Introduction

As artificial intelligence becomes a big part of today‘s business solutions, the job of making sure AI is fair, clear, and responsible is becoming more important for Business Analysts (BAs).

Ethical AI is no longer something that’s just nice to have—it’s now a must-have for keeping users trusty, meeting laws, and building long–term business success.

This article shows you how BAs can include ethical AI ideas in their work during the process of gathering needs, talking with stakeholders, creating process models, and checking data.
You’ll also see real–life examples and methods to find any unfairness early on and build AI systems that are fair for everyone.

What is Ethical AI and Why Should BAs Care?

The BA’s Role in Ethical AI

Spotting Bias During Requirements Gathering

Techniques to Find Ethical Blind Spots

Guaranteeing Fairness in Data Collection and Validation

Real–Life Scenarios for Ethical AI in BA Work

Tools and Frameworks for BAs

Conclusion

1.What is Ethical AI and Why Should BAs Care?

Ethical AI means creating and using AI that is:
Fair
Clear
Inclusive
Responsible
Accountable
Protects privacy
Why BAs Should Care About Ethical AI

Business Analysts connect the business, users, and development teams.

They help shape how AI will work because:

They determine the rules the AI will follow
They help make requirements clear
They understand how users and stakeholders expect the AI to behave
They check if the data is properly used and within proper limits

If unfair or biased ideas aren’t caught during the early stages of setting up requirements, the AI might:

Treat some people unfairly
Make wrong predictions
Hurt certain groups
Put the company at risk of rules or legal trouble

2.The BA’s Role in Ethical AI

A BA has an important part to play in preventing unfair outcomes.
Main Responsibilities of a BA in Ethical AI
Check that the business needs don’t lead to biased results.

Talk to experts, data scientists, compliance teams, and users about ethical risks.

Look at the data to make sure it’s complete, balanced, and fair.

Identify where AI decisions might affect different groups.

Write down fairness, transparency, explainability, and the ability to track the AI in the requirements.

3.Spotting Bias During Requirements Gathering

Bias can appear at different steps.
A BA must be watchful for it during meetings, workshops, and process analysis.

Types of Bias to Watch for

Data Bias—when past data is not representative
Sampling Bias—when some groups are not included enough
Prediction Bias—when AI favors one group
Automation Bias—when people trust AI without checking
Confirmation Bias—when stakeholders use data to support their own ideas

Example

A bank wants to use AI for loan approvals.
If past data shows fewer loans approved for younger people, the AI might repeat that pattern.

What a BA Can Do:

Check if the age data is balanced and ask: “Are we not approving young people because they are riskier, or because the data is biased?”

4.Techniques to Find Ethical Blind Spots

To catch unfairness early, BAs should use specific methods.

Five Whys Technique

Helps you dig into the reasons behind a requirement.

Example:
Requirement: “AI should prioritize customers with long credit history.”

Why?
To reduce risk.

Why?
Because shorter history might mean higher risk.

Why?
Are we unfairly treating younger applicants?

Stakeholder Workshops

Hold a workshop with:

Legal experts

User experience designers

Data scientists

Advocates for customers

Ask questions like:

“Who might be at risk from this AI decision?”

“Can the user understand how the AI makes decisions?”

Data Walkthrough Sessions

Work with data engineers to:

Check where the data comes from

Find any unfair patterns

Remove any attributes that might lead to bias (like race, gender, location)

Scenario–Based Elicitation

This is very useful for AI projects.

Scenario Example:
An AI for facial recognition has trouble with darker skin tones.

Ask: “What happens if the AI can’t identify someone?”

Check if the process treats all users equally.

Suggest alternatives like manual check or better training data.

5.Ensuring Fairness in Data Collection and Validation

Without good, fair, and unbiased data, fairness in AI is impossible.

BA Responsibilities in Data Validation

Check if data represents all users fairly.

Example: Hiring AI trained only with resumes from men will favor men.

Identify any sensitive data and remove it or make it anonymous:

Gender
Race
Caste
Ethnicity
Age (if not relevant)
ZIP codes (sometimes linked to race)

Challenge data assumptions:

If data scientists say: “Fraud happens most in low–income areas,”

The BA should ask: “Is it because of real fraud, or because we monitor those areas more?”

Add requirements in functional needs:

Data must be updated regularly

Bias checks must be done before model approval

AI decisions must be explainable for end users

6.Real–Life Scenarios for Ethical AI in BA Work

Here are real examples of how BAs make sure their AI is fair.

Scenario 1: AI for Heart Attack Risk Prediction

Problem: Most data for training the AI came from men.

Unfair Outcome: Female symptoms aren’t well covered, leading to wrong diagnoses.

What a BA Can Do:

Make sure the data includes enough records from both men and women.
Add a requirement: “Test the AI separately for men and women to ensure fairness.”
Propose getting more data to make it balanced.

Scenario 2: AI to Filter Job Applications

Problem: Past hiring data favored graduates from certain schools.
Unfair Outcome: AI rejects smart people from smaller schools.

What a BA Can Do:

Question the rule “Top schools preferred.”

Add a requirement: “AI should score based on skills, not school names.”

Ask for removal of school names from data to reduce bias.

Scenario 3: AI for Loan Approval

Problem: AI denies loans based on ZIP codes.

Unfair Outcome: Indirect discrimination based on location (ZIP codes often link to race or income).

What a BA Can Do:

Identify ZIP code as a possible proxy for race or income.
Suggest using credit behavior instead of location.

Write a fairness requirement: “AI should not use location data unless approved.”

7.Tools and Frameworks for BAs

BAs can use different tools to make sure ethical issues are considered.

Fairness Indicators (Google): Checks how fair a model is for different groups.

AI Fairness 360 (IBM): Helps detect and fix bias.

Model Cards: A tool that BAs can help to create for documentation.

Risk Matrices: Include ethical risks along with usual business risks.

RACI Matrix: Helps assign responsibility for testing fairness.

8.Conclusion

Ethical AI is not just a technical job—it’s a strategic chance for BAs.
By finding bias early during requirements, checking if data is fair, and using ethical elicitation methods, BAs can create AI that is fair, responsible, and user–friendly.