Building culturally intelligent artificial intelligence systems that understand, serve, and empower Black and brown communities.
A research laboratory dedicated to ensuring Black people aren't erased from the AI revolution through technology, education, and workforce development.
Within 5 years, AI BLACK API becomes the standard layer that every major company integrates when they want their AI to understand Black culture.
We're building the infrastructure, the knowledge, and the community that ensures when AI reshapes the world, Black people are leading, not left behind.
Black engineers, entrepreneurs, students, and professionals learning, connecting, and building together as the vanguard of culturally intelligent AI.
Join Community →From everyday AI literacy to advanced engineering. Master culturally intelligent AI development with expert-led courses designed for Black innovators.
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A Black AI Lab building for Black people, by Black people
F3 LABS is where Black and brown researchers come together to build AI systems that actually understand our communities, our culture, and our needs.
We're training AI models that represent people who look like us, developed by us, for us. No more biased algorithms that exclude our experiences.
Our flagship project: an API that brings cultural intelligence to any AI system. Help us build it, test it, and bring it to the world.
A comprehensive framework for addressing the systemic exclusion of Black communities from the artificial intelligence revolution
This white paper presents a comprehensive framework for addressing the systemic exclusion of Black communities from the artificial intelligence revolution. As Large Language Models (LLMs) and AI systems become the foundational infrastructure of the 21st-century economy, Black Americans—particularly those in under-resourced communities—face a dual crisis: they are simultaneously excluded from AI development processes and disproportionately harmed by biased AI systems that lack cultural competency.
We propose a multi-layered approach centered on the AI BLACK API—a culturally intelligent abstraction layer built atop existing foundational models (OpenAI, Anthropic Claude, Google Gemini, xAI Grok)—combined with comprehensive community education, workforce development, and participatory design methodologies.
87% of Black households earning under $35,000 annually have limited or no understanding of AI technology
Current LLMs demonstrate persistent bias in understanding AAVE and Black cultural references
Fewer than 2.5% Black technical staff in the AI industry creates a dangerous feedback loop
AI-driven automation could eliminate up to 40% of jobs concentrated in Black communities by 2030
A comprehensive, multi-layered approach to building cultural intelligence in AI
Building a culturally intelligent abstraction layer that sits between foundation models and applications, enriching AI responses with deep Black cultural knowledge through RAG (Retrieval-Augmented Generation) architecture and vector databases.
Meeting communities where they are with tiered AI literacy programs—from awareness workshops in housing complexes to advanced technical training—ensuring no one is left behind in the AI revolution.
Creating paid apprenticeship pathways that transform individuals from $32K/year survival mode to $100K+ AI engineering careers, with comprehensive support addressing childcare, housing, healthcare, and emergency assistance.
Ensuring Black communities are not just users but co-creators of AI systems through community review boards, cultural knowledge curation, and ethical safeguards that protect against extraction and appropriation.
Creating Black-owned AI companies, infrastructure, and revenue streams that ensure wealth generated by AI flows back into Black communities, not just through jobs but through ownership and entrepreneurship.
Understanding the dual crisis of exclusion and harm facing Black Americans in the AI revolution
By 2030, AI-driven automation will eliminate 3-4 million jobs disproportionately held by Black workers
AI wealth will accrue almost entirely to non-Black owners and investors
AI systems will continue encoding bias across every sector of society
Black children will grow up with AI infrastructure built without their humanity in mind
This is not inevitable. Preventing it requires urgent, comprehensive action.
A culturally intelligent abstraction layer that transforms how AI systems understand and respond to Black cultural contexts
Enhanced with cultural competency
Intelligent routing across foundation models
GPT-4, Claude, Gemini, Grok
Compute, storage, networks
Curated by Black communities
Analyzes whether cultural context is relevant—AAVE, Black cultural references, or community-specific knowledge
Enriches requests with cultural metadata, knowledge base retrieval, and disambiguation of Black cultural contexts
Routes to the most appropriate foundation model(s) based on query type—or queries multiple models
Verifies cultural accuracy, corrects misunderstandings, enhances with additional context, adjusts tone
Community feedback, flagging inaccuracies, adding quality responses to knowledge base
A comprehensive, community-curated repository of Black cultural information—the heart of the AI BLACK API
Not created by external "experts" but by Black communities themselves through open contribution, diverse review boards, and compensated knowledge sharing
Continuous updates reflecting cultural evolution, trend tracking, version control, and preservation of historical context as culture changes
Protection against cultural appropriation, community consent for knowledge sharing, revenue sharing, and clear guidelines on internal vs. external knowledge
From community education to elite engineering talent, we're creating multiple pathways into AI for Black and brown communities
Community Education
We bring AI literacy directly to communities and everyday people, teaching them how to properly utilize AI tools and understand how this technology impacts their lives. This isn't about becoming an engineer—it's about empowerment through knowledge.
