ACTS ML helps innovators build AI that serves real communities responsibly. These guidelines walk you through defining a problem well, collecting data fairly, and preparing it soundly. They are practical guidance — meant to help you build better models, not to slow you down. The platform links back to the relevant section of this page as you go.
Before you build: co-define the problem
The most important decisions happen before any model is trained. Ask yourself:
- Who is your anticipated user community? If you are building this AI for your own personal use and from your own personal data, the community questions below don't apply.
- What exactly do you want to predict or diagnose? For example, "how much rain will fall today".
- Could a non-AI method solve this instead? If yes, that is often the more responsible choice — simpler, cheaper, and easier to trust. Only build a model when the problem genuinely calls for one.
- What factors or variables influence what you want to predict? These become your input (independent) variables, alongside the target variable you want to predict.
- Did you co-define the problem with a representative sample of the user community — across age, gender, persons with disability, poverty levels, and geographic distribution?
Privacy & Confidentiality Protocol
When you collect opinions or data from a user community, you take on a duty of care over their information. This protocol sets the baseline we expect on the platform.
- Informed consent. People should understand what data is being collected, why, and how it will be used, and should agree to it freely before collection begins.
- Trusted engagement. Interact with the community through a person they trust, using a language they understand well. This protects both the quality of the data and the dignity of the people it comes from.
- Confidentiality. Restrict access to personal data to those who genuinely need it. Do not share data outside your organisation unless it has been explicitly cleared for sharing.
- Anonymisation. Where data is personal, remove or mask identifying details before using it to train models, and before sharing it in any form.
- Public vs. private. Only mark a dataset "open-source" when you have the right to share it and it contains no sensitive personal information.
Data Management Protocol
Responsible AI depends on well-managed data. Following a consistent protocol keeps your dataset trustworthy over its whole life.
- Representativeness. Collect data from a representative sample of the user community — across age, gender, persons with disability, poverty levels, and geographic distribution. Note the dimensions your data does and does not cover.
- Provenance. Record where the data came from: who collected it, when, where, by what method, and whether it is primary (collected directly) or secondary (obtained from another source).
- Structure. Organise your data with the variable you want to predict as the target, and the influencing variables as the inputs. A spreadsheet (CSV or Excel) with clear column headers is ideal.
- Purpose & scope. Be clear about why the data was collected and whether it represents a sample or an entire population.
- Stewardship. Store data securely, control who can access it, and keep it only as long as it is needed for the stated purpose.
Preprocessing Protocol
Before data is used to train a model, it should be prepared carefully so the model learns from clean, well-formed inputs.
- Clean and validate. Handle missing values, remove duplicates, and correct obvious errors before training.
- Confirm the variables. Check that your independent variables are valid, measurable, and genuinely related to what you want to predict.
- Compare models fairly. When building a predictive or diagnostic model, compare the accuracy of models trained with different machine-learning algorithms, and select the best-performing one.
- Check against reality. When the predicted outcome actually occurs, compare what you predicted against what happened to confirm the model's accuracy — and keep improving it over time.
Getting support
The platform offers support across the whole journey — from problem identification and data access through to deployment and scaling. If you have questions about applying these guidelines to your project, contact us at info@actsml.com.