This document explains how AI should and should not be used at Tattle. The document is primarily meant for people working at Tattle but it may also be useful for clients, funders and partner organizations who work with Tattle.
Process of Drafting
The policy was jointly drafted by the nine employees at Tattle in February 2026. The group members had training in journalism, design, anthropology, data science and engineering. The specific activities we undertook are:
- A poll to understand disposition towards using AI. The perception in the team ranged from 'don't want to use AI' to 'try as many tools as possible'.
- Reading and discussing papers ([1],[2],[3]) to build understanding of how LLMs work, and criticisms of the technology.
- Doing demos of how we use them in our individual workflows.
- Mapping what AI tools are good at and not good at.
- Mapping intuitions for where AI should be used and AI should not be used.
- Drafting policy collaboratively based on discussion.
General Guiding Principles
- Writing is also thinking. There was general consensus that using AI for writing can reduce the ability to think deeply or critically, which is central to the work we do.
- Communication is between people. Respect the other person's time.
- AI is always only an assistant. Don't go to it with an empty slate or ambiguous problems wanting it to generate ideas from scratch. Use AI once the problem statement is clearly articulated.
- Consent and privacy: Don't use AI on other people's information and data without their permission.
License
The AI Use Policy by Tattle is being shared under CC BY 4.0 license. Others are open to sharing and adapting this document with attribution.
Revisions
This policy is in effect from March, 2026 and will be revised as use cases and our own understanding of the capabilities of AI in Indian languages evolves.
Use Cases
The rest of this document describes policy for specific use cases. The general points covered for each use case are:
- Redlines: when not to use
- Disclosures: What to disclose, when to disclose, who to disclose to
- Best practices on prompting for the use case: At what stage in the process should you prompt, and what kind of prompts should you use?
The policy for each use case was written by the team members most involved in that use case. The tone of the policy for each use case will differ.
Research and Writing
This section pertains to Tattle's policy for using AI tools for writing and generating content. Writing encompasses the following:
1. Applications: Grants, proposals for collaborations, pitches for Tattle organizations.
- What to use for:
- Tailor existing grant applications or publicly available research
- Applications that need technical writing to explain Tattle's technical expertise and digital products.
- What to not use for:
- Don't feed the application in its entirety to generate a response. Instead interpret the questions and give detailed instructions on what kind of response is expected.
2. Administrative: Writing about Tattle, within Tattle or with clients. Emails, internal communication via Slack, event documentation.
- What to use for: Formatting, grammar and translation.
- What to not use for: Meeting notes, call documentation.
- Caution: In case of external/client communication, review with a supervisor/peer.
3. Workshops and Educational Material: Lesson plans, content and decks.
- What to use for: Broadly to build on ideas but don't completely rely on it. Review it and detail it out.
4. Research Writing: Articles, papers and reports.
- What to use for: Formatting and Grammar.
- What to not use for: AI based language translation should not be used for research writing.
5. Creative Writing: Blogs, Social media content.
- What to use for: Formatting, grammar and translation (see section on translation).
- Caution: If using to polish the writing, review the tone and messaging.
Prohibitions
- Do not upload any material to a free AI tool that is under NDA, unpublished or contains personal information. For e.g. datasets from clients, unpublished reports or policies of Tattle, medical records, personally identifiable information.
- Tattle's socio-political position on any subject must not be AI generated.
Disclosures
- Within Tattle:
- Always inform the supervisor or a peer/collaborator about AI use in any writing and a summary of the prompt used.
- Share rough drafts of emails, documentation with supervisor or reporting peer before feeding to AI tools for generating polished versions. This allows the person to develop a voice and standpoint over time. It also prevents critical information going missing from AI based translation into specific formats like blogs, knowledge sharing documentation, etc.
- Outside Tattle:
- Disclose use of AI to clients while operating with NDA bound data.
- In applications for grants and proposals, be truthful about use of AI if there is a question for disclosure.
Best Practices
- Don't use AI tools to generate text from a blank slate.
- Always prepare detailed outlines when prompting AI tools to polish language or generate prose.
- Example: Instead of "Create a blog on evaluation of AI safety" instead provide clear objectives, headers, main arguments, and examples.
- AI generated content must be proofread for the following in addition to grammar:
- Vocabulary, Quotes, Arguments, Citations, Facts and Figures, punctuation.
