THE 360°AI BLOG

Predictive justice covers AI tools that forecast judicial outcomes and reoffending risk. In the US, the COMPAS algorithm was accused of racial bias and later upheld in court under strict limits. In France, the DataJust project was abandoned. The EU AI Act classifies these systems as "high-risk." While AI can streamline civil litigation, using it in criminal justice raises serious questions about transparency, discrimination, and accountability.

Algorithmic bias is one of the most serious risks embedded in modern AI, and one of the least visible. From Amazon's sexist hiring tool to COMPAS in the US criminal justice system, AI systems have been shown to replicate and amplify the inequalities of the societies that created them. This article explains where these biases come from, how they snowball over time, and what regulators, developers, and users can do to detect and correct them before they cause irreversible harm.

AI doesn't just make mistakes, it makes them with total confidence. This piece unpacks two structural flaws behind every chatbot answer: hallucinations, where models invent facts and state them as truth, and algorithmic bias, where systems replicate real-world inequality at scale. From sanctioned lawyers citing fake case law to a wrongful arrest triggered by facial recognition, we trace the real-world fallout, the fixes that exist, and why human oversight can't be optional.

AI promises progress but reproduces existing inequalities first. Pew Research finds 58% of adults under 30 have used ChatGPT versus just 10% of those 65+. Globally, 2.2 billion people remain offline. The UNDP documents widening North-South gaps for the fourth year running. Yet in low-HDI countries, 70% see AI as a productivity opportunity. Between inclusive promise and fractured reality: the full verdict, backed by data.

Behind ChatGPT, facial recognition, and autonomous vehicles lie artificial neural networks, inspired by the brain but grounded in advanced mathematics. They learn from data to recognise, predict, and generate. From their origins in 1943 to the deep learning revolution of 2012, this article unpacks how they work, their real-world applications, and their ethical and environmental limits.