TEAconf is where researchers and business leaders collaborate to advance and promote Triple Entry Accounting — a new recordkeeping framework designed for the digital age. TEA enhances trust, promotes integrity, and elevates the standard for verifiable transactions.
Join us in sunny Malta November 18-22, 2026! Ticket sales will be announced soon.
Our upcoming Intro to TEA course gives you an insight into the technology and its applications, showcasing its potential and bringing you up to speed with this innovative framework.
Click here to watch our podcast, featuring academics and business leaders discussing the advantages and implementations of Triple Entry Accounting.
TEAconf community member Kostas Sgantzos, alongside co-author Massimiliano Ferrara of the University Mediterranea of Reggio Calabria, has published a new peer-reviewed paper in WSEAS Transactions on Business and Economics exploring a pressing problem at the intersection of AI and data integrity: what happens when the data used to train AI systems can't be trusted?
The paper, "Mitigating Big Data Pollution and AI Model Deterioration: A Dataset Core Approach with Blockchain-Based Verification", addresses the growing risk of AI model collapse — the phenomenon where models trained on increasingly AI-generated data progressively lose diversity and predictive reliability. As synthetic content floods the internet, successor models inherit its statistical biases, compressing the richness of the information ecosystem they depend on.
The authors' proposed solution combines two innovations. The first is a Dataset Core methodology — a mathematically grounded approach that preserves the essential information content of a training dataset while filtering out polluted or adversarially injected samples. Experimental results show that a core constructed at just 20% of the original dataset size retains around 95% of its information value, while reducing the effectiveness of adversarial poisoning attacks by 73%.
The second is a blockchain-based verification layer built on Triple Entry Accounting principles. Every training iteration is recorded as a three-part transaction: the input data hash, the model output, and an immutable blockchain record linking the two. This creates a complete, tamper-evident audit trail for AI training — something conventional machine learning pipelines entirely lack.
The paper also introduces an economic incentive mechanism that rewards high-quality data contributions and financially disincentivises data poisoning, along with a thought-provoking proposal to use proof-of-work token systems to counter what the authors term "cognitive imperialism" — the risk that AI convenience erodes genuine human reasoning and marginalises non-dominant knowledge systems.
The authors acknowledge Ian Grigg and George Papageorgiou for their review of early drafts.
We’re excited to share some updates with you!
1. TEAconf 3 announced
The third edition of TEAconf will take place on November 18-22, 2026, in sunny Malta at the Salini Hotel. The Call for Papers is now open — we look forward to your submissions.
2. Tickets
Ticket packages will be available soon. Subscribe to our newsletter to be the first to know when sales open.
3. Newsletter & News
All News posts are now included in our newsletter — making it easier to stay up to date.
We are developing a free Introduction to TEA course, designed to give you a solid grounding in the theory and practice of Triple Entry Accounting. Scheduled for release in Q2 2026, the course will be open to everyone. In the meantime, get a head start by watching episodes of TEAtalks.
We’ve added a new Opportunities page outlining ways for students, businesses, auditors, and regulators to get involved with TEAconf.
We’re excited about what lies ahead for TEAconf and the broader Triple Entry Accounting community. Stay up to date by subscribing to our newsletter, following TEAtalks, and exploring the new site. We look forward to seeing you — online, and in Malta in 2026.