Talent Recruitment & Opportunities
We don't just train talent—we place them. Our comprehensive placement program ensures our engineers land positions where they can thrive, with ongoing support to ensure long-term success.
Rigorous, community-centered evaluation to ensure the AI BLACK API actually works
Dataset of 10,000 questions requiring Black cultural knowledge, validated by expert panel
Compared to 40-60% for bare foundation models
Benchmark dataset measuring preservation of meaning and cultural nuance
Sentiment analysis accuracy and meaning preservation
Standardized bias testing frameworks measuring stereotypical associations
Compared to base foundation models
User satisfaction ratings and expert review of responses
In user satisfaction vs. base models
Monthly sessions evaluating system performance and identifying blind spots
Regular sessions with diverse populations testing real-world usage
"Red team" of experts actively seeking failures and edge cases
Tracking performance over time as knowledge base grows
Whether you're a developer, researcher, community member, or someone who believes in this mission—there's a place for you in this revolution
Join our apprenticeship program, contribute to the API, or build applications using our cultural intelligence layer
Apply NowCollaborate on cultural AI research, access our datasets, publish with us, or join HBCU partnerships
Get InvolvedContribute cultural knowledge, join review boards, participate in workshops, or support the mission
Join Community
Cite this white paper:
F3 LABS. (2026).
AI While Black: A White Paper on Building Culturally Intelligent AI
Infrastructure for Black Communities.
F3 LABS Research Division.
Welcome to our community hub where we're building the first culturally intelligent AI system designed by Black people, for Black people. Choose your community based on your interests and skill level.
Teaching AI to truly see Black people and culture
This community welcomes data scientists, machine learning engineers, computer vision researchers, and anyone interested in teaching AI to recognize Black people and cultural contexts accurately. Whether you're experienced or just starting out, there's a place for you here.
Current AI vision systems fail to recognize Black hair, Black spaces, and Black cultural contexts because they're trained on predominantly white, Western datasets. We're building the Black Cultural Vision Dataset to fix this fundamental problem and ensure AI can see us accurately.
The problem is stark: AI cannot distinguish between Type 4A, 4B, and 4C natural hair textures. It labels protective styles as unprofessional and fails to recognize the artistry in fades, edge control, or loc maintenance. We are teaching AI to understand natural hair texture classification across Type 1 through 4 with A through C variations, protective style recognition including box braids, Senegalese twists, cornrows, and bantu knots, hair care product identification like edge control and silk bonnets, cultural contexts ranging from church hair to everyday to athletic styles, and temporal understanding of hair journeys including the big chop and various loc stages.
AI trained on corporate America and suburban homes fails to recognize where Black life actually happens. We are teaching it to identify sacred spaces including Black churches with their unique architecture, interior design, and pulpit setups. Community hubs like barbershops, beauty salons, and community centers. Educational institutions including HBCU campuses, Greek life spaces, and Black study lounges. Cultural gatherings such as family reunions, cookouts, block parties, and step shows. Economic spaces including Black-owned businesses, beauty supply stores, and soul food restaurants.
AI wrongly associates professionalism exclusively with white faces in Western business attire. We are training it to recognize Black executives in corporate settings with suits, boardrooms, and leadership poses. Black professionals across diverse fields including doctors, lawyers, engineers, teachers, and artists. Professional code-switching in body language, dress, and communication styles. Excellence markers such as awards, credentials, and achievements within Black contexts.
AI expects nuclear families and fundamentally misunderstands Black family dynamics. We are teaching it to recognize extended family networks where grandparents, aunts, uncles, and cousins serve as primary caregivers. Multi-generational households. Chosen family and fictive kin relationships. Church family and community parenting structures. Single-parent resilience and strength.
We are teaching AI to identify and understand Kente cloth patterns and their meanings. African artifacts and their cultural significance. Black art movements including the Harlem Renaissance and AfroFuturism. HBCU paraphernalia and Greek letter organization symbols. Pan-African colors and their symbolism.
We are teaching AI to recognize daps, handshakes, and culturally specific greetings. Dance movements across genres including step, bounce, twerk, and African dance. Gestures with cultural meaning. Personal space and touch norms. Eye contact patterns in different contexts.
Learn ethical data collection in Black communities, image annotation standards and tools like Label Studio and CVAT, consent frameworks and privacy protection, dataset versioning and management, and quality control and validation processes. You will help curate images from diverse Black contexts, annotate images with cultural context, validate annotations from other community members, and identify gaps in dataset representation. We use Python and OpenCV for image processing, Label Studio for annotation, AWS S3 or Google Cloud Storage for dataset hosting, and DVC for dataset versioning.
