Responsible AI - Because Fairness Cannot Be Fully Automated
- Miriam Mukasa - Inclusive Leadership & AI
- Nov 3
- 3 min read
What Is AI?
AI systems (namely Gen-AI), use historical data to predict future outcomes. These systems learn by example, not by understanding
Large Language Models (LLMs) are designed for probability, not for inclusion
Data matters: AI learns patterns from data. If the data is flawed, the outcomes will be too
What Is Responsible AI?
Responsible AI means developing and deploying AI systems that reflect our values, safeguard human dignity, and promote equity. It’s not just a technical challenge; it’s also a leadership imperative.
Key Principles:
Value Alignment: AI must be designed to reflect ethical and societal values
Avoiding Harm: Systems should be tested and monitored to prevent unintended negative consequences
Equity by Design: AI should benefit everyone, avoiding the reinforcement of existing inequalities, whether in loan approvals, access to healthcare, job opportunities, accommodations for people with disabilities, access to education, to government support and more
Data and Design
Thoughtful Curation: Diverse and inclusive datasets are essential for equitable outcomes
Inclusive Design: Engage a broad range of stakeholders, particularly those likely to be impacted. This builds trust and helps uncover blind spots
Context Matters: A model trained in one region, sector or task, may not perform effectively elsewhere
Design for Inclusion: From algorithm selection to user interface layout, inclusion must be a guiding principle
Simulation Testing: Run test scenarios to assess how different groups are affected.
Prioritising PAC: Privacy, Agency, Cybersecurity
Privacy: Protect user data and ensure informed consent
Agency: Users should opt in, not be opted in. They must also have control over how their data is used
Cybersecurity: Build robust infrastructure that supports privacy and agency
System Integrity
Explainability: Users should be able to understand how decisions are made
Transparency: Communicate clearly about how AI systems function
Accountability: Assign clear responsibility for outcomes
Auditing and Monitoring: Continuously track performance to detect bias and model drift
Before You Begin your AI journey: Questions to Ask
What problem are we trying to solve and how is it framed? The way a problem is framed determines what the AI will optimise for
Is AI truly necessary, or could automation, training, or increased staffing suffice?
Do we need a complex model, or would a smaller model, fine-tuned for specific tasks suffice?
Have we consulted those who will be impacted?
Are we addressing root causes of the “problem” we’re trying to solve, or merely symptoms?
What does success look like? Consider fairness, equity, and long-term impact, not just efficiency, speed or prediction accuracy.
Final Thought
Responsible AI is not just about building smarter systems; it’s about building systems people can trust, understand, and challenge. Inclusion is not a feature; it’s a foundation.
If you’re considering how to embed Responsible AI in your organisation and want guidance on key priorities - from mitigating bias in recruitment and/or other AI tools, to improving transparency and accountability, feel free to reach out to me here on LinkedIn You can also visit ExecutiveGlobalCoaching.com to learn more about what we do.
If you’d like to learn more about embedding AI Literacy and Responsible AI in your organisation, then please free to contact me on LinkedIn or, visit ExecutiveGlobalCoaching.com to learn more about how we work
Subscribe below to receive an alert as soon as I publish new editions of my LinkedIn newsletters or, to read previous editions:
Responsible AI - (this one) Putting People and Culture at the heart of AI Strategy.
#ResponsibleAi #inclusiveai #humancentredai #ethicalai #futureofwork #changemanagement #aibias #leadershipdevelopment
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