Analyzing the civil liability structure resulting from the automatic decisions of Autonomous Agents algorithms in the blockchain platform; Trying to determine the causal relationship between programming error and damage in artificial intelligence based systems

Introduction: Decision architecture and legal challenges in the black box of algorithms

With the integration of artificial intelligence (AI) and distributed ledger technology (DLT), a new generation of software systems called "Autonomous Agents" have been formed. These systems are able to analyze data without human intervention and make financial or executive decisions in the context of decentralized networks.

As a researcher and developer of data-driven systems with a combined background in computer engineering, artificial intelligence and law, I have always faced this fundamental question in the design of the backend architecture: "**If the machine learning algorithm (ML) or smart contract code suffers a calculation error and causes damage to users, who is responsible for compensating for this damage?" we do

1. Architecture of Autonomous Agents in Blockchain

Autonomous agents are programs that understand and react to their environment based on machine learning and big data analysis (BDA) models. The deployment of these agents in the blockchain platform adds immutability and automatic execution to them.

From the perspective of software development, these systems are a combination of predictive models (e.g. with Python) and deterministic executable codes (e.g. Solidity). Complexity begins when the system's output decisions result from the algorithm's dynamic interaction with new environmental data, not just pre-written code.

2. Blind spot of civil liability: verification of causality

In civil rights, in order to realize compensation, three basic elements must be proven: 1. Entry of loss 2. Harmful act (guilt) and 3. Causal relationship between the two.

In traditional systems, finding the culprit is relatively simple; But in complex artificial intelligence (Deep Learning) algorithms, which have a nature similar to a "black box", how can it be proven that the damage caused was directly caused by a "bug in coding" or caused by "false learning of the algorithm from environmental data"?

Here, rights require data analysis and reverse engineering tools. We must be able to separate the contribution of human (programmer) error from systematic error.

3. Mathematical modeling of causal relationship in algorithmic error

As a Decision Support Systems (DSS) strategist, I believe that probabilistic models and data analysis should be used to solve this legal challenge in the courts. Causation can be modeled based on Bayes' Theorem to calculate the probability that damage (DD) is directly caused by a programming bug (BB):

P(B∣D)\=P(D∣B)⋅P(B)P(D)P(B|D) = \\frac{P(D|B) \\cdot P(B)}{P(D)}in this formula:

• The component P(B∣D)P(B|D) indicates the probability (or percentage of fault) of the existence of a software bug under the condition of damage.
• The component P(D∣B)P(D|B) shows the probability of this level of damage in the presence of such a bug in the testing environment.

If based on log management documentation and AI Forensics tools, it is proven that the value of P(B∣D)P(B|D) is higher than a critical threshold (Threshold), the court can establish the causal relationship between the developer's error and the damage. Otherwise, the damage may be considered as a result of digital force majeure or unforeseen fluctuations in the input data.

4. Aligning technology with risk management strategy

For businesses that use AI on the blockchain platform, these legal uncertainties are considered a strategic risk. What is the operational solution?

Developers and IT managers should adopt a "Legal Compliance by Design" approach. This includes the implementation of explainable mechanisms of artificial intelligence (Explainable AI or XAI) and the transparent storage of decision logs in relational databases (such as PostgreSQL) alongside the blockchain, so that in the event of cross-border disputes or legal claims, it is possible to reproduce the decision path of the algorithm.

Strategic conclusion

The real value of innovation in AI and blockchain is realized when it is accompanied by a deep understanding of legal and business requirements. Determining civil liability for autonomous agents requires a transition from traditional concepts of blame and acceptance of models based on data analysis and mathematics. Algorithmic transparency is not only a legal requirement, but also a sustainable competitive advantage for digital platforms to gain the trust of users and stakeholders.