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Multilingual and Contextual AI Evaluation call for proposals - Fall 2026 

Evaluating AI across the world's languages

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About this CFP

AI systems are transforming daily life around the world, and it is critical they work well for everyone. As AI adoption accelerates globally, expanding benchmarks and datasets beyond high-resource languages can unlock the potential of AI for billions of people, including in the Global South. By investing in the infrastructure to assess and improve AI systems across languages and cultural contexts, we can drive meaningful impact where it matters most. Evaluating AI systems responsibly across languages and cultures for safety and fairness, with human judgment in the loop is central to this work. AI systems that are not evaluated in the languages and contexts where they are used can carry harms that standard benchmarks never surface.

In February 2026, Amazon signed the New Delhi Frontier AI Impact Commitments at the India AI Impact Summit. The second commitment, "Strengthening Multilingual and Use-Case Evaluations," asks signatories to:

  • Collaborate with governments and local ecosystems to develop datasets, benchmarks, and expertise for evaluating AI systems in underrepresented languages and cultural contexts
  • Prioritize the Global South in this work
  • Support evaluation infrastructure that does not yet exist at scale

This ARA Call for Proposals is how we're making progress on that commitment. We're looking for proposals from academic institutions and non-profits that tackle multilingual AI evaluation, culturally contextual AI, or AI deployment in emerging markets.

Amazon already invests in multilingual and cross-lingual AI through efforts like Bedrock Cross-Lingual Evaluation, SWE-PolyBench, Amazon Translate, and AWS-enabled projects like Kiwa. This call extends that work by funding external research, with a particular focus on outreach to include institutions in the Global South.

Areas of interest

We're looking for proposals in the following areas, especially work that benefits underrepresented languages, cultures, and regions:

Benchmarks for under-represented languages

Building datasets and evaluation frameworks for low-resource languages - for example, Indic/South Asian, African, Southeast Asian, Latin American (Indigenous), Middle East and indigenous languages. This includes new benchmarks for tasks like machine translation, question answering, summarization, and conversational AI in languages where evaluation resources are scarce or don't exist yet.

Culturally contextual AI evaluation

Developing evaluation methods that account for local cultural norms, values, and real-world use cases, so AI systems work appropriately for a wide range of populations. This covers context-sensitive safety evaluation, localized alignment metrics, and evaluation that incorporates community feedback and Indigenous knowledge. This includes evaluation infrastructure at scale - tools, platforms, and pipelines for multilingual and cross-cultural AI testing.

Application of AI in emerging markets

Studying how AI models are actually deployed in emerging economies - real-world usage patterns, infrastructure and data constraints, and what enables or limits effective adoption in sectors like healthcare, agriculture, education, financial services, and public administration. We also welcome research on how deployment conditions in the Global South differ from assumptions in standard AI evaluation work.

Additional welcome topics

Other related topics are also welcome, including but not limited to:

  • Low-resource data collection methods, including synthetic data generation and data augmentation for underrepresented languages
  • Human-in-the-loop and community-sourced evaluation
  • Responsible AI for multilingual systems - fairness, safety, and transparency across languages
  • Cross-lingual transfer learning and its evaluation
  • AI literacy and capacity building for evaluation in Global South institutions

Timeline

Submission period: October 1 - November 4, 2026 (11:59PM Pacific Time).
Decision letters will be sent out in February 2027.

Award details

Selected Principal Investigators (PIs) may receive the following:

  • Unrestricted funds, no more than $50,000 USD on average
  • AWS Promotional Credits, no more than $50,000 USD on average
  • Training resources, including AWS tutorials and hands-on sessions with Amazon scientists and engineers

Awards are structured as one-time unrestricted gifts. The budget should include a list of expected costs specified in USD, and should not include administrative overhead costs. The final award amount will be determined by the awards panel.

Eligibility requirements

Please refer to the ARA Program rules on the Rules and Eligibility page.

Proposal requirements

Proposals should be prepared according to the proposal template and are encouraged to be a maximum of 4 pages, not including Appendices.

In addition, please include the following information:

  • How does your research address the needs of underrepresented languages, cultures, or regions? Describe the specific communities or linguistic contexts your work will benefit.
  • What datasets, benchmarks, or evaluation frameworks do you plan to create or extend? How will these be made publicly available?
  • How does your research differ from or build upon existing work in multilingual or cross-cultural AI? What makes your approach innovative?
  • Please list the AWS ML tools you plan to use (e.g., Amazon SageMaker, Amazon Bedrock, Amazon Translate).
  • If applicable, describe any partnerships with institutions, governments, or communities in the Global South.

Selection criteria

ARA will make funding decisions based on the following criteria:

  • Potential impact on multilingual AI evaluation, particularly for underrepresented languages and the Global South
  • Scientific rigor, novelty, and quality of the proposed methodology. Rigor of the responsible-AI approach in the evaluation of multilingual and culturally contextual AI systems
  • Feasibility of the research plan within the one-year award period
  • Commitment to open-source release of datasets, benchmarks, and tools
  • Alignment with the New Delhi Frontier AI Impact Commitments & partnership with institutions, governments, or local communities.
  • Use of AWS AI/ML services (e.g., Amazon SageMaker, Amazon AI services, Amazon Bedrock)

Expectations from recipients

To the extent deemed reasonable, award recipients may acknowledge support from ARA (e.g., (“Research reported in this [publication/press release] was supported by an Amazon Research Award, [Cycle /Year].“). Award recipients will inform ARA of publications, presentations, code and data releases, blogs/social media posts, and other speaking engagements referencing the results of the supported research or the Award. Award recipients are expected to provide updates and feedback to ARA via surveys or reports on the status of their research. Award recipients will have an opportunity to work with ARA on an informational statement about the awarded project that may be used to generate visibility for their institutions and ARA.

How to apply
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