Master transfer learning from base models like ResNet, EfficientNet, and Vision Transformers. Fine-tune on culturally specific datasets. Learn data augmentation techniques and how to handle class imbalance. Train on GPU clusters using Google Colab Pro or vast.ai. We use PyTorch or TensorFlow and Keras, Hugging Face Transformers for vision models, Weights and Biases for experiment tracking, and ONNX for model deployment. You will build hair texture classifiers distinguishing 4A versus 4B versus 4C, protective style detectors covering over twelve style categories, Black space recognition systems, and professional context analyzers.
Learn to create culturally relevant test sets, measure fairness metrics like demographic parity and equal opportunity, conduct intersectional bias testing across skin tone, gender, and age, perform error analysis and failure case identification, and drive community-driven validation. We track accuracy across skin tones using the Fitzpatrick scale, precision and recall for each cultural category, false positive and negative rates by demographic, and cultural authenticity scores rated by community members.
Learn model serving using TensorFlow Serving or TorchServe, API development with FastAPI, real-time inference optimization, monitoring model performance in production, and continuous learning from user feedback. The final deliverable is a production-ready API that can analyze images for Black cultural context. It will be usable by hiring platforms to detect bias in candidate photo screening, social media for better content moderation for Black creators, healthcare for recognizing Black patients in medical imaging, and education for culturally responsive educational tools.
Psychologists, neuroscientists, machine learning engineers, and anyone interested in teaching AI to understand Black emotional expression in its full cultural context.
Current emotion recognition AI misreads Black emotional expression, labeling Black faces as angry or threatening when expressing neutral or positive emotions. We're building emotion AI that understands cultural context and reads our emotions accurately.
Existing emotion AI is trained primarily on white faces displaying Western emotional norms. It misclassifies Black facial expressions due to different facial feature proportions, ignores cultural display rules about how emotions are appropriately expressed, lacks context understanding between work versus home versus church versus protest settings, and cannot detect microaggressions or racial stress responses.
We move beyond Ekman's six basic emotions of joy, sadness, anger, fear, surprise, and disgust to culturally specific expressions. We are teaching AI to understand code-switching facial expressions between professional mask and authentic expression, church emotions including spiritual joy, catching the Holy Ghost, and shouting, protest emotions such as righteous anger, collective grief, and resistance, Black joy with its specific qualities of celebration and familial warmth, and trauma responses including historical trauma and racial battle fatigue.
Voice AI trained on Standard English misreads AAVE prosody, tone, and emotional content. We are teaching it AAVE emotional prosody patterns, tone-switching between contexts, call-and-response patterns, preaching cadence and spiritual speech, signifying, playing the dozens, and verbal artistry, plus detection of microaggressions in speech.
We combine facial expression, voice, body language, and context to understand cultural performances like step shows, praise dance, and spoken word, detect emotional incongruence such as smiling through pain, and recognize the complexity of respectability politics.
You start with literature review and framework development over two months, learning the history of emotion research and its racial biases, cultural psychology of Black emotional expression, existing affective computing datasets and their limitations, ethical considerations in emotion AI, and participatory design methodologies. Your reading list includes Rage of a Privileged Class by Ellis Cose, The Cultural Nature of Human Development by Barbara Rogoff, research on racial bias in emotion recognition AI, and studies on weathering and racial battle fatigue.
Then move into data collection protocol over three months, learning video data collection in naturalistic settings, consent and privacy in emotion research, annotation of multimodal emotional data, inter-rater reliability across cultural backgrounds, and handling sensitive emotional content. We collect ecological moments showing real emotional expressions in Black contexts with consent, acted scenarios with Black actors portraying culturally specific emotional situations, historical footage of Black emotional expression from civil rights speeches and cultural performances, and self-report where participants describe their own emotional states. Tools include video annotation software like ELAN and Anvil, multimodal data synchronization, secure cloud storage with encryption, and audio processing tools like Praat and Parselmouth.
Next, develop models over four months, learning facial action coding system and its limitations, deep learning for facial expression recognition, speech emotion recognition architectures, attention mechanisms for multimodal fusion, and transfer learning from general emotion models. Our technical stack includes OpenFace, FaceNet, and AffectNet for vision, librosa, pyAudioAnalysis, and OpenSMILE for audio, and late fusion, early fusion, and attention-based fusion for multimodal integration, all using PyTorch and TensorFlow frameworks. You will build a Black facial expression classifier, AAVE speech emotion recognizer, context-aware multimodal emotion system, and microaggression detection tool.
Finally, validate through community testing over three months, creating culturally valid evaluation metrics, conducting community-based participatory evaluation, measuring real-world impact, addressing model failures gracefully, and iterative improvement based on feedback. Validation methods include community panels reviewing model outputs, comparison to human annotators from Black communities, cross-cultural comparison studies, and longitudinal testing for consistency.
Making AI understand Black language
Linguists, NLP engineers, computational social scientists, and anyone passionate about how AI understands Black language and ensuring AAVE is recognized as a sophisticated linguistic system.
