Bias-Aware AI in Agricultural Extension: Enhancing Equity and Inclusivity Through Human-Centered Design
This Info Note explores the transformative potential of Artificial Intelligence (AI), particularly Large Language Models (LLMs), in agricultural extension services. It emphasizes the importance of inclusivity and bias mitigation to ensure equitable outcomes for smallholder farmers, women, and margin...
| Main Authors: | , |
|---|---|
| Format: | Poster |
| Language: | Inglés |
| Published: |
Accelerating Impacts of CGIAR Climate Research for Africa
2025
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/174306 |
| _version_ | 1855530775728881664 |
|---|---|
| author | Nganga, Kevin Gitau Ghosh, Aniruddha |
| author_browse | Ghosh, Aniruddha Nganga, Kevin Gitau |
| author_facet | Nganga, Kevin Gitau Ghosh, Aniruddha |
| author_sort | Nganga, Kevin Gitau |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This Info Note explores the transformative potential of Artificial Intelligence (AI), particularly Large Language Models (LLMs), in agricultural extension services. It emphasizes the importance of inclusivity and bias mitigation to ensure equitable outcomes for smallholder farmers, women, and marginalized groups. While LLMs can enhance climate resilience and decision-making by offering timely, context-aware advisories, they risk reinforcing systemic biases if not carefully designed. Moreover, it advocates for the integration of Human-Centered Design (HCD) principles and participatory methods throughout AI development to align technologies with diverse user needs. A novel methodology using the DALL·E image generation model demonstrates how prompt engineering can mitigate stereotypical representations in AI outputs. By combining ethical AI practices, localized insights, and inclusive visual and textual content, the InfoNote presents a roadmap for equitable innovation in agronomic and climate information systems. Policy and governance recommendations to foster trust, transparency, and broad adoption of AI tools in agriculture are also outlined. |
| format | Poster |
| id | CGSpace174306 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Accelerating Impacts of CGIAR Climate Research for Africa |
| publisherStr | Accelerating Impacts of CGIAR Climate Research for Africa |
| record_format | dspace |
| spelling | CGSpace1743062025-11-11T16:35:00Z Bias-Aware AI in Agricultural Extension: Enhancing Equity and Inclusivity Through Human-Centered Design Nganga, Kevin Gitau Ghosh, Aniruddha artificial intelligence inclusion communities This Info Note explores the transformative potential of Artificial Intelligence (AI), particularly Large Language Models (LLMs), in agricultural extension services. It emphasizes the importance of inclusivity and bias mitigation to ensure equitable outcomes for smallholder farmers, women, and marginalized groups. While LLMs can enhance climate resilience and decision-making by offering timely, context-aware advisories, they risk reinforcing systemic biases if not carefully designed. Moreover, it advocates for the integration of Human-Centered Design (HCD) principles and participatory methods throughout AI development to align technologies with diverse user needs. A novel methodology using the DALL·E image generation model demonstrates how prompt engineering can mitigate stereotypical representations in AI outputs. By combining ethical AI practices, localized insights, and inclusive visual and textual content, the InfoNote presents a roadmap for equitable innovation in agronomic and climate information systems. Policy and governance recommendations to foster trust, transparency, and broad adoption of AI tools in agriculture are also outlined. 2025 2025-04-23T21:36:57Z 2025-04-23T21:36:57Z Poster https://hdl.handle.net/10568/174306 en Open Access application/pdf Accelerating Impacts of CGIAR Climate Research for Africa Nganga K. Ghosh A. 2025. Bias-Aware AI in Agricultural Extension: Enhancing Equity and Inclusivity Through Human-Centered Design. AICCRA InfoNote. Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA). |
| spellingShingle | artificial intelligence inclusion communities Nganga, Kevin Gitau Ghosh, Aniruddha Bias-Aware AI in Agricultural Extension: Enhancing Equity and Inclusivity Through Human-Centered Design |
| title | Bias-Aware AI in Agricultural Extension: Enhancing Equity and Inclusivity Through Human-Centered Design |
| title_full | Bias-Aware AI in Agricultural Extension: Enhancing Equity and Inclusivity Through Human-Centered Design |
| title_fullStr | Bias-Aware AI in Agricultural Extension: Enhancing Equity and Inclusivity Through Human-Centered Design |
| title_full_unstemmed | Bias-Aware AI in Agricultural Extension: Enhancing Equity and Inclusivity Through Human-Centered Design |
| title_short | Bias-Aware AI in Agricultural Extension: Enhancing Equity and Inclusivity Through Human-Centered Design |
| title_sort | bias aware ai in agricultural extension enhancing equity and inclusivity through human centered design |
| topic | artificial intelligence inclusion communities |
| url | https://hdl.handle.net/10568/174306 |
| work_keys_str_mv | AT ngangakevingitau biasawareaiinagriculturalextensionenhancingequityandinclusivitythroughhumancentereddesign AT ghoshaniruddha biasawareaiinagriculturalextensionenhancingequityandinclusivitythroughhumancentereddesign |