Philosopher John Rawls once proposed a thought experiment that makes us ponder the nature of justice. Imagine you are creating rules for society without knowing who you will be in it — rich or poor, healthy or sick, part of the majority or a minority. Such a 'veil of ignorance' forces the creation of truly fair systems — after all, you could find yourself on either side.
In a world where artificial intelligence increasingly makes vital decisions, this concept becomes critically important. Strangely, a philosophical concept from half a century ago may hold the key to solving modern technological dilemmas.
When Machines Choose People
Take the hiring sphere as an example. Observing how more and more organizations delegate the initial selection of candidates to algorithms, one inevitably wonders: who are the judges? And by what rules do they judge? Behind algorithmic decisions, there are always people, their values, and notions of dignity.
The problem is that artificial intelligence learns from historical data that reflects biases, gaps in knowledge, and previous unjust practices. If you train #AI on decades of corporate hiring, it may 'learn' to favor resumes with elite university degrees or Western names — not because these qualities should be valued today, but because they were historically preferred.
The Dark Side of Algorithms
The situation is exacerbated by the opacity of AI tools: they function like black boxes, hiding decision-making processes. When 'the blind leads the blind,' falling into a pit is just a matter of time. The same is true for algorithms: their hidden biases are only revealed after harm has been done.
Business leaders must acknowledge a fundamental fact: without special design, artificial intelligence cannot adhere to the philosophical principle of the 'veil of ignorance.' Instead of creating fair solutions, it merely reflects and amplifies existing social biases and inequalities from the training data.
The good news is that companies can design AI systems that approach Rawls' principles of fairness. However, this requires conscious choices at every stage: data selection, algorithm development, auditing decisions, and human oversight.
Justice as a Competitive Advantage
Implementing Rawls' principles in AI development is not only an ethical imperative but also a powerful competitive advantage. Companies that create fair algorithmic systems open up new horizons of talent unavailable to those relying on traditional assessment methods.
Imagine the revolution that a truly impartial hiring algorithm could bring. Instead of the usual sieve that filters out non-standard resumes, such a system is capable of seeing potential where a human eye, burdened by stereotypes, might miss it.
A business that implements fair AI systems not only creates more diverse teams — a source of creativity and innovation — but also strengthens its reputation in a society increasingly sensitive to issues of discrimination. Moreover, as regulatory bodies intensify oversight of algorithmic fairness, such an approach also becomes a way to minimize legal risks.
Algorithmic Pollution
Data has become the new oil of the modern economy, and injustice is a new form of pollution: invisible but destructive to the social fabric of society. We see this in various spheres — from financial algorithms that reject loans to certain groups, to facial recognition systems that perform poorly with non-standard phenotypes.
The challenge posed by Rawls to the philosophers of the last century is now resonating anew in the technological sphere. Creators and implementers of AI face the same ethical dilemma: how to create systems that are fair for all, not just for privileged groups? Only now the cost of error is much higher — algorithms scale decisions to millions of people at the push of a button.
AI cannot naturally embody fairness. It needs to be taught this, just as a child is taught to distinguish right from wrong. And, like parents, we must remember that our lessons will shape the future.
For those who create and implement artificial intelligence systems, Rawls' principles can be transformed into practical recommendations:
Design AI systems as if you could find yourself subject to their decisions
Train algorithms on data cleansed of historical biases
Demand transparency when implementing AI in critical decision-making processes
Before our eyes, Bitcoin is rewriting the rules of finance, while artificial intelligence is reshaping the principles of social interaction. In this context, it is critically important to remember: technology must serve human values, not the other way around.
The golden rule of ethics states: treat others as you would like to be treated. In the age of algorithms, this principle takes on a new resonance: artificial intelligence should treat people as its creators would want it to treat them.