RIQZ, a London-based artificial intelligence innovator, has announced a pioneering breakthrough with the launch of its True Random AI Engine, aiming to fundamentally transform automated decision-making by eliminating algorithmic bias and increasing transparency and fairness. According to the company, traditional AI systems rely on pseudo-random algorithms—sequences that only simulate randomness but remain ultimately predictable. This predictability, RIQZ contends, introduces bias, repetition, and vulnerability into AI decision frameworks. The new system replaces these mechanisms with genuine, entropy-based randomness derived from naturally unpredictable sources, marking a significant departure from conventional deterministic AI models.

The core of RIQZ’s True Random AI Engine lies in its integration of quantum-inspired entropy sources combined with continuous verification algorithms to ensure each decision or data output is truly random, tamper-proof, and independently generated. This approach—described by RIQZ as "entropy intelligence"—embraces randomness not as noise but as a structural feature of intelligent behaviour, allowing AI systems to adapt, evolve, and make autonomous choices free from hidden manipulation or reverse engineering. The modular design of the engine also allows seamless integration into existing infrastructures via secure APIs, enabling organisations to upgrade without full system replacements.

RIQZ states the technology’s benefits span multiple sectors. In cybersecurity, the introduction of authentic random behaviour patterns disrupts adversaries attempting to predict system responses. Autonomous systems gain resilience and flexibility through non-repetitive, adaptive decision-making. In data science and simulations, unbiased random variables enhance the reliability of predictive models and datasets, while in scientific research, verifiable entropy ensures reproducibility and experimental integrity.

Complementing this product announcement, independent research provides insight into the underlying technologies enabling true randomness in AI. A recent scientific paper described the Quasi-Superposition Quantum-inspired System (QSQS), which generates true randomness by measuring unpredictable sorting times and deterministic permutation counts in computational processes, with the output entropy approaching theoretical maxima. This physics-based method exemplifies how real-time system nuances such as CPU jitter and cache latency can be leveraged to produce secure, unpredictable random bits crucial for post-quantum cryptographic security.

Beyond technical innovation, the launch of RIQZ’s True Random AI Engine connects with growing ethical and regulatory demands for AI fairness, accountability, and transparency. Studies in AI fairness underscore how biases in data, models, and procedures can entrench social inequalities, undermining public trust and further marginalising disadvantaged groups. By embedding scientifically validated randomness at a structural level, RIQZ proposes a new ethical foundation for automated decision-making—where unpredictability supports fairness by preventing systematic bias and manipulation, serving as a mechanism of trust in AI systems increasingly integrated into society.

Nonetheless, the broader AI governance landscape highlights ongoing challenges. Experts stress the need for multifaceted frameworks that combine robustness, privacy protection, and fairness to navigate vulnerabilities created by rapid technological advances. Ensuring secure, privacy-preserving, and fair AI requires comprehensive policy measures, such as access control policies and domain-specific fairness assessments, that can coexist with innovations like RIQZ’s entropy-based randomness engine. In this regard, RIQZ’s technology could represent an important piece of a wider strategy to improve AI integrity and societal trust.

In sum, RIQZ’s True Random AI Engine may signify a paradigm shift in how intelligence is defined and operationalised—transforming uncertainty from a problem into a foundational feature that enhances autonomy, transparency, and fairness. As automated systems become more pervasive, innovations that ensure unbiased and verifiable randomness could be essential for building the trustworthy AI ecosystems demanded by both industry and society.

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Source: Noah Wire Services