The book used Minority Report to explore what happens when algorithms predict human behavior and institutions act on those predictions before anything has happened. That scenario is no longer speculative. Algorithmic scoring systems now influence who gets hired, who gets a loan, who gets insurance, and who gets to post on social media — and in most cases, the people being scored have no idea how the system works or how to challenge it.
China's social credit system is the most visible example — a network of government and private scoring systems that rate citizens' trustworthiness based on their financial behavior, legal record, social connections, and online activity. Low scores can restrict access to flights, train tickets, good schools, and certain jobs. The system is neither as monolithic nor as dystopian as some Western media reports suggest, but it represents a genuine shift: the systematic use of data-driven scores to gate access to civic life.
What receives less attention is how pervasive algorithmic scoring already is outside China. In hiring, companies use AI tools like HireVue to analyze video interviews, scoring candidates on facial expressions, word choice, and vocal patterns. In lending, algorithmic credit scoring goes far beyond traditional credit histories, incorporating data from social media, browsing patterns, and purchasing behavior. In insurance, risk models use increasingly granular data to price policies — effectively penalizing people for behaviors they may not even know are being tracked. In content moderation, algorithms decide what speech is visible and what is suppressed, functioning as gatekeepers of public discourse.
The common thread is that consequential decisions about people's lives are being made by systems that are opaque, often biased, and difficult to contest. The book's discussion of Predictive Algorithms — the gap between correlation and causation, the problem of false positives, the question of who bears the cost when the algorithm is wrong — has become a description of daily life for millions of people.
The contestability problem is fundamental. In most traditional decision-making systems, a denied loan or rejected job application comes with some explanation and a path to appeal. Algorithmic decisions often come with neither. The model is proprietary. The features it uses may be unknown to the person being scored. The "explanation" may be a post-hoc rationalization that does not reflect how the model actually works.
The book's Informed Consent framework is severely strained. Being subject to algorithmic scoring is, for most people, not something they chose — it is a condition of participating in modern economic life. The Surveillance, Privacy, and Control dimension is equally clear: these scoring systems require vast amounts of personal data, creating surveillance infrastructure that persists whether or not the scoring is accurate or fair.
The question of whether algorithms can be more fair than human decision-makers is genuinely complex. Human hiring managers are biased too. Human loan officers discriminate. But the scale and opacity of algorithmic systems create a different kind of risk — one where bias is systematized, invisible, and difficult to correct. See Should an algorithm be allowed to decide whether I get a job, a loan, or parole?