,
    check_types=["gender", "inclusive_language", "stereotypes", "cultural"]
)
# Returns: bias_score, fairness_score, issues, recommendations

# Get inclusive alternatives
alternatives = await bias_detector.suggest_inclusive_alternatives(content)

# Generate full report with grade
report = await bias_detector.generate_bias_report(content)
# Returns: grade (A-F), summary, actionable_steps

Features:

Gender bias detection and balance analysis
50+ non-inclusive term replacements
Stereotype pattern matching
Cultural sensitivity checking
Representation analysis
Comprehensive bias reports with grades

🎙️ Multi-Modal Output Agent

from adk.agents.multimodal_output_agent import MultimodalOutputAgent

multimodal_agent = MultimodalOutputAgent()

# Prepare for text-to-speech
tts = await multimodal_agent.prepare_for_tts(
    text,
    speech_rate="medium",
    add_pauses=True
)
# Returns: tts_text, ssml, estimated_duration

# Generate audio descriptions
audio_desc = await multimodal_agent.generate_audio_description(
    visual_content,
    detail_level="standard"
)

# Export to structured formats
json_output = await multimodal_agent.generate_structured_output(
    content,
    format_type="json",
    include_metadata=True
)

# Generate print-friendly format
print_friendly = await multimodal_agent.generate_print_friendly(
    content,
    font_size=14,
    line_spacing=1.5
)

# Prepare for Braille translation
braille = await multimodal_agent.generate_braille_ready(text, grade=2)

Features:

TTS preparation with SSML, abbreviation expansion, pause insertion
Audio descriptions for visual content
Structured format export (JSON, XML, YAML, Markdown)
Print-friendly formatting (dyslexia-optimized)
Braille-ready text preparation

🧩 Neurodiversity Support Agent

from adk.agents.neurodiversity_support_agent import NeurodiversitySupportAgent

neurodiversity_agent = NeurodiversitySupportAgent()

# Autism-friendly adaptation
autism = await neurodiversity_agent.adapt_for_autism(content)
# Returns: adapted_content, autism_friendly_score, changes_made

# ADHD support with chunking
adhd = await neurodiversity_agent.adapt_for_adhd(
    content,
    chunk_size=150,
    add_focus_aids=True
)

# Dyslexia formatting recommendations
dyslexia = await neurodiversity_agent.adapt_for_dyslexia(content)
# Returns: formatting_recommendations, challenges_identified

# Sensory overload detection
sensory = await neurodiversity_agent.detect_sensory_overload_risk(
    content,
    has_animations=True,
    color_count=10
)

# Executive function support
ef_support = await neurodiversity_agent.provide_executive_function_support(
    task_description
)
# Returns: checklist, time_estimates, aids

Features:

Autism: Literal language, reduced ambiguity, explicit structure
ADHD: Chunking, focus aids, time estimates, progress tracking
Dyslexia: Font recommendations, spacing, word length analysis
Sensory: Overload risk assessment and mitigation
Executive function: Task breakdown, checklists, time management

Quick Start

🚀 Try in Google Colab (Fastest)

💻 Local Installation

# Clone repository
git clone https://github.com/Tuesdaythe13th/AccessibleDeepAgent.git
cd AccessibleDeepAgent

# Install dependencies
pip install -r requirements.txt

# Run demo notebook
jupyter notebook accessibility_features_demo.ipynb

🎯 Quick Example

from adk.agents.visual_accessibility_agent import VisualAccessibilityAgent
from adk.agents.cognitive_accessibility_agent import CognitiveAccessibilityAgent
from adk.agents.content_bias_detector import ContentBiasDetector

# Initialize agents
visual_agent = VisualAccessibilityAgent()
cognitive_agent = CognitiveAccessibilityAgent()
bias_detector = ContentBiasDetector()

# Make your AI output accessible
ai_output = "Your AI-generated content here..."

# Check reading level
reading = await cognitive_agent.analyze_reading_level(ai_output)
print(f"Reading level: Grade {reading['grade_level']}")

