Metaverse and Crime
Convergence of AI, Blockchain and the Metaverse — Data, Privacy, Crime and Regulation
Much ink is being spilled in academia and media, on the potential in the convergence of recent technological advances in computing power, smart devices, Internet of Things (IoT), artificial intelligence (AI) i.e. machine learning (ML), blockchain, and now — the Metaverse. Mostly young technologies that have yet to reach maturity individually.
The possible convergence of these technologies is expected to bring about ‘exponential growth’ in superintelligence[1] aka artificial super intelligence (ASI)[2][3], and herald the 4th Industrial Revolution. Some of the literature on the topic is backed by serious academic research. Yang et al. (2022) “Fusing blockchain and AI with metaverse: A survey” — is in itself a catalogue of research papers on the topic.[4]
S.S. Gill, S. Tuli and M. Xu et al. (2019)[5] in their article on the transformative effects of IoT, blockchain and AI on cloud computing, cite how the ‘triumvirate’ of IoT, blockchain and AI might need ‘on demand, metered access’ to compute resources in geographically distributed applications (Image 1, below).
Enrico Zanardo (2023)[6] in his paper ‘Learningchain. A Novel Blockchain-Based Meritocratic Marketplace for Training Distributed Machine Learning Models’, combines blockchain, AI/ML and distributed computing power to present the Beez blockchain — a peer-to-peer distributed computing solution for AI models aimed at improving machine learning accuracy.
Numerous other studies are presenting increasingly viable solutions, use cases and scenarios combining up to three or four disruptive technologies. However, what remains missing in these studies very often, and what is of utmost importance is — experimentation, a practical application of these theories and an illustration of the multiplicative effect backed by empirical results, regardless of the scale of the dataset used.
Now talking of data — the backbone of the digital data economy. And, not just any data — Big Data, that is supposed to be the starting point and the raw material meant to fuel this industry. There is no shortage of it — we are generating tonnes of it by the minute, far more than can be processed by the human brain or by artificial narrow intelligence (ANI) –> where we are at currently with AI.
It is this ‘big data’ (generated by human activity) that is supposed to be collected in real-time by IoT and wearable smart devices with sensors that surround us, and fed directly to AI/ML models to make sense of it in real-time so that it can be instantly used (monetised), to augment our AR/VR experience in the metaverse — basically, to make us spend more time in these digital marketplaces.
These AI/ML models first need to be trained on mammoth size datasets through different ML training techniques such as supervised, unsupervised or federated learning; so that one day they can autonomously, and in real-time, predict our behaviour, our likes and dislikes, emotional states, etc. based on a live stream of data pouring in from IoT sensors on wearable devices.
Non-negligible research has been carried out and needs mentioning here concerning AI and bias — gender, race, ethnicity related and other covert biases that seep into AI inadvertently through ‘biased’ ML datasets (ref. Coded Bias, a documentary film with input from AI researcher Joy Buolamwini).[7][8]
This is where ethical concerns regarding data and privacy come in — constant collection, and instant processing of unfiltered data for unsupervised machine learning. Data collected on the go, processed and consumed on the spot (while it’s still hot!). This data stream could include (intentionally or unintentionally) data that is personal, private and sensitive in nature — protected in most advanced economies by now.
RQ 1: This raises a crucial research question — Where (at what stage), and how do we apply personal data and privacy protecting filters in this automated conveyer belt sequence of events –> constant data collection via IoT smart devices, instant processing with AI/ML and spontaneous use in Metaverse AR/VR experience?
The risk of not integrating privacy filters by design is running into trouble with legal and regulatory bodies at a later stage. Personal data protection regulations are already in place in Europe and parts of the USA; and gaining popularity around the world (Brazil, China, India, etc.).
Children’s privacy online is the new hot topic that is fast gaining momentum with potential to disrupt the huge gaming industry (that is fast moving towards the metaverse).
The next big concern is regarding crime and governance in a decentralized metaverse[9]. This problem is best illustrated by the following statement:
It is one thing when a 14 year old reports a stolen game-boy, and another when he reports a stolen multi-million dollar gaming metaverse.
The metaverse, as we well know, is built on the internet, and:
“The Internet was created without an identity layer“ — Kim Cameron, Chief Architect of Identity for Microsoft.
A bustling digital economy in a decentralized governance model, with a missing accountability (responsibility, traceability) layer raises a number of problems for crime prevention (financial crimes, money laundering, identity theft, etc.; see Image 2, above) in the metaverse — which is increasingly perceived by many as a ‘digital twin’, a reflection of our earthly existence; and a natural, logical extension of our online/internet presence (into the metaverse).
RQ 2: Now for the next crucial research question: How do we regulate and prevent crime in a metaverse — a borderless technology built on the internet without an identity or accountability layer, that does not belong to any one jurisdiction, and proclaims to be decentralized by nature?
Whatever needs to be done to answer/solve these two questions, needs to be done fast, as these technologies are developing at lightening speed, and so is their adoption by the masses. Governments (and their regulations) are notorious for being slow at the expense of being negligent — they missed the train at the time of the rise of the Internet, and now it will be a mammoth task to catch up with the Metaverse.
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References:
- HOW LONG BEFORE SUPERINTELLIGENCE?, Originally published in Int. Jour. of Future Studies, 1998, vol. 2: https://nickbostrom.com/superintelligence
- The AI Revolution: The Road to Superintelligence, 2015: https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html
- Is Current Progress in Artificial Intelligence Exponential?, 2020: https://medium.com/@reevesastronomy/is-current-progress-in-artificial-
intelligence-exponential-8e18f126d2cb - Yang, Qinglin, Yetong Zhao, Huawei Huang, Zehui Xiong, Jiawen Kang, and Zibin Zheng. “Fusing blockchain and AI with metaverse: A survey.” IEEE Open Journal of the Computer Society 3 (2022): 122–136, describe the role of blockchains and AI in the context of metaverse(s). Link: https://ieeexplore.ieee.org/abstract/document/9815155
- S.S. Gill, S. Tuli and M. Xu et al., 2019, Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges: https://www.sciencedirect.com/science/article/abs/pii/S2542660519302331
- E. Zanardo, 2023, Learningchain. A Novel Blockchain-Based Meritocratic Marketplace for Training Distributed Machine Learning Models: https://link.springer.com/chapter/10.1007/978-3-031-21435-6_14
- Bias in Artificial Intelligence, 2021: https://www.harvardmagazine.com/2021/08/meredith-broussard-ai-bias-documentary
- Artificial Intelligence and Ethics, 2019: https://www.harvardmagazine.com/2019/01/artificial-intelligence-limitations
- Wu et al.: Financial Crimes in Web3-empowered Metaverse, 2023: https://ieeexplore.ieee.org/document/10045768
Note: This article was originally written as an assignment for University of Nicosia, MSc in Blockchain and Digital Currency’s compulsory course on Emerging Topics in Blockchain and Digital Currency, Spring 2023.
