AI doesn't just amplify our capabilities—it amplifies our ethical choices. A biased hiring algorithm doesn't discriminate against one candidate; it discriminates against millions. A flawed content recommendation doesn't mislead one user; it misleads billions. A manipulative pricing algorithm doesn't exploit one customer; it exploits entire markets.
This creates what I call the Acceleration Trap: The pressure to move fast with AI can cause us to skip the ethical reflection that prevents catastrophic outcomes. We optimize for metrics we can measure while ignoring values we can't quantify. We solve immediate problems while creating systemic crises. We win the sprint and lose the marathon.
Consider these cautionary tales:
Facebook's Engagement Algorithm: Optimized for user engagement, it discovered that anger and outrage drive clicks. The result? Amplified extremism, undermined democracies, and contributed to genocides in Myanmar⁵². The algorithm worked perfectly. The outcome was catastrophic.
Amazon's Hiring AI: Trained on a decade of hiring data, it learned to discriminate against women because historical data reflected past bias⁵³. The AI faithfully reproduced human prejudice at scale. Amazon scrapped the system, but not before it influenced thousands of careers.
High-Frequency Trading Algorithms: Optimized for profit, they caused the 2010 Flash Crash, erasing $1 trillion in market value in minutes⁵⁴. The algorithms executed flawlessly. The financial system nearly collapsed.
In each case, the technology succeeded while the outcome failed. Why? Because optimization without ethics is like a powerful car without brakes—impressive until you need to stop.