Background

AfriLabs

AfriLabs is a network organization that supports innovation hubs, tech communities, and entrepreneurship in Africa. With a presence across 53 countries, AfriLabs plays a key role in fostering the growth of innovative tech solutions that address critical challenges in Africa. By connecting innovators, providing resources, and promoting collaboration, AfriLabs drives entrepreneurship and technological advancements across the continent. The organisation has been a strategic partner in various initiatives aimed at empowering African innovators, including organizing hackathons, incubation programs, and capacity-building initiatives.

Gates Foundation and Meta

The Gates Foundation, through its Gender Equality Digital Connectivity (GEDC) and Digital Public Infrastructure (DPI) teams, is committed to fostering digital inclusion and gender equality across Africa. The GEDC team focuses on developing digital tools and technologies that are sensitive to gender issues, ensuring equitable access to information and services for women.

Meta (formerly Facebook) is a global technology leader, known for its work in advancing artificial intelligence (AI), machine learning, and open-source tools. Meta supports the Llama Impact Grants Program, which seeks to empower innovators and startups to leverage open-source AI models like Llama to address societal challenges, including gender sensitivity and inclusivity, and linguistic diversity.

Together, the Gates Foundation and Meta have partnered to launch a series of programs aimed at improving AI outputs in African languages and ensuring that these solutions are gender-sensitive, linguistically diverse, and culturally relevant.

About the Project

The AI Bias Reduction & Inclusive Data Generation Engine programme is a key initiative designed to engage innovators across Africa in the development of AI-driven solutions that address gender sensitivity, particularly in African languages. The goal is to create innovative AI models that are inclusive and free from gender bias, promoting equitable access to information for both women and men in African communities.

In Phase 1, there was a hackathon where AfriLabs engaged 99 participants, forming 19 country-based teams that developed prototypes over the course of an intensive competition. The top eight winning teams proceeded to apply for the Llama Impact Grants, a fund that supports innovative applications of their open-source AI model, Llama, to address pressing societal challenges.

These teams have now advanced to Phase 2, where four for the eight winning teams have been selected to receive mentor support, to develop a gender sanitization engine that will be analysing and cleaning outputs within 17 pre-determined African languages. Consultancy is a critical component of this initiative, as it provides participants with the guidance needed to refine their work when it comes to the creation of the gender sanitization engine.

Project Objectives

The AI BRIDGE (AI Bias Reduction & Inclusive Data Generation Engine) initiative aims to advance the development of gender-sensitive, inclusive AI tools by engaging African innovators in dataset-level bias mitigation. The primary objectives of Phase 2 of this initiative are to:

1. Detect and mitigate gender bias within datasets used for training AI/LLM systems.
2. Support dataset curators and AI developers with tools to rewrite biased content while maintaining meaning.
3. Promote inclusive AI development across African and global linguistic contexts.
4. Enable scalable, modular integration across any AI model pipeline.

This phase builds on earlier outputs from the efforts within the phase one activity; the hackathon and will empower selected teams to build model-agnostic, upstream bias detection and rewriting pipelines that enhance fairness and representation in AI systems within the African context.

Project Methodology

The implementation of AI BRIDGE Phase 2 will follow a structured, modular, and academically rigorous approach. It will blend automated processes with human oversight to ensure gender sensitivity and cultural accuracy in AI training datasets. The methodology is divided into seven critical stages:

1. Data Ingestion

• Source/Curate diverse structured and unstructured datasets across targeted African languages and sectors.
• Align data sources with use cases relevant to gender representation and inclusion.

1. Bias Detection

• Apply hybrid scanning techniques (rule-based logic, machine learning classifiers, and statistical metrics) to identify gender-biased, stereotypical, or exclusionary content.
• Highlight problematic language using linguistic and contextual tagging.

1. Content Analysis

• Conduct contextual tagging of flagged entries, including metadata classification (e.g., severity, stereotype type, grammatical role).
• Organize entries into review-ready clusters for annotation and sanitization.

1. Content Sanitization

• Deploy transformer-based models to suggest rewritten alternatives that are semantically consistent yet bias-free.
• Incorporate a controlled vocabulary for gender-sensitive rewriting.

1. Human-in-the-Loop Validation

• Facilitate expert review from gender specialists, linguists, and cultural advisors to validate rewrites in context-sensitive cases.
• Enable override or acceptance workflows using customized annotation tools.

1. Dataset Finalization

• Finalize sanitized datasets, version-control all changes, and document transformation history for audit and transparency.
• Deliver outputs with full traceability and aligned with ethical AI documentation standards.

1. Feedback and Continuous Learning

• Maintain a feedback loop for rejected or complex cases to improve rule sets and classifier accuracy.
• Integrate feedback into successive model training iterations and guideline updates.

