Treating ethical considerations as a post-launch fix creates massive "technical debt" that is nearly impossible to resolve. Just as an AI trained to detect melanoma on one skin color fails on others, solutions built on biased data are fundamentally flawed. Ethics must be baked into the initial design and data gathering process.

The legal system, despite its structure, is fundamentally non-deterministic and influenced by human factors. Applying new, equally non-deterministic AI systems to this already unpredictable human process poses a deep philosophical challenge to the notion of law as a computable, deterministic process.

Sociological research shows the era a person is born into—the "birth lottery of history"—is a more significant predictor of criminality than individual factors like psychology or poverty. Just a few years' difference can double the arrest rate for people from otherwise identical backgrounds.

While AI can inherit biases from training data, those datasets can be audited, benchmarked, and corrected. In contrast, uncovering and remedying the complex cognitive biases of a human judge is far more difficult and less systematic, making algorithmic fairness a potentially more solvable problem.

The promise of "techno-solutionism" falls flat when AI is applied to complex social issues. An AI project in Argentina meant to predict teen pregnancy simply confirmed that poverty was the root cause—a conclusion that didn't require invasive data collection and that technology alone could not fix, exposing the limits of algorithmic intervention.

Most crimes are committed by people under 35, and recidivism rates for those over 50 are near zero. Despite this, the fastest-growing demographic in U.S. prisons is people over 55. This highlights a costly misalignment between sentencing policies and the reality of criminal behavior over a lifespan.

Citing high rates of appellate court reversals and a 3-5% error rate in criminal convictions revealed by DNA, former Chief Justice McCormack argues the human-led justice system is not as reliable as perceived. This fallibility creates a clear opening for AI to improve accuracy and consistency.

Leading longevity research relies on datasets like the UK Biobank, which predominantly features wealthy, Western individuals. This creates a critical validation gap, meaning AI-driven biomarkers may be inaccurate or ineffective for entire populations, such as South Asians, hindering equitable healthcare advances.

AI systems often collapse because they are built on the flawed assumption that humans are logical and society is static. Real-world failures, from Soviet economic planning to modern systems, stem from an inability to model human behavior, data manipulation, and unexpected events.