# Simplify if needed
if reading['grade_level'] > 8:
    simplified = await cognitive_agent.simplify_text(ai_output)
    ai_output = simplified['simplified_text']

# Check for bias
bias = await bias_detector.detect_bias(ai_output)
print(f"Fairness score: {bias['fairness_score']:.2f}")

# Fix bias if detected
if bias['total_issues'] > 0:
    fixed = await bias_detector.suggest_inclusive_alternatives(ai_output)
    ai_output = fixed['inclusive_content']

# Prepare for TTS
tts = await multimodal_agent.prepare_for_tts(ai_output)
print(f"Ready for screen readers: {tts['ssml']}")

Installation

Requirements

Python 3.8+
pydantic
torch (optional, for emotion classification)
numpy (optional, for advanced features)

Standard Installation

git clone https://github.com/Tuesdaythe13th/AccessibleDeepAgent.git
cd AccessibleDeepAgent

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Minimal Installation (No ML)

pip install pydantic

All accessibility agents work without ML dependencies!

For DeepAgent Framework

# Additional dependencies for benchmark evaluation
pip install -r requirements-adk.txt

Comprehensive Feature Guide

See the complete accessibility_features_demo.ipynb for:

✅ 40+ working code examples
✅ All features demonstrated end-to-end
✅ Google Colab compatible
✅ Copy-paste ready for your projects
✅ Detailed explanations and best practices

Architecture

Modular Design

AccessibleDeepAgent/
├── src/adk/agents/
│   ├── visual_accessibility_agent.py      # Visual features
│   ├── cognitive_accessibility_agent.py    # Cognitive features
│   ├── content_bias_detector.py           # Bias detection
│   ├── multimodal_output_agent.py         # Multi-modal output
│   ├── neurodiversity_support_agent.py    # Neurodiversity support
│   ├── core.py                            # Core coordination
│   └── ui_adaptation_agent.py             # UI adaptations
├── src/adk/
│   ├── bidirectional_reasoning.py         # Emotion AI fairness
│   ├── neuroadaptive_wrapper.py           # Complete integration
│   └── utils/                             # Shared utilities
├── accessibility_features_demo.ipynb       # 🆕 Complete demo
└── src/                                   # DeepAgent framework

Integration Patterns

Use Individual Agents:

from adk.agents.cognitive_accessibility_agent import CognitiveAccessibilityAgent

cognitive_agent = CognitiveAccessibilityAgent()
result = await cognitive_agent.simplify_text(ai_output)

Use Complete Wrapper:

from adk.neuroadaptive_wrapper import NeuroadaptiveWrapper

wrapper = NeuroadaptiveWrapper()
await wrapper.initialize()
result = await wrapper.process_interaction_with_emotion(...)

Platform Integration

Integrate into Your AI Application

class AccessibleChatbot:
    def __init__(self):
        self.cognitive_agent = CognitiveAccessibilityAgent()
        self.bias_detector = ContentBiasDetector()
        self.multimodal_agent = MultimodalOutputAgent()

    async def generate_accessible_response(self, user_input, user_profile):
        # Generate AI response
        ai_response = await your_llm.generate(user_input)

        # 1. Check for bias
        bias_check = await self.bias_detector.detect_bias(ai_response)
        if bias_check['total_issues'] > 0:
            alt = await self.bias_detector.suggest_inclusive_alternatives(ai_response)
            ai_response = alt['inclusive_content']

        # 2. Simplify if needed
        if user_profile.get('reading_level') == 'simple':
            simp = await self.cognitive_agent.simplify_text(ai_response)
            ai_response = simp['simplified_text']

        # 3. Prepare multi-modal outputs
        tts = await self.multimodal_agent.prepare_for_tts(ai_response)

        return {
            'text': ai_response,
            'tts_ssml': tts['ssml'],
            'fairness_score': bias_check['fairness_score']
        }

API Service Example

from fastapi import FastAPI
from adk.agents import CognitiveAccessibilityAgent, ContentBiasDetector

app = FastAPI()
cognitive = CognitiveAccessibilityAgent()
bias = ContentBiasDetector()

@app.post("/api/accessible")
async def make_accessible(content: str, features: List[str]):
    result = {}
    if "simplify" in features:
        result['simplified'] = await cognitive.simplify_text(content)
    if "bias" in features:
        result['bias'] = await bias.detect_bias(content)
    return result

DeepAgent Framework

AccessibleDeepAgent also includes a research-grade multi-tool reasoning agent for benchmark evaluation.

