When discussing AI transparency, one of the most common misconceptions is that it simply involves revealing the algorithm’s source code. While open-source AI systems certainly have their merits, true transparency is about much more than just showing lines of code. In reality, the goal of transparency is to ensure that AI systems are fair, unbiased, and accountable—outcomes that can be achieved without needing to expose proprietary code.

Why Exposing Code Isn’t Always Enough

At first glance, it seems logical that the most transparent approach to AI would be to make the algorithm’s code public. However, this method has several critical flaws. For one, most people (including many decision-makers and regulators) do not have the technical knowledge to understand or evaluate complex AI code. Furthermore, AI models—especially those built using machine learning—rely heavily on training data. Even if the code is made public, it does not reveal how the AI is learning from the data, which is often where biases and unfair practices can be introduced.

A deeper issue is that machine learning algorithms evolve based on the data they process, making the final decision-making logic opaque, even to their developers. As the article “Accountable Algorithms” argues, disclosing code does not guarantee fairness or accountability, and can be insufficient for analyzing how a decision is made. In some cases, transparency through code could also compromise privacy or allow bad actors to exploit the system.

Real AI Transparency: Fairness and Accountability

True AI transparency means providing clear and understandable explanations for the outcomes AI systems produce. This can be achieved through explainable AI (XAI), which ensures that AI models are designed to explain their reasoning and outputs in ways that humans can understand. Rather than focusing solely on source code, transparency in AI should revolve around:

• Demonstrating Unbiased Decisions: AI systems must be tested and validated to ensure they are free from bias, especially when dealing with sensitive areas like loan approvals, hiring, or criminal justice. Alternative techniques, such as dynamic testing, can be used to evaluate how a model performs on different sets of input data to ensure that it treats all groups fairly.
• Procedural Regularity: Ensuring that AI systems follow the same set of rules consistently, without deviation. This can be achieved through software verification and other computational techniques to prove that decisions are made based on declared principles and standards.
• Explaining Decisions to Users: AI systems must provide users with explanations for decisions. These explanations need to be clear and accessible, explaining why a certain outcome was reached and whether it aligns with legal and ethical standards.

Alternative Methods for AI Transparency

There are several alternative approaches to transparency that go beyond code disclosure:

1. Fairness Audits: These involve evaluating AI systems against fairness metrics, ensuring they don’t produce biased outcomes. For instance, machine learning systems can be audited to ensure their decisions are blind to factors like race or gender, aligning them with social and legal standards.
2. Dynamic Testing: Instead of relying on static analysis of code, dynamic testing evaluates how AI systems behave in real-world scenarios. By feeding AI systems different data sets and observing their outputs, organizations can ensure that decisions remain fair and consistent under varying conditions.
3. Cryptographic Proofs: Tools like zero-knowledge proofs allow developers to demonstrate that AI systems are following predefined rules without exposing the code or input data. This ensures that AI systems are operating ethically, without requiring full disclosure of proprietary technology.

The Future of AI Transparency and Governance

As AI becomes more integrated into decision-making processes, regulators and policymakers are beginning to focus on transparency frameworks. These frameworks aim to ensure that AI systems are fair, accountable, and compliant with legal standards without requiring the full exposure of proprietary systems.

Governance structures are likely to require businesses to demonstrate the fairness of their AI models using alternative methods like fairness audits and software verification. These methods provide strong evidence that AI systems operate within ethical boundaries and comply with societal values, even when the system’s code remains confidential.

At Marketways Arabia, we are pioneering the use of explainable AI models that not only drive business outcomes but also ensure fairness and transparency. We help organizations align their AI deployments with emerging regulatory standards by utilizing cutting-edge methods that ensure accountability without needing to disclose proprietary systems.