AI-Enabled Farmer Advisory: Risk Taxonomy
October 13, 2026Authors: Jona Repishti, Namita Singh and Sara Chamberlain
What is the taxonomy?
The AI-Enabled Farmer Advisory Risk Taxonomy defines and categorizes risks that can stop AI advisory from benefiting farmers. They’re grouped into 10 “gates”: access, usability, relevance, accuracy, adoption, norms, income, accountability, environment and privacy.
Together, the gates trace a farmer’s path from first hearing about an advisory service, to earning from its advice. They also cover the wider effects on communities and the environment. Each risk names an assumption the product’s designers made, and what can go wrong when that assumption doesn’t hold. Gender is applied as a lens across the gates rather than treated as a separate category.
This taxonomy complements multi-level AI evaluation frameworks in the social sector, including The Agency Fund’s Living Playbook for AI Evaluation in the Social Sector. While those frameworks focus on evaluation approaches and tools, this taxonomy tells you what to look for at each stage of a farmer’s journey, gate by gate, identifying the specific failure conditions that can undermine success, while clarifying who may be disproportionately exposed or disadvantaged.
It doesn’t rank risks or prescribe fixes. Instead, it gives teams a shared vocabulary to spot, prioritize, monitor and address risks at every stage of an advisory service.
What risks does the taxonomy cover?
AI-enabled farmer advisory does not fail in one place. It can fail across multiple stages, even when the underlying AI models perform well. Together, these 10 gates capture where advisory systems can break down across technical performance, product design, user constraints, structural inequities, economic outcomes, environmental effects, and data governance.
Our taxonomy maps risk exposure across ten domains:
Advice can be accurate
but never reach the farmer.
Show all ten as a list
- Advice can be accurate but never reach the farmer (Gate 1: Access).
- Advice can reach the farmer but be hard to follow (Gate 2: Usability).
- Advice can be understood but not fit the farm, or the farmer's role or goals (Gate 3: Relevance).
- Advice can fit the farm but still be wrong, out of date or unsafe (Gate 4: Accuracy).
- Advice can be right but not acted on, or carried out poorly (Gate 5: Adoption).
- Advice can be right, but app use puts the farmer at odds with their household or community (Gate 6: Norms).
- Advice can increase yield or output but not income (Gate 7: Income).
- Advice can raise income for some but leave others behind, with no recourse when it's wrong (Gate 8: Accountability).
- Advice can pay off today but damage land and water for the future (Gate 9: Environment).
- Advice can pay off but expose the farmer to data privacy risks (Gate 10: Privacy).
The taxonomy is also explicitly designed to assess:
- Gender-differentiated risks: reflecting structural exposure faced by women and marginalized farmers
- AI-specific risks such as bias, hallucination, opacity, profiling, and data misuse
How to use the taxonomy
This taxonomy is a structured tool for identifying and managing risks when deploying AI-enabled advisory services for smallholder farmers. It is designed for product developers, implementers, donors, evaluators, researchers and policymakers. There are four ways to use it:
Embed it in design.
Use the ten gates to identify where things could break down in your context, and for whom, at every stage of the product life cycle. As features and program approaches change, reassess how severe each failure would be and who is exposed to it. Keep designing, testing and updating safeguards.
Align key stakeholders.
Use the taxonomy to widen discussions beyond reach and accuracy. Identify where risks to equity, safety, livelihoods, the environment or data rights may be overlooked, and agree who is responsible for addressing each one.
Strengthen evaluation.
When engagement or outcomes stall, use the risks to pinpoint where the breakdown is happening, whether in the model, the product, how people use it, or its impact. Look past surface symptoms to find the root causes.
Apply a gender and AI lens.
Use the risks to identify the structural disadvantages women farmers face, and the AI risks to identify weaknesses in algorithms and data governance. Prioritize risks that are both severe and likely to deepen inequality for monitoring and mitigation.
The gates
Gate 1 of 10
Access
Do farmers ever hear about the app so they can install and use it?
Who can access and discover the app is usually determined by choices made early in a product's life: which channels distribute it, what device and skills it requires, who vouches for it, what it costs to run, and how and how often it reaches people, including women. Each choice encodes an assumption about the intended users: that they learn about the product, own a certain type of device, control it, can afford to run it, and have access to the right kind of network. These assumptions often hold for some users and not others. They also compound: a barrier that blocks the first install tends to resurface later, eroding sustained use.
Assessing these risks helps inform critical design choices around distribution channels, marketing and partnerships, device requirements and pricing.
Reach risk
Distribution and onboarding efforts reach a small number of intended users.
Endorsement risk
Relies on distribution channels that carry low trust signals (i.e. no vouching of peers, institutions, experts).
Device availability and connectivity risk
Requires a phone (class, storage, quality) and network type that most intended users don't have access to.
Device agency risk
Assumes the user controls the phone enough to install the app and use it regularly.
Affordability risk
App costs (i.e. a download fee and/or data bundles) make regular use challenging or out of reach.
