BlogData & AI
Responsible AI: bias, privacy and accountability
By Micah Ondiwa, Senior Software Engineer 1 min read
AI systems can reproduce bias, expose personal data and blur responsibility. A few practices keep them fair and accountable.
AI can improve decisions — or automate existing unfairness at scale. Responsible use is a design choice.
Check for bias
Models learn from historical data, including its biases. Test results across groups — regions, genders, customer types — and investigate differences.
Minimise personal data
Use only the data the purpose needs, anonymise where possible, and comply with the Data Protection Act, 2019, including rules on automated decision-making.
Keep humans accountable
Decide who is responsible for each AI-supported decision. For significant decisions about people, a human should review the outcome.
Be transparent
Tell people when AI is used in decisions that affect them and how to challenge the result.
Monitor in production
Track accuracy and fairness over time, not just at launch.
Document
Record the purpose, data, testing and limitations of each model.
See our responsible AI and data services.
About the author
Micah Ondiwa
Senior Software Engineer
Micah Ondiwa writes about software engineering — application architecture, APIs and integration, data and AI-assisted development, and reliable delivery.