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With research staff from more than 70 countries, and offices across the globe, IFPRI provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition in developing countries.

researcher spotlight

Lilia Bliznashka

Lily Bliznashka is a Research Fellow in the Nutrition, Diets, and Health Unit. Her research focuses on assessing the effectiveness of multi-input nutrition-sensitive and nutrition-specific interventions and the mechanisms through which they work to improve maternal and child health and nutrition globally. She has worked in Burkina Faso, Burundi, Tanzania, and Uganda.

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Since 1975, IFPRI’s research has been informing policies and development programs to improve food security, nutrition, and livelihoods around the world.

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Where we work

IFPRI currently has more than 480 employees working in over 70 countries with a wide range of local, national, and international partners.

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Projects

The AI For Food Systems Research initiative is bringing together researchers and practitioners across CGIAR and its partners to explore how artificial intelligence (AI) can be designed, governed, and applied responsibly in food systems research and innovation. Amid the growing use of AI in agricultural research, policy, and extension, the initiative investigates how emerging technologies shape the ethics, equity, and effectiveness of research processes and outcomes. It supports the development of ethical review processes, research agendas, and institutional guidance for responsible AI innovation.

Project duration: 2025 – Ongoing

Contact

Eliot Jones-Garcia

Partners

Digital Green

Publications

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Journal Article

Asking the right question: Toward a research agenda for responsible GAI in agricultural extension

2026Jones-Garcia, Eliot; Singaraju, Niyati; Davis, Kristin E.; Koo, Jawoo
Details

Asking the right question: Toward a research agenda for responsible GAI in agricultural extension

This study explores how generative AI (GAI) tools for agricultural extension can be designed and evaluated more responsibly. While current GAI systems offer scalable, personalized advice, they often ignore the lived realities of smallholder farmers—especially women—by relying on generic datasets and rigid evaluation metrics. We investigate three complementary methods: adversarial testing to expose gendered and contextual blind spots in model outputs; deliberative stakeholder engagement using the C-H-A-T framework, which focused on Collective knowledge, Human insight, Augmentation, and Trust, to surface value tensions and design trade-offs; and field-level insights from extension officers to uncover trust-building, diagnostic reasoning, and social intelligence absent from static GAI interactions. Together, these approaches reveal that responsible GAI requires more than technical accuracy. It demands participatory design processes that foreground user realities, surface stakeholder assumptions, and account for social and institutional context. We recommend developing gender-responsive benchmarks, embedding reflexive, participatory design methods, and modeling advisory reasoning based on real-world extension practice. The findings contribute to a growing agenda for responsible AI development—highlighting the importance of aligning GAI tools not only with technical goals, but with the social, cultural, and political contexts in which they operate.

Year published

2026

Authors

Jones-Garcia, Eliot; Singaraju, Niyati; Davis, Kristin E.; Koo, Jawoo

Citation

Jones-Garcia, Eliot; Singaraju, Niyati; Davis, Kristin E.; and Koo, Jawoo. 2026. Asking the right question: Toward a research agenda for responsible GAI in agricultural extension. Advancements in Agricultural Development 7(2): 35-49. https://doi.org/10.37433/aad.v7i2.633

Keywords

Gender; Women Farmers; Smallholders; Artificial Intelligence; Agricultural Extension; Evaluation

Language

English

Access/Licence

Open AccessCC-BY-4.0

Links

https://doi.org/10.37433/aad.v7i2.633 Source record

Record type

Journal Article

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Journal Article

Man vs. machine: Multi-country experimental evidence on the quality and perceptions of AI-generated research blog content

2026Keenan, Michael; Karachiwalla, Naureen; Koo, Jawoo; Mwangi, Christine Wamuyu; Breisinger, Clemens; Kim, MinAh
Details

Man vs. machine: Multi-country experimental evidence on the quality and perceptions of AI-generated research blog content

Academic research is not always available in a form that is accessible or engaging to a non-academic audience, hindering readers’ engagement with it. Non-academics, even if highly educated and policy experts in their fields, tend to need research to be presented in a more accessible way than peer-reviewed articles — one example being non-technical blogs. However, writing these requires some effort from researchers. Artificial Intelligence (AI) tools can make academic research easier to understand by summarizing and simplifying academic papers much more quickly than researchers can, making it easier for researchers to produce such summaries. However, disclosure of AI use may lower readers’ perceived quality of and trust in the blog, generating a trade-off for the researcher. In this paper, we evaluate an 11-country experiment cross-randomizing a blog’s actual and reported author as AI or human. We find that research stakeholders rate the quality of AI-generated blogs marginally lower than human-written ones (p 0.1), but disclosure of AI use offsets the negative effect (p 0.1). The study sample consists of policy-relevant stakeholders who typically engage with academic research; they are highly educated and include thematic specialists. Indeed, findings indicate that this audience interprets “accessibility” differently, preferring slightly more technical summaries of research. The nature of the respondents may thus explain the particular findings in this study, suggesting that researchers should tailor their prompts for their intended audience. There are no effects on readers’ reported likelihood of engaging with the blog or on beliefs about others predicted engagement with it. Consequently, we hypothesize that researchers can leverage AI to communicate their research more easily without a penalty from disclosing its use.

