Blockchain-based AI governance is a novel approach to ensure the ethical, transparent, and accountable use of artificial intelligence (AI) systems. By leveraging the distributed ledger technology of blockchain, AI governance can record, verify, and audit the data, decisions, and actions of AI models in a decentralized and (nearly) immutable way.
This can help build trust and confidence among the stakeholders of AI applications, such as developers, users, regulators, and society at large.
This article explores what blockchain-based AI governance means, why it is important, and how it can be used to form the backbone of transparent and auditable mechanisms for AI systems.
It also dives into three of the most promising use cases for distributed ledger technologies in building AI systems: decision-making, accountability, and ethical consideration.
Why Blockchain-Based Governance In AI Systems Is Necessary
Artificial intelligence is a powerhouse that continues to transform every aspect of our lives, including organizational approach, corporate, social, and governance (ESG) initiatives. On the other hand, AI governance is the formulation of policies and guidelines to ethically manage artificial intelligence.
Unregulated, it may lead to privacy infringement, bias propagation, or unchecked decision-making with far-reaching consequences. The launch of ChatGPT by OpenAI allowed both enthusiasts and critics of generative artificial intelligence to interact with the technology.
The majority of the world’s population (those who have interacted with AI) believe that technology can transform the global economy by enhancing efficiency, reducing costs, and fostering a better decision-making process. However, some experts, like the former Google scientist Geoffrey Hinton (nicknamed the “Godfather of AI”), are much more focused on the potential consequences of the technology.
Hinton, along with a notable group of 350 individuals – including OpenAI’s CEO Sam Altman and Elon Musk – penned an open letter alerting the world to AI’s possible hazards. They collectively expressed the urgency to momentarily slow down AI evolution, allowing ample time to draft necessary policies for overseeing this rapidly expanding technology.
“Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.”
Amid this public skepticism towards artificial intelligence, the utilization of blockchain technology for AI governance could make the technology safer and more ethical and significantly aid firms in fostering public confidence in their conscientious use of AI.
Using Blockchain in AI Governance
As the world transitions to accommodate generative AI, organizations, and governments must ensure that they are addressing the various concerns that have been raised, including ethical and social apprehensions.
It is essential to build AI systems that are fair and accountable and promote transparency. Difficult questions such as how to protect enterprise intellectual property (IP), guarantee privacy and security of data used by AI models as well as how to prevent the misuse or abuse of AI by malicious actors must be addressed.
Blockchain technology, on the other hand, is a distributed ledger that allows for secure, transparent, and (relatively) tamper-proof applications. Blockchain technology can provide a solution to some of the challenges faced by AI, such as data provenance, model explainability, compliance verification, and dispute resolution.
By integrating AI and blockchain technology, more trustworthy and efficient frameworks for AI governance can be built to benefit various industries and society at large. Essentially, the application of blockchain technology to permanently document every decision made regarding an AI or machine learning (ML) model signifies a significant leap toward transparency – a key antecedent to trust.
Such use of blockchain deployment also enables audibility, bolstering the establishment of trust even further. These principles are central to an AI governance model that revolves around a corporate AI and model development standard, all underpinned by blockchain technology.
Building an AI System With Blockchain Technology
The process of building an AI decision model is delicate and complex, comprising numerous incremental decisions. For example, developers must take into account all the variables under the model’s scope, algorithms, the design of the model, training and test data used, the assortment of features, not forgetting the model’s raw latent features, and testing with ethics and stability in mind.
These incremental decisions often do not stray so much from the scientists who dedicated their time to building the various parts of the variable sets, as well as those who took part in the model creation process and conducted model testing.
By leveraging the power of blockchain technology, the cumulative record of these decisions affords the clarity necessary for the efficient internal governance of models in line with corporate-prescribed standards, assigns responsibility, and meets looming regulatory demands.
Some ways to utilize the nearly immutable nature of blockchain records in building AI systems include:
- Model objectives;
- How the model is built, encompassing machine learning algorithm;
- The degrees of freedom scientists have when solving problems as well as those they don’t;
- Reapplication of trusted, audited, and verified variables;
- Specifications for training and testing data;
- Procedures and standards for ethical AI;
- Evaluations for robustness and stability;
- Checklists for specific model testing and validation.
Auditable Blockchain-Based Governance Mechanisms For AI Systems
AI systems are often seen as mysterious ‘black boxes’ that can’t be examined to determine whether they are working correctly and ethically. Blockchain technology could help solve this issue by integrating it into the foundation of AI systems to ensure transparency.
A robust infrastructure for AI management necessitates the incorporation of clear and auditable governance procedures and frameworks. Assigning accountability and responsibility for AI involves demanding transparency of organization-wide governance, encompassing clear goals and targets for the AI framework.
These include well-outlined roles, duties, and chains of command; a diversified team skilled in managing AI infrastructures; a wide array of stakeholders; and processes to mitigate risks. Moreover, it is critical to seek out auditable governance elements at the system level, including documented tech specifications for the specific AI system, adherence to regulations, and stakeholder access to system design and operation data.
While this may sound complex, it simply means that blockchain technology can be incorporated into the process of building decision-making AI systems to make them more auditable and, thus, more accountable. All the decisions made regarding the AI model would be recorded on the blockchain and open for anyone to view and confirm.
Building Ethical AI Systems With Blockchain Technology
It would be impossible to tell whether an AI model without blockchain integrations in its governance systems or similar open-source transparency mechanisms is acting ethically. These integrations would not only improve the tech itself but would also build trust in the community and the general public.
Building blockchain-based AI governance starts by ensuring that transparent model governance is paramount to the development of ethical AI systems that can be audited by any onlookers worried about the potential of malicious models.
With the help of blockchain technology, the complete record of these decisions becomes fully visible, allowing for the effective internal governance of models, clear attribution of responsibility, and compliance with the inevitable regulatory scrutiny directed toward AI systems.
The development of blockchain-supported models could be a systematized process that culminates in the generation of comprehensive documentation and mechanisms to easily check any part of the system, thereby affirming that all components have undergone rigorous scrutiny for appropriate ethical considerations and decision-making.
Such components can be retrospectively examined at any juncture, offering crucial resources for model governance. Consequently, analytical model development and decision-making processes become subject to audits.
This could become a key element in ensuring accountability for AI technology, as well as the data scientists who architect it. It has the potential to be a crucial stride towards eliminating biases, ensuring high ethical standards, and simply improving the decision-making of vital AI systems.
