Advancing Gender Equity in Digital Agro-advisory through Inclusive Artificial Intelligence (AI): Bias Analysis and Strategic Recommendations from the iShamba Platform Based on Five Years of Female Farmers’ Queries

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cg.contributor.affiliationAlliance of Bioversity International & CIATen
cg.contributor.donorWorld Bank
cg.contributor.programAcceleratorClimate Action
cg.coverage.countryKenya
cg.coverage.iso3166-alpha2KE
cg.coverage.regionAfrica
cg.coverage.regionEastern Africa
cg.coverage.regionSub-Saharan Africa
cg.creator.identifierAmanda Grossi: 0000-0001-9861-8551
cg.reviewStatusInternal Reviewen
cg.subject.actionAreaResilient Agrifood Systems
cg.subject.actionAreaSystems Transformation
cg.subject.alliancebiovciatAGRICULTUREen
cg.subject.alliancebiovciatCLIMATE CHANGE ADAPTATIONen
cg.subject.alliancebiovciatGENDER AND EQUITYen
cg.subject.impactAreaClimate adaptation and mitigation
cg.subject.impactAreaNutrition, health and food security
cg.subject.sdgSDG 1 - No povertyen
cg.subject.sdgSDG 5 - Gender equalityen
cg.subject.sdgSDG 13 - Climate actionen
cg.subject.sdgSDG 17 - Partnerships for the goalsen
dc.contributor.authorNganga, Kevin Gitauen
dc.contributor.authorGrossi, Amandaen
dc.contributor.authorWanjau, Agnes Njambien
dc.date.accessioned2025-06-25T04:14:35Z
dc.date.available2025-06-25T04:14:35Z
dc.identifier.urihttps://hdl.handle.net/10568/175290
dc.titleAdvancing Gender Equity in Digital Agro-advisory through Inclusive Artificial Intelligence (AI): Bias Analysis and Strategic Recommendations from the iShamba Platform Based on Five Years of Female Farmers’ Queriesen
dcterms.abstractThis working paper presents a comprehensive bias analysis of iShamba’s digital agro-advisory service and provides strategic recommendations to gender-inclusive artificial intelligence (AI) that will be integrated to the platform. Using five and half years of farmer query data (year 2020–2025), we identify multiple biases, including gender participation gaps, referral biases in query resolution, language and literacy-related issues, prevalence of very short queries, content duplication, regional usage disparities, and inconsistencies in units/currency references. The Methodology outlines our data-driven approach and discusses data filtering challenges. A Background and Literature Context situates iShamba within broader efforts on gender-responsive AI in agriculture, drawing on AICCRA, CGIAR, and global studies. In Findings, we quantify each bias and visualize, illustrating how women farmers are underserved or mis-served. We discuss how these biases can undermine equitable service delivery, for instance, how language barriers or regional gaps disproportionately affect women and marginalised groups. The Recommendations section links directly to each identified bias, suggesting interventions such as inclusive AI training data, human oversight mechanisms, data standardisation (for units and terms), multilingual support, and proactive inclusion strategies to engage women farmers. By addressing biases and centering gender inclusion, we can enhance the quality and equity of advisory services. This paper serves as a resource for practitioners and researchers seeking to ensure that AI-driven agricultural advisory tools are fair, context-aware, and accessible to all farmers regardless of gender or region.en
dcterms.accessRightsOpen Access
dcterms.bibliographicCitationNganga K, Grossi A, Wanjau A. 2025. Advancing Gender Equity in Digital Agro-advisory through Inclusive Artificial Intelligence (AI): Bias Analysis and Strategic Recommendations from the iShamba Platform Based on Five Years of Female Farmers’ Queries. AICCRA Working Paper. Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA)en
dcterms.extent27 p.en
dcterms.issued2025-06-20
dcterms.languageen
dcterms.licenseCC-BY-ND-4.0
dcterms.publisherAccelerating Impacts of CGIAR Climate Research for Africa
dcterms.subjectclimate changeen
dcterms.subjectdigital agricultureen
dcterms.subjectartificial intelligenceen
dcterms.subjectdesignen
dcterms.subjectinclusionen
dcterms.subjectwomen farmersen
dcterms.subjectclimate actionen
dcterms.typeWorking Paper

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