Ethical AI Frameworks
Every company says they want "Ethical AI." But how do you measure it? How do you enforce it? Here is how to move from principles to practice.
The Core Principles
Most global frameworks (OECD, UNESCO, NIST) converge on five core values:
- Fairness: Preventing discrimination and bias.
- Accountability: Ensuring humans are responsible for AI actions.
- Transparency: Understanding how the system works.
- Privacy: Respecting user data rights.
- Safety: Preventing harm to people or property.
Operationalizing Ethics
Principles are useless without processes.
Fairness Metrics
Don't just "hope" your model isn't racist. Measure it. Use metrics like Disparate Impact Ratio and Equalized Odds to quantify bias in your testing phase.
Stakeholder Impact Assessment
Before launching, ask: Who could be hurt by this? Conduct a formal assessment of potential negative impacts on vulnerable populations.
The "Human-in-the-Loop"
For high-stakes decisions (hiring, lending, healthcare), you must ensure a human has the final say. The AI should be a decision support tool, not a decision maker.
Ethical Risk Assessment
Take our 20-question assessment to see where your AI projects stand on the ethics scale.