The Importance of Transparency in AI Agent Operations

The poorly defined idea of transparency in AI remains a recognizable issue for the tech industry. It is a heavily politicized word that can mean all sorts of things. From openness in development to the information about the sourcing of training data, the term covers a wide range of processes that are often far removed from each other.

Nonetheless, talking about transparency is crucial for the long-term evolution of the AI space. The biggest issue for many end users and employers of AI technology is the lack of proper explanations and transparency on the end of developers of principal infrastructure and tooling. Due to the current popularity of agentic AI projects in DeFi investment, it is hugely important to understand how they work.

What is the function of an AI agent?

One of the best definitions of agentic AI is that it is an autonomous program that can gather and analyze data to create and complete tasks required to reach its predetermined goal. A barebones example would look thusly:

• A user wants to find the best DeFi investment to allocate capital.
• An AI agent receives its instructions and identifies tasks to solve.
• The agent may ask the user for additional inputs to make the goal more precise.
• The program will gather data like suitable strategies, market history, etc.
• It will analyze the available information to find the best solution.

While the sequence of actions is quite clear and straightforward, a machine can use a variety of techniques to arrive at its output. For instance, it could be using one of dozens of decision-making models or employing a combination of them. End users rarely can look under the hood. Nonetheless, with the right methods for assessing the level of transparency of AI agents, we could create a paradigm shift.

One of the explanations for the lack of aforementioned transparency is the poor communication on the part of people who are creating foundational artificial intelligence tools and infrastructure. They simply do not explain how these systems work well enough for the general public to understand. While end users and developers do not need to know literally everything, the very trust in AI agents depends on the level of understanding among them.

Stefan Larsson et al. from Lund University argue that we need to create a more nuanced terminology to address the complexity of the issue. Transparency is a term that covers way too many things including openness and explainability. Many experts think that the term is heavily politicized and requires clarification to provide the necessary level of precision to any conversation about artificial intelligence.

Explainable AI and transparency

The politicization of the term is not a coincidence. Many countries have stepped forward to ensure that they are tackling the issue of AI development transparency. There are several key legislations that are targeting AI technology:

• The US WH Executive Order that demands safety, security, and transparency in the development and use of artificial intelligence was released in October 2023. The order specifically protects consumers, patients, transportation users, and students. It also requires developers and owners of technology to be able to explain how it works.
• The Hiroshima AI Process Framework is a special document outlining how a typical policy regulating AI development should be created to work well on the international level. It was created at the G7 Summit in Japan. Among key principles, regular transparency reports and responsible distribution of information are notable highlights.
• In the Blueprint for AI Bill of Rights, one of the principles is called “Notice and Explanation”. It says that people involved in the creation of artificial intelligence must provide explanation of how it works using accessible language that can be read by a person with a barebones knowledge of technology.

IBM describes explainability, interpretability, and transparency in artificial intelligence quite well by defining each term in a single sentence:

1. Explainability explains how a model comes up with the output.
2. Interpretability explains how a model makes decisions that lead it to the output.
3. Transparency describes the model, the data it uses for training, and how decision-making works.

The latter is what we are concerned about in this article. Consumers and developers must have access to crucial information about an AI model. For instance, if it is used to find the best DeFi strategies and help investors make better capital allocation decisions, it is important to know what kind of data it uses and how it makes decisions.

One of the examples of successful projects that ensure transparency in AI in the DeFi investment sector is Rivo with its Maneki AI agent. The functionality of this agent can be summarized like this:

1. It guides users through the interface to smooth out the learning curve and make onboarding easier.
2. It can analyze protocols, and yield strategies, and define risks using predetermined algorithms and data from trusted sources.
3. The agent is capable of providing actionable insights and suggesting the best strategies.
4. It can monitor the market and the DeFi sector to notify users about updates and notable price action motions.
5. The agent can check out interfaces and identify fake ones to rule out fraudulent protocols.
6. The agent gathers data from social media and data aggregators to generate and publish content.

Rivo does not hide the inner workings of its artificial intelligence or where it sources data from. Another important factor is that humans are still in full control when it comes to DeFi investment. The feedback from users is actively used in the development process making it a great example of how communities influence innovations in the field of AI transparency to make the process of choosing the best DeFi strategies simpler.

The experience in developing such technology is more than just valuable. Companies that want to encourage their users to engage with it must focus on providing transparency. Disclosing basic information such as purpose, risk level, model policy, and training data as well as biases and evaluation metrics should be something that happens naturally. The ethical implications of AI transparency are hugely important when it comes to domains like finance or medicine.

Take DeFi investment as an example. If a system is biased toward certain protocols, users will be swayed to invest in a strategy that may appear less effective than others. Poor data sourcing can lead to issues with performance. When it comes to choosing the best DeFi strategies, models like Maneki AI still rely on humans’ ability to discern truth from false.

Future of transparency in AI systems

Precedence Research estimates the size of the AI market at roughly $757 billion in 2025 with a potential to reach $3.6 trillion by 2034 at a CAGR of 19.2%. Other estimates are quite close. The march of progress is unstoppable. The only thing we can realistically do about it is make the development process as transparent as possible.

The influence of technology on the development of transparency in AI is unclear. Some companies are doing everything they can to educate end users and other developers while others hide information either intentionally for competitive advantage or because they ignore contemporary guidelines.

Hopefully, this particular paradigm will shift soon. Modern models are already used in critical fields like self-driving cars, DeFi investment, stock trading, medicine, pharmaceuticals, law, and more. It is important for the current and future trends in transparency and AI to be oriented toward consumers and people whose lives will depend on the quality of thinking produced by artificial intelligence.