Bias in AI has come to the forefront. We see it in credit card algorithms accused of gender discrimination. We see it in healthcare algorithms investigated for racial bias in patient care. We see it in judicial systems where algorithmic recommendations perpetuate existing disparities.
While AI has had dramatic successes, the explainability challenge remains critical. Complex AI algorithms today are black boxes. Their inner workings are unknown and unexplainable. This is why controversies keep emerging. While gender or race might not be explicitly encoded in algorithms, subtle and deep biases can creep into data. It does not matter if input factors are not directly biased. Bias can be inferred by AI algorithms.
Companies have no proof to show that a model is not biased. On the other hand, substantial proof often exists for bias based on real-world outcomes. Complex AI algorithms are invariably black boxes. If AI solutions are not designed with foundational fixes, we will continue to see more such cases.
Prevention Over Cure
One of the main problems with AI today is that issues are detected after the fact, usually when people have already been impacted. This needs to change.
Explainability needs to be a fundamental part of any AI solution, from design through production. Not just part of post-mortem analysis. In order to prevent bias we need visibility into the inner workings of AI algorithms, as well as data, throughout the AI lifecycle. We need humans in the loop monitoring explainability results and overriding algorithm decisions where necessary.
The Question of Who
What do businesses need to trust predictions? First, explanations so we understand what is going on. Then we need to know that these explanations are accurate and trustworthy, and come from a reliable source.
Here is the fundamental tension: if the same organization builds AI algorithms and also explains them for customers without third-party involvement, it does not align with incentives for customers to completely trust the models. It is a catch-22 for any company in the business of building AI models.
This is why impartiality and independent third parties are crucial. They provide an independent opinion on algorithm-generated outcomes. Third-party AI governance and explainability services are not just nice to have. They are crucial for AI's evolution and use moving forward.
The Case for Independence
Consider the financial audit analogy. Companies do not audit their own financial statements for public reporting. Independent auditors provide assurance that statements are accurate and complete. This independence is essential for investor trust.
AI systems making consequential decisions deserve similar treatment. When a model denies credit, rejects an applicant, or determines healthcare prioritization, affected individuals deserve explanations from a source that does not have incentives to minimize problems.
Independence matters for several reasons:
Incentive alignment: An organization building AI has incentives to show it works well. An independent party has incentives to find the truth.
Fresh perspective: People who built a system may have blind spots about its limitations. Outsiders often see problems insiders miss.
Credibility: Stakeholders, whether regulators, customers, or the public, are more likely to trust findings from parties without conflicts of interest.
Accountability: When problems are found, independent parties can report them without internal political pressure to minimize or hide issues.
Ethics and Explainability
Ethics plays a significant role in explainability because ultimately the goal is to ensure companies build ethical and responsible AI. For explainability to succeed in its ultimate goal of ethical AI, we need an agnostic and independent approach.
Organizations can change their stances on explainability. Some have started ethics boards only to dissolve them. Some have gone on record questioning the value of explainable AI. Stances shift based on business pressures, customer feedback, and market conditions.
Whatever the motivation, it is good when organizations embrace explainability. But we should not rely solely on those who build AI to also be the ones who validate it.
Building Trust Architecture
For AI supervision to work, the architecture of trust matters. This includes:
Model documentation that captures how models work, what data they use, and what assumptions they make.
Independent testing that evaluates model behavior across demographic groups and edge cases.
Ongoing monitoring that detects when model behavior changes or problems emerge.
Transparent reporting that shares findings with stakeholders who need to know.
Each of these components can benefit from independence. Even if internal teams perform the work, having external validation strengthens confidence in the results.
The Path Forward
The conversation around explainable AI has accelerated. Major cloud providers now offer explainability tools. This demonstrates that companies need to move fast and adopt explainability as part of their machine learning workflows.
But the question remains: who should be explaining? The organizations that build trust most effectively will be those that recognize the value of independence. Not because it is required by regulation (though increasingly it may be), but because it is the right way to build AI systems that stakeholders can trust.
Separation between those who build and those who validate is not an obstacle to AI progress. It is a foundation for AI that earns and maintains the trust it needs to fulfill its potential.
