Beyond the black box: Building transparency and accountability in AI

Artificial intelligence is at the core of a new technological revolution, reshaping industries from finance to healthcare. Yet, AI’s rapid adoption has been accompanied by an equally swift rise in concerns over its transparency, accountability, and trustworthiness. These concerns aren’t just theoretical; regulatory frameworks such as the EU AI Act and FCA regulations make it clear that AI-driven decisions must be explainable and auditable. But how do we achieve that in practice?

Bridging the gap between AI’s decision-making processes and human understanding is essential to its future success and adoption. Doing so requires rethinking how an AI system is built, how they communicate their reasoning, and how they align with both regulatory and human expectations. In this article, we’ll explore some of the challenges in creating transparent AI and highlight emerging solutions that are making it more accountable.

The barriers to AI transparency

One of the biggest barriers to accountability in AI is its inherently complex and often opaque nature. Many of the AI models attracting the most attention at the moment, such as large language models (LLMs), operate as black boxes. They generate predictions and decisions based on vast amounts of data but provide little insight into how they’ve reached them.

This is because current AI architectures aren’t designed for interpretability. LLMs, for example, rely on probabilistic reasoning and statistical correlations, making it difficult to trace their decision-making steps. This method has so far helped us reach our current level of progress with AI, which has been no small feat. But to make AI truly effective in business contexts, we’re going to need to find a different way.

The psychological challenge is equally significant. Human trust in decision-making relies on understanding causal mechanisms; people need to know why something happens, especially when large amounts of money or people’s health are at stake. If an AI system determines the outcome of a medical insurance claim, for example, that calculation could change the course of somebody’s life or put the insurer’s entire business at risk. Without clear explanations, AI remains an enigma, fostering skepticism and reluctance to adopt it in high-stakes environments like law, finance, or healthcare.

Innovative approaches to AI accountability

The key to solving this problem lies in AI architectures that prioritise explainability from the ground up. Neurosymbolic AI, a hybrid approach that combines deep learning with rule-based symbolic reasoning, offers one way of doing this. Unlike traditional LLMs, neurosymbolic AI structures its understanding in a deterministic and interpretable manner.  It does this by transforming data – such as documents – into symbolic representations, like logic graphs, which outline their meaning in a structured, machine-readable format.

For example, our neurosymbolic AI system ingests a document, analyses its structure and translates it into our proprietary symbolic language, Universal Language. This allows the AI to represent the document’s meaning explicitly, rather than making inferences based on statistical patterns alone. An AI agent then goes through and validates that logic using a set of ‘training scenarios’ designed to ensure accuracy. Once the system has built a valid understanding of the domain – be it insurance policies, loan applications or financial regulation – it can test millions of scenarios against it, offering precise, explainable decisions.

One of the significant advantages of this approach is efficiency. Whereas LLMs require millions or even billions of data points to train effectively, neurosymbolic AI can achieve high precision with only a few dozen training scenarios. This means it is taught, not trained. And, when it makes a decision, every step of its reasoning process is explicitly laid out, eliminating the risk of hallucinations or unexplained conclusions that plague the standard black-box AI models.

This ability to generate structured, traceable explanations is critical in regulated industries where decisions must be auditable. For example, if an AI system denies a loan application, it should be able to pinpoint the exact policy rules, financial thresholds or risk factors that influenced that outcome. Neurosymbolic AI ensures this by maintaining a logical and reproducible reasoning path, bridging the gap between technical complexity and human understanding.

Practical strategies for building trust in AI systems

Transparency alone is not enough. AI must be designed with trust in mind. Three strategies for achieving this include:

1. Human-centric explanations: AI outputs must be tailored to their audiences. Businesses  need detailed, structured audits of AI decisions to comply with regulations, while consumers require digestible explanations free from jargon. Creating multiple layers of explanation for both technical review and human comprehension enhances trust.
2. Robust audits and traceability: AI systems should produce clear, auditable trails of their decision-making processes. This is particularly important in regulated industries such as insurance and finance, where post-hoc justifications are insufficient. Neurosymbolic AI, by design, ensures that every decision follows a logical and reproducible path.
3. Bias control and ethical safeguards: AI models inherit biases from the data they are trained on. The best way to mitigate bias is to design systems that actively monitor and adjust for it in real-time. This means not only selecting diverse and representative training data but also implementing fairness constraints that prevent discriminatory outcomes.

Bridging the AI-human understanding gap

The AI industry must take responsibility for making its systems more interpretable. Many organisations have focussed on developing tools like Explainable AI techniques, but these often remain too complex for non-technical users. The challenge is to move beyond generic, after-the-fact explanations and towards AI systems that are inherently interpretable.

In highly regulated sectors like financial services, explicability is not just a nice-to-have; it’s a legal requirement. Banks and insurers must demonstrate that their AI-driven decisions comply with regulatory frameworks. Without robust explanations, businesses face not only legal repercussions but also reputational damage and loss of customer trust.

As AI continues to evolve, transparency must be treated as a foundational principle rather than an afterthought. Neurosymbolic AI represents a significant opportunity to facilitate this, allowing AI systems to become both powerful and accountable. By prioritising transparency today, we can ensure AI serves society in a responsible, fair, and effective manner tomorrow.