Supported Benchmarks

Benchmark	Type	Status
ToolBench	Tool use	✅ Supported
GAIA	QA with tools	✅ Supported
API-Bank	API calling	✅ Supported
RestBench	REST API use	✅ Supported
ToolHop	Multi-hop tool use	✅ Supported
ALFWorld	Embodied tasks	✅ Supported
WebShop	Web navigation	✅ Supported

Quick Start

# Run on GAIA benchmark
python src/run_deep_agent.py \
    --config_path ./config/base_config.yaml \
    --dataset_name gaia \
    --enable_tool_search \
    --eval

See original sections below for full DeepAgent documentation.

ADK: Neuroadaptive Accessibility

The ADK (Accessibility Development Kit) provides real-time neuroadaptive accessibility with emotion AI bias mitigation.

Core Features

Real-time Signal Processing: Eye tracking, interaction patterns, mouse movement
Cognitive State Estimation: Load, attention, fatigue, stress, comprehension
Bidirectional Reasoning: 40% FNR reduction for alexithymic users
BeTaL Testing: Automated fairness benchmark generation (5.8% gap vs 12.5% baseline)
Memory System: Persistent profiles with mem0.ai
UI Adaptation: Dynamic accessibility adjustments

Quick Start

import asyncio
from adk.agents.core import AccessibilityCoordinator
from adk.utils import SignalType

async def main():
    coordinator = AccessibilityCoordinator()
    await coordinator.initialize()

    # Process interaction
    raw_signals = [
        (SignalType.EYE_TRACKING, 0.7, {}),
        (SignalType.INTERACTION_TIMING, 0.65, {}),
    ]

    result = await coordinator.process_user_interaction(
        raw_signals=raw_signals,
        user_id="user123",
        content_to_refine="Your content..."
    )

    print(f"Cognitive Load: {result['cognitive_state']['cognitive_load']:.2f}")
    await coordinator.close()

asyncio.run(main())

Fairness & Bias Mitigation

Bidirectional Reasoning Network

Problem: Emotion AI discriminates against alexithymic users (40% higher false negatives)

Solution: Bidirectional verification instead of unidirectional classification

Audio → Encoder → Emotion → Decoder → Reconstructed Audio
  ↓                                        ↓
Embedding 1 ←── Contrastive Loss ──→ Embedding 2

Result: 40% reduction in false negative rate

BeTaL: Automated Fairness Testing

LLM-guided benchmark generation achieving 5.8% fairness gap (vs 12.5% baseline)

Designer LLM → proposes scenarios
Student LLM → evaluated on fairness
Feedback Loop → optimizes for gaps

Documentation

Core Documentation

🆕 Accessibility Demo: accessibility_features_demo.ipynb - Complete feature showcase
Main README: README.md - This file
ADK Docs: src/adk/docs/README.md
Bidirectional Reasoning: src/adk/docs/BIDIRECTIONAL_REASONING.md
BeTaL Framework: src/adk/docs/BETAL.md

Standards Compliance

✅ WCAG 2.1 AA/AAA
✅ Federal Plain Language Guidelines
✅ Person-First Language Standards
✅ Section 508 Accessibility
✅ Flesch-Kincaid Readability

Use Cases

AI Content Generators - Ensure all generated content is accessible
Chatbots & Assistants - Adapt responses based on user profiles
Educational Platforms - Simplify content to appropriate levels
CMS Systems - Check content for bias before publishing
Documentation - Generate multi-modal versions
Customer Support - Provide accessible responses

Contributing

We welcome contributions! Priority areas:

Additional language support
More bias detection patterns
Enhanced neurodiversity features
New output formats
Accessibility testing tools
Documentation improvements

See CONTRIBUTING.md for guidelines.