- Review must be done by someone other than the author.
Research Methodology
At the current level of LLM capabilities, we do not find AI tools to be reliable for developing summaries of literature for research and transcription. Therefore, we will not use AI tools for the purpose of summarizing articles and books for research or for transcribing interviews, discussions, and calls.
Coding
This policy is written with three personas in mind:
- Developer: The primary person responsible for translating feature description into functional code
- Reviewer: One or more people responsible for reviewing a Developer's PR and ensuring that it meets the acceptance criteria for a feature
- Maintainer: One or more people who engage with code written by the Developer and reviewed by the Reviewer after the code has been merged by the Reviewer. This engagement could involve updating the feature or building other new functionalities that intersect with the previously written code
General Process
We will use our existing good practices and workflows for code review at Tattle. Broadly this means that every feature or task is written up in detail on a github issue. Any discussions about the issue happen on the issue comments. When a developer has implemented the feature, they make a PR against the dev branch. Reviewer reviews the code and adds inline comments on the PR which a developer addresses. They can use the PR comment section for discussing and resolving differences. When the PR looks satisfactory, it is merged into the dev branch.
Common use cases we can envision at this stage are:
- Developers using AI to help them write the code, tests and documentation for a feature
- Reviewers using AI to review a PR
- Maintainers using AI to expedite onboarding on a codebase
Prohibitions
- Developers should not use AI to write PR notes. Using AI to polish the text a little bit is acceptable but the goal here is to maximize for a Reviewer's comprehension, so a developer should be respectful of reviewer's time and write very thoughtful self explanatory comments to ensure the reviewer has all the information they need to understand and review the PR.
- Developers should not push code they haven't tested manually for review.
Disclosures
The dev team does not feel the need to disclose at what stage a developer, reviewer or maintainer used AI. Nor do we think it is practical — a vague statement like "coauthored with AI" is not helpful and working with AI is an iterative process. So, collecting all the prompts used and sharing them in the PR notes would not be practical.
It is more important to be respectful to your colleague's efforts and be accountable for the quality of the work you submit.
Suggestions
- The dev team should try out new AI optimizations within the regular PR workflow as long as the policy mentioned above is respected.
- Maintainers can use AI to guide their onboarding onto a code base.
Translation
This is an official policy of Tattle for using AI tools for translation and content assistance.
Do Use
- You can use it to understand a specific word in detail.
- You can do the task and issue for personal understanding.
- Sort information.
- Communication.
- You can translate documentation and emails for your own understanding, but they should not contain any information that is not meant to be published.
- Personal script for speech.
- Blog posts or other content that is already published can be used.
- It can be used for writing a PR description, but only after reviewing it yourself.
Do Not Use
- Employees must not copy and paste any complete document, page, or content containing personal or confidential information into any AI tool for understanding or translation purposes.
- Any email, document, or content that is to be officially published must not be entirely generated or fully translated by AI. The final content must be carefully reviewed and edited before publishing.
- AI translation may be used to assist in preparing spoken speeches or verbal communication. However, AI-translated content must not be directly submitted or delivered as official written material without proper human review and rewriting.
AI Use Disclosure Guidelines
When to disclose: Disclosure should be made when AI tools have been used to assist in drafting, translating, or significantly editing any official content intended for external publication or public distribution.
Who to disclose to: Disclosure should be made to the relevant supervisor, project lead, or the person approving before the content is finalized or published.
What to disclose: The disclosure should clearly state:
- That AI tools were used.
- The purpose of use (e.g., translation support, drafting assistance).
- Confirmation that the final content has been reviewed and edited by a human.
Image Generation
The team doesn't find AI based image generation good or useful for its work. Thus we don't have a policy for it.
Hiring
Tattle will not use AI in reviewing CVs or in categorizing or filtering candidates from a pool of applicants.
Project Planning
Encompasses:
1. Workshop session planning
- Good to use to find ice breaker and engaging activities around the focus area of the workshop.
- Broad structuring can be done through AI but the facilitator should ensure thorough vetting and ideate it critically.
- Bad use case:
- Designing the entire workshop through AI
- Finding workshop content material on AI.
2. Project planning: journalistic projects, program development, product planning
- Good to use to create tasks
- Task calendar
Given how we often don't have people with Project management training at Tattle, AI tools can support in structuring project plans.
Disclosure: Internally disclose AI usage.