Make AI understand that AAVE is not broken English but a sophisticated linguistic system with its own grammar, pragmatics, and cultural logic. We're building language models that respect and accurately process how we actually speak.
Teaching AI the grammar and syntax of AAVE including habitual be where he be working means he works regularly, zero copula where she smart means she is smart, negative concord where I don't know nothing means I don't know anything, aspectual markers using done, been, and steady, and unique question formation patterns that make AAVE a complete linguistic system.
We are teaching AI about code-switching patterns and triggers, call-and-response structures, signifying and indirect communication, respectability politics in language, generational language differences, and regional AAVE variations.
Understanding when dead means funny, recognizing sarcasm and irony in Black contexts, detecting tone indicators unique to Black Twitter, understanding figurative language and metaphor, and identifying cultural references and allusions that give language meaning.
Begin with corpus building over four months through ethical collection of AAVE text data. Learn web scraping and API usage, data cleaning while preserving linguistic features, anonymization and privacy protection, and copyright and fair use considerations. Data sources include Black Twitter with consent, Black literature and creative writing, transcripts of Black podcasts and media, AAVE language blogs and resources, and historical AAVE texts. Tools include Python with BeautifulSoup and Scrapy, Twitter API and Reddit API, regex for pattern matching, and Pandas for data organization.
Move to linguistic annotation over three months, learning part-of-speech tagging for AAVE, dependency parsing, named entity recognition in Black contexts, sentiment annotation with cultural nuance, and discourse analysis. The annotation framework covers grammatical features unique to AAVE, cultural references and their meanings, code-switching boundaries, sentiment with context including sarcasm, irony, and hyperbole, and offensive versus in-group language use.
Train models over three months, learning to fine-tune language models like BERT, RoBERTa, and GPT, understanding training from scratch versus transfer learning, handling low-resource language scenarios, evaluation metrics for language understanding, and bias testing in NLP models. Projects you will build include an AAVE grammar checker that does not correct to Standard English, an AAVE-aware sentiment analyzer, a cultural reference detector, a code-switching identifier, and a microaggression detection tool for text.
Finally, integrate with AI BLACK API over two months, learning API design for NLP services, real-time text processing, caching and optimization, user feedback integration, and continuous model improvement.
Learn AI fundamentals while contributing to research
Educators, students, researchers, and anyone who wants to learn the fundamentals of AI while contributing to meaningful research. No matter where you're starting from, we have a learning path for you.
No prerequisites are needed. Start with Python programming for AI, machine learning fundamentals, computer vision basics, natural language processing basics, and data ethics and bias. We guide you through every step as you build a foundation in artificial intelligence.
For those with programming experience. Learn deep learning architectures, PyTorch and TensorFlow frameworks, model evaluation and testing, research methodology, and academic paper reading and writing. This track prepares you for advanced AI work.
For experienced practitioners. Work on novel architecture design, publishing research papers, grant writing, leading research teams, and industry collaboration at the highest level. Push the boundaries of what AI can do.
We meet you where you are and help you grow. Whether you're starting from zero or you're an experienced researcher, our community provides mentorship, structured learning paths, hands-on projects, and opportunities to contribute to real research that matters.
Ensuring our work serves Black communities
Community organizers, ethicists, activists, and anyone who wants to ensure our technical work actually serves Black communities. You don't need technical skills to make an essential contribution here.
You serve as the bridge between technologists and communities, ensuring research is ethical and beneficial. You gather community input on AI priorities, test systems in real-world contexts, and advocate for responsible AI development. Your role ensures that the technology we build truly respects and serves our people rather than extracting value or causing harm. You ensure research benefits communities rather than exploiting them, protect community privacy and autonomy, and test whether AI systems actually help or harm in practice.
You develop ethical frameworks for data collection that respect community privacy and autonomy. This ensures that data is collected with genuine informed consent and that communities understand how their contributions will be used.
You ensure communities actually benefit from research rather than being exploited for data. This includes creating structures for revenue sharing, job creation, and ensuring the technology serves community needs.
You lead sessions where community members actively shape how AI is built, ensuring their voices drive technical decisions rather than just being consulted after the fact.
You evaluate whether our technology actually helps or harms communities in practice, measuring real-world outcomes rather than just technical metrics.
You advocate for policies that protect Black communities from AI harm at the local, state, and federal levels, translating technical understanding into policy language.
You test AI systems in actual community contexts to ensure they work as intended and identify failures before they cause harm at scale.
Pick one or more communities that match your interests and skills. You can join multiple labs.
Tell us about your background, skills, what you want to learn, your time commitment, and your goals.
We'll connect you with a mentor, project team, and learning resources tailored to your level and interests.
Begin making meaningful contributions to the project within your first week. No waiting period.