Citation

@software{accessibledeepagent2025,
  title={AccessibleDeepAgent: Comprehensive Accessibility Framework for Generative AI},
  author={Tuesday, ARTIFEX Labs},
  year={2025},
  url={https://github.com/Tuesdaythe13th/AccessibleDeepAgent},
  note={Production-ready accessibility toolkit with fairness-focused AI agents}
}

License

MIT License - see LICENSE file for details.

Acknowledgments

Humane Intelligence Accessibility Bias Bounty - Motivating force
WCAG & W3C - Accessibility standards
Plain Language Community - Guidelines and best practices
Neurodiversity Advocates - User-centered design insights
mem0.ai - Memory system infrastructure
OpenAI, Anthropic - LLM providers

Contact & Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Email: general@artifex.fun

Project Status

🚀 Active Development | ⭐ Production-Ready Accessibility Features

Recent Updates

✅ NEW: 5 comprehensive accessibility agents (Visual, Cognitive, Bias, Multi-Modal, Neurodiversity)
✅ NEW: Complete Jupyter demo notebook with 40+ examples
✅ NEW: Platform-agnostic integration guides
✅ Bidirectional reasoning for emotion AI fairness
✅ BeTaL automated fairness testing
✅ Multi-benchmark evaluation harness

Roadmap

Making Generative AI Accessible to Everyone ♿️

Built with accessibility at the core. Production-ready. Platform-agnostic. Open source.

Name		Name	Last commit message	Last commit date
Latest commit History 57 Commits
config		config
data		data
docs		docs
figures		figures
src		src
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
SCRIPT_REVIEW.md		SCRIPT_REVIEW.md
VERIFICATION_REPORT.md		VERIFICATION_REPORT.md
accessibility_features_demo.ipynb		accessibility_features_demo.ipynb
accessible_deep_agent_accessibility_demo.ipynb		accessible_deep_agent_accessibility_demo.ipynb
bounty_valence_analysis.ipynb		bounty_valence_analysis.ipynb
requirements-adk.txt		requirements-adk.txt
requirements.txt		requirements.txt
verify_results.py		verify_results.py

License

Tuesdaythe13th/AccessibleDeepAgent

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AccessibleDeepAgent

🎯 What's New: Expanded Accessibility Features

🆕 New Accessibility Agents

📓 Complete Demo Notebook

Table of Contents

Overview

What Makes This Different?

Top Accessibility Challenges Addressed

1. Visual Barriers 🎨

2. Cognitive Barriers 🧠

3. Bias and Fairness ⚖️

4. Multi-Modal Access 🎙️

5. Neurodiversity 🧩

Key Features

🎨 Visual Accessibility Agent

🧠 Cognitive Accessibility Agent

⚖️ Content Bias Detector

🎙️ Multi-Modal Output Agent

🧩 Neurodiversity Support Agent

Quick Start

🚀 Try in Google Colab (Fastest)

💻 Local Installation

🎯 Quick Example

Installation

Requirements

Standard Installation

Minimal Installation (No ML)

For DeepAgent Framework

Comprehensive Feature Guide

Architecture

Modular Design

Integration Patterns

Platform Integration

Integrate into Your AI Application

API Service Example

DeepAgent Framework

Supported Benchmarks

Quick Start

ADK: Neuroadaptive Accessibility

Core Features

Quick Start

Fairness & Bias Mitigation

Bidirectional Reasoning Network

BeTaL: Automated Fairness Testing

Documentation

Core Documentation

Standards Compliance

Use Cases

Contributing

Citation

License

Acknowledgments

Contact & Support

Project Status

Recent Updates

Roadmap

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