Year published

2026

Authors

Keenan, Michael; Karachiwalla, Naureen; Koo, Jawoo; Mwangi, Christine Wamuyu; Breisinger, Clemens; Kim, MinAh

Citation

Keenan, Michael; Karachiwalla, Naureen; Koo, Jawoo; Mwangi, Christine Wamuyu; Breisinger, Clemens; and Kim, MinAh. 2026. Man vs. machine: Multi-country experimental evidence on the quality and perceptions of AI-generated research blog content. PLoS One 21(3): e0342852. https://doi.org/10.1371/journal.pone.0342852

Keywords

Artificial Intelligence; Generative Artificial Intelligence; Research; Quality

Language

English

Access/Licence

Open AccessCC-BY-4.0

Links

https://doi.org/10.1371/journal.pone.0342852 Source record

Project

Digital Innovation

Record type

Journal Article

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Report

Risk and ethics assessment and the GAIA ethics toolkit

2025CAB International
Details

Risk and ethics assessment and the GAIA ethics toolkit

Generative AI (GenAI) is rapidly reshaping agricultural advisory systems, offering new opportunities for climate resilience, productivity gains, and improved decision-making. Yet its adoption in agriculture—particularly in smallholder contexts—comes with significant sociotechnical and ethical risks. These risks arise from data quality limitations, unequal power dynamics, opaque algorithms, and gaps in accountability and governance. This report outlines these emerging challenges and presents the rationale, structure, and expected impact of a forthcoming GAIA Ethics Toolkit designed to ensure responsible development and deployment of GenAI-enabled agricultural advisory systems. Global conversations around data governance, including outcomes from the 2024 UN World Data Forum, stress the importance of equitable and FAIR data ecosystems. In agriculture, where digital innovation intersects closely with farmer livelihoods, environmental stability, and national strategies, responsible AI development is essential. Applications such as crop management chatbots, long-lead weather forecasting, and AI-driven pest detection tools already demonstrate strong potential. However, they also highlight the vulnerabilities introduced by poor data representativeness, model opacity, privacy risks, and the marginalisation of traditional knowledge.

Year published

2025

Authors

CAB International

Citation

CABI. 2025. Risk and ethics assessment and the GAIA ethics toolkit. CAB International. https://hdl.handle.net/10568/181439

Keywords

Risk; Ethics; Frameworks; Artificial Intelligence

Language

English

Access/Licence

Open Access

Source

Source record

Record type

Report

Blogs

Beyond the algorithm: The need for farmer participation and data justice in digital agricultural technology
Issue Post
 | 
December 02, 2025

Beyond the algorithm: The need for farmer participation and data justice in digital agricultural technology

By Ayorinde Ogunyiola

Addressing systemic disadvantages.

Generative AI’s environmental footprint poses difficult tradeoffs for agrifood systems in low- and middle-income countries
Issue Post
 | 
October 24, 2025

Generative AI’s environmental footprint poses difficult tradeoffs for agrifood systems in low- and middle-income countries

By Amy Margolies

Costs and benefits of data centers.

Asking the right questions: A stakeholder dialogue on generative AI in digital extension
Research Post
 | 
October 07, 2025

Asking the right questions: A stakeholder dialogue on generative AI in digital extension

By Eliot Jones-Garcia, Kristin Davis, and Niyati Singaraju

Balancing new technology with local needs.

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Events

AI and Remote Sensing for Agricultural Insights: From Satellite Imagery to Decision Support 
IFPRI Program/Country Event

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November 20, 2025, 9:30 – 10:30 am

AI and Remote Sensing for Agricultural Insights: From Satellite Imagery to Decision Support 

How can advances in AI and remote sensing improve agricultural decision-making, particularly in contexts where timely, accurate data is difficult to obtain? This webinar explores the intersection of machine learning and satellite imagery, focusing on innovations that enhance monitoring, planning, and climate resilience in food systems.  Ambica Paliwal will share insights on how remote sensing […]

Benchmarking LLMs for Agricultural Advisory: Insights from a Global Community of Practice 
IFPRI Program/Country Event

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November 6, 2025, 9:30 – 10:30 am

Benchmarking LLMs for Agricultural Advisory: Insights from a Global Community of Practice 

As large language models (LLMs) are increasingly applied in agricultural advisory services, there is growing recognition of the need for shared benchmarks to evaluate their performance, equity, and contextual relevance. In response, a global community of practice, supported by the Gates Foundation, has begun developing standards and tools to assess how well LLMs perform across […]

Launching the Ethical AI Methods Toolkit: Reflections from Research and Practice
IFPRI Program/Country Event

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October 23, 2025, 9:00 – 10:00 am

Launching the Ethical AI Methods Toolkit: Reflections from Research and Practice

What does it mean to design ethical AI for agricultural research and development—and how can researchers and practitioners begin to put principles into practice? Following our previous webinar on A Problem-Oriented Approach to AI Ethics in Food Systems, this event marks the launch of the Ethical AI Methods Toolkit: a practical resource developed to support […]

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News

February 17, 2026 | Press Release

IFPRI and Farm Radio International collaborate to give a voice to the most vulnerable farmers by advancing AI tools for African languages

Introducing Longa, an AI-powered speech recognition tool designed to strengthen agricultural communication and inclusivity across sub-Saharan Africa.

October 06, 2025 | In the News

Agriculture Ministry and LIFAAS host generative AI workshop to strengthen extension services (Ministry of Agriculture, Liberia)

With support from IFPRI, the workshop brought together diverse stakeholders to explore how generative AI can close extension gaps and enhance the reach and impact of agricultural advisory services in Liberia.

Team members

Jawoo Koo

Jawoo Koo

Senior Research Fellow, Natural
Resources and Resilience

Other

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