---
title: "Responsible AI in ISE"
source: Microsoft Code-With Engineering Playbook
source_url: https://github.com/microsoft/code-with-engineering-playbook
licence: CC-BY-4.0
domain: engineering
subdomain: microsoft-code-with-playbook
date_added: 2026-04-25
---

# Responsible AI in ISE

## Microsoft's Responsible AI principles

Every ML project in ISE goes through a Responsible AI (RAI) assessment to ensure that it upholds Microsoft's [6 Responsible AI principles](https://www.microsoft.com/en-us/ai/responsible-ai):

- Fairness
- Reliability & Safety
- Privacy & Security
- Inclusiveness
- Transparency
- Accountability

Every project goes through the RAI process, whether we are building a new ML model from scratch, or putting an existing model in production.

## ISE's Responsible AI process

The process begins as soon as we start a prospective project. We start to complete a Responsible AI review document, and an impact assessment, which provides a structured way to explore topics such as:

- Can the problem be addressed with a non-technical (e.g. social) solution?
- Can the problem be solved without AI? Would simpler technology suffice?
- Will the team have access to domain experts (e.g. doctors, refugees) in the field where the AI is applicable?
- Who are the stakeholders in this project? Who does the AI impact? Are there any vulnerable groups affected?
- What are the possible benefits and harms to each stakeholder?
- How can the technology be misused, and what can go wrong?
- Has the team analyzed the input data properly to make sure that the training data is suitable for machine learning?
- Is the training data an accurate representation of data that will be used as input in production?
- Is there a good representation of all users?
- Is there a fall-back mechanism (a human in the loop, or a way to revert decisions based on the model)?
- Does data used by the model for training or scoring contain PII? What measures have been taken to remove sensitive data?
- Does the model impact consequential decisions, like blocking people from getting jobs, loans, health care etc. or in the cases where it may, have appropriate ethical considerations been discussed?
- Have measures for re-training been considered?
- How can we address any concerns that arise, and how can we mitigate risk?

At this point we research available [tools and resources](https://www.microsoft.com/en-us/ai/responsible-ai-resources), such as [InterpretML](https://interpret.ml/) or [Fairlearn](https://github.com/fairlearn/fairlearn), that we may use on the project. We may change the project scope or re-define the [ML problem definition](./envisioning-and-problem-formulation.md) if necessary.

The Responsible AI review documents remain living documents that we re-visit and update throughout project development, through the [feasibility study](./feasibility-studies.md), as the model is developed and prepared for production, and new information unfolds. The documents can be used and expanded once the model is deployed, and monitored in production.
