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Can Blockchain Solve AI Transparency Problems?
Artificial intelligence (AI) is revolutionizing industries by improving data processing and decision-making beyond human limits. However, as AI systems become more sophisticated, they are becoming increasingly opaque, raising concerns about transparency, trust, and fairness.
The “black box” nature of most AI systems often leaves stakeholders wondering about the origins and reliability of the outputs generated by AI. In response, technologies such as Explainable AI (XAI) have emerged trying to demystify AI operations, although often to be lacking to fully clarify its complexity.
As the complexities of AI continue to evolve, so does the need for robust mechanisms to ensure that these systems are not only effective, but also trustworthy and fair. Enter blockchain technology, known for its critical role in improving security and transparency through decentralized record-keeping.
Blockchain has the potential to not only secure financial transactions, but also imbue operations with artificial intelligence. with a level of verifiability which was previously difficult to achieve. It has the potential to address some of the most persistent challengessuch as data integrity and decision traceability, making it a critical component in the quest for transparent and trustworthy AI systems.
Chris Feng, COO of Chainbase, offered his insights on the topic in an interview with crypto.news. According to Feng, while blockchain integration may not directly solve every aspect of AI transparency, it does improve several critical areas.
Can blockchain technology really improve transparency in AI systems?
Blockchain technology does not solve the fundamental problem of explainability in AI models. It is crucial to distinguish between interpretability and transparency. The main reason for the lack of explainability in AI models lies in the black-box nature of deep neural networks. Although we understand the inference process, we do not grasp the logical meaning of each parameter involved.
How does blockchain technology improve transparency in ways other than the interpretability improvements offered by technologies like IBM’s Explainable AI (XAI)?
In the context of explainable AI (XAI), various methods, such as uncertainty statistics or analysis of model outputs and gradients, are used to understand the functionality of models. The integration of blockchain technology, however, does not alter the internal reasoning and training methods of AI models and therefore does not improve their interpretability. However, blockchain can improve the transparency of training data, procedures, and causal inference. For example, blockchain technology enables tracking of data used for model training and incorporates community input into decision-making processes. All of these data and procedures can be securely recorded on the blockchain, thus improving the transparency of both the AI model building and inference processes.
Given the widespread problem of bias in AI algorithms, how effective is blockchain in ensuring data provenance and integrity throughout the entire AI lifecycle?
Current blockchain methodologies have demonstrated significant potential in securely storing and delivering training data for AI models. The use of distributed nodes improves privacy and security. For example, Bittensor employs a distributed training approach that distributes data across multiple nodes and implements algorithms to prevent cheating between nodes, thereby increasing the resilience of distributed AI model training. Additionally, safeguarding user data during inference is critical. Ritual, for example, encrypts data before distributing it to off-chain nodes for inference computations.
Are there any limits to this approach?
A notable limitation is the supervision of model bias from training data. In particular, identifying bias in model predictions for gender or race from training data is often overlooked. Currently, neither blockchain technologies nor AI model debiasing methods effectively target and eliminate bias through explainability or debiasing techniques.
Do you think blockchain can improve the transparency of AI model validation and testing phases?
Companies like Bittensor, Ritual, and Santiment are using blockchain technology to connect on-chain smart contracts with off-chain processing capabilities. This integration enables on-chain inference, providing transparency between data, models, and processing power, thus improving overall transparency throughout the entire process.
What do you think are the most suitable consensus mechanisms for blockchain networks to validate AI decisions?
Personally, I advocate integrating Proof of Stake (PoS) and Proof of Authority (PoA) mechanisms. Unlike conventional distributed computing, AI training and inference processes require consistent and stable GPU resources for extended periods. Therefore, it is crucial to validate the effectiveness and reliability of these nodes. Currently, reliable computing resources are mostly hosted in data centers of different sizes, as consumer-grade GPUs may not sufficiently support AI services on the blockchain.
Looking ahead, what creative approaches or advances in blockchain technology do you think could prove critical to overcoming current AI transparency challenges, and how might they reshape the AI trust and accountability landscape?
I see several challenges in current blockchain-based AI applications, such as addressing the relationship between model debiasing and data and leveraging blockchain technology to detect and mitigate black-box attacks. I am actively exploring ways to incentivize the community to conduct experiments on model interpretability and improve the transparency of AI models. Additionally, I am thinking about how blockchain can facilitate the transformation of AI into a true public good. Public goods are defined by transparency, social benefit, and serving the public interest. However, current AI technologies often exist somewhere between experimental projects and commercial products. By using a blockchain network that incentivizes and distributes value, we could catalyze the democratization, accessibility, and decentralization of AI. This approach could potentially achieve executable transparency and promote greater trustworthiness in AI systems.