Purpose of the Assignment

AfriLabs is seeking an Annotation expert to support the development of AI-driven gender-sensitive tools for African languages. The expert will provide technical guidance, assess linguistic and gender biases in model outputs, and support the integration of inclusive practices within the AI systems being developed. This role requires a strong understanding of AI technologies, gender sensitivity and analysis, linguistic diversity, the African innovation landscape, and familiarity with the nuances of African Sociolinguistic contexts.

Scope of Work

The annotation expert will be responsible for the following:

1. Annotation of Gender Bias and Non-Inclusive Language:

• Review text entries in structured and unstructured datasets to identify gender bias, stereotypes, and exclusionary or non-inclusive phrasing.
• Apply defined tagging schemas to annotate:
  • Type of bias (e.g., occupational stereotyping, exclusion of gender identities)
  • Severity of bias
  • Linguistic or cultural context
• Use annotation tools such as Label Studio, Prodigy, or custom platforms as provided by the team.

1. Application of Metadata and Contextual Tags

• Attach context-aware metadata to flagged content, including:
  • Language/dialect identifiers
  • Sociocultural or gender-related cues
  • Intended use-case relevance (e.g., customer service dataset, health dataset)
• Ensure consistency and accuracy in applying metadata for future ML model training.

1. Collaboration with Linguists and Gender Experts:

• Work collaboratively with linguists and gender domain specialists to refine annotation guidelines and clarify ambiguous or culturally sensitive entries.
• Participate in feedback loops to improve annotation accuracy and address edge cases (e.g., non-binary pronouns, culturally specific roles or titles).

1. Quality Control and Validation

• Perform cross-checks and peer reviews on annotated datasets to ensure:
  • High inter-annotator agreement
  • Consistency in bias categorization
  • Adherence to project-specific ethical guidelines
• Flag annotation conflicts or uncertain entries for expert validation or group review.

1. Support to Human-in-the-Loop Review

Required Qualifications and Experience

1. General Qualifications

The expert is expected to possess the following qualifications:

• Native or near-native fluency in at least one of the 17 selected African languages, coupled with strong command of English or French for coordination and reporting.
• Experience working on linguistic or text-based projects involving annotation, tagging, or corpus development.

1. Specialized Qualifications and Experience

The Consultant should be qualified two or more of the following areas:

• Demonstrated understanding of gender-coded language, social norms, and contextual cues across different African linguistic communities.
• Familiarity with digital annotation tools such as Prodigy, Label Studio, or custom annotation platforms used for NLP tasks.
• Ability to follow structured annotation protocols and maintain consistency in labelling across diverse datasets.
• Experience in reviewing AI-generated content (e.g., rewrites or translations) for semantic accuracy, tone, and cultural appropriateness is desirable.

Proposed/Tentative timeline of deliverables

• Selected Consultants are expected to carry out this assignment for a total of five months (from 7th August 2025 to the 31st of December 2025).
• This will be done remotely

Method Of Application  

Applying as an Individual:  

• Filled Vendor request form 
• Send a Cover Letter, CV or portfolio stating profile and relevant experience. 
• Means of Identification.
• Financial proposal (This must be in USD)
• Submission of organization reference (i.e. Name, email and phone number of organization you have provided similar services)
• A sample of previous review work, preferably related to competitive innovation or grant programs.

Applying as a company:  

• Filled Vendor Request Form 
• Send your company’s certificate. 
• Company registration document. 
• Cover letter and CV of relevant experience of the applicant or Team Lead. 
• Means of Identification of the team lead. 
• Financial proposal (This must be in USD)
• Submission of organization reference (i.e. Name, email and phone number of organization you have provided similar services) A sample of previous review work, preferably related to competitive innovation or grant programs.

Evaluation Criteria:

Evaluation of each applicant will be based on the following:

Technical Proposal (65%)

• Submission of the requested documents as outlined in the method of application for each category
• Proof of required qualification and expertise
• Proof of work done in relation to the requested scope

Financial Proposal (35%)

• Financial proposal should be submitted in USD and kindly indicate if the amount proposed is negotiable or not.
• Validity of the financial proposal should be minimum of 6 months.

Note the following:  

• Please download and use the attached Vendor information form attached below to submit your application to procurement@afrilabs.com with the email subject: “AI BRIDGE ANNOTATOR”. Applicants should submit their applications on or before COB August 15th, 2025.  

• Kindly find the Vendor information form. Please download before use, DO NOT edit the uploaded template.
• Paid Hub members are strongly advised to apply with their company name (If within their thematic areas and expertise). 
• Women are strongly encouraged to apply
• Only the shortlisted applicants will proceed to the next stage
• Renumeration Range: $3,500 – $5,000