Feature
Despite AI’s growing role in everything from hiring decisions to medical diagnoses, one question continues to haunt researchers and regulators: How does AI make its decisions?
In many cases, even the engineers who build these models struggle to explain their reasoning. This phenomenon, known as the AI black box, raises serious concerns — especially when AI systems determine creditworthiness, approve job candidates or assist in legal sentencing. If we can’t fully understand AI’s reasoning, how can we trust it?
Table of Contents
- What Is the AI Black Box?
- How AI Makes Decisions: A Look Inside the Black Box
- The Risks of Opaque AI Decisions
- Efforts to Make AI Explainable
- Balancing AI Performance With Interpretability
- Beyond the Black Box: Building AI We Can Trust
What Is the AI Black Box?
The term "black box AI" refers to AI models — particularly deep learning systems — whose decision-making processes are difficult or impossible to fully interpret, even by their creators. Unlike traditional software, where programmers write explicit rules that govern how outputs are generated, AI systems, especially those using neural networks, learn patterns and make predictions based on vast amounts of data. This means that while AI can produce highly accurate results, it often does so without providing a clear, understandable rationale for how it arrived at its conclusions.
This lack of transparency presents a fundamental trade-off between accuracy and explainability. More complex models, such as deep neural networks, are capable of outperforming simpler, rule-based systems in areas such as image recognition, natural language processing (NLP) and predictive analytics. However, their complexity makes it difficult to trace the exact reasoning behind their decisions, leading to concerns in fields where accountability is crucial.
In traditional software, every function is explicitly coded and can be inspected line by line, but conversely, AI models "learn" behaviors from training data rather than follow predefined logic. This makes them adaptive and powerful but also raises critical questions:
- Can we trust AI decisions if we don’t fully understand them?
- How do we ensure fairness, accountability and ethical responsibility in AI-driven systems?
These challenges make the black-box nature of AI one of the most pressing issues in AI adoption.
Related Article: How AI Is Reshaping Corporate Decision-Making — and What You Need to Know
How AI Makes Decisions: A Look Inside the Black Box
AI makes decisions through intricate mathematical models that analyze extensive datasets to detect patterns, forecast outcomes and generate responses. Unlike conventional software, which relies on predefined logic, AI — especially deep learning models, neural networks and large language models (LLMs) — continuously refines its decision-making by learning from data rather than adhering to rigid programming rules.
Neural networks mimic the way the human brain processes information, using interconnected layers of artificial neurons. These networks take input data, pass it through multiple layers where calculations are performed and, ultimately, produce an output. Each layer refines the information by adjusting "weights," which are numerical values that determine the importance of different factors. These weights are modified during training, as the AI system analyzes patterns in data and optimizes its responses.
Deep learning models, such as those used in image recognition and natural language processing, rely on hidden layers — intermediate processing stages between input and output — that extract increasingly complex features from data. However, the deeper these networks become, the harder it is to trace exactly how a decision was made. For instance, an AI model may correctly identify a medical condition from an X-ray image, but even the developers might struggle to pinpoint which exact features in the image led to that conclusion.
Dattaraj Rao, chief data scientist at Persistent Systems, noted that the increasing complexity of AI models makes explainability a major challenge.
"Today’s AI systems comprise highly performing machine learning models, including deep learning and large language models," he said. "Given that these models are highly complex (with many layers), they can make sense of complex correlations in data, but the same reason makes them very difficult to explain." To address this issue, Rao emphasized the importance of data lineage — ensuring AI decisions can be traced back to the data they were trained on. “Maintaining some lineage to the original data helps us understand how a change in input affects the decision.”
This unpredictability is particularly concerning in high-stakes applications where accuracy and accountability are critical. Anmol Agarwal, AI security researcher at Alora Tech LLC, pointed out that large AI models operate stochastically, meaning that the same input may not always produce the same output.
"A black box AI model is more prone to mistakes because it is difficult to see what is happening inside,” explained Agarwal. “Many attacks, such as data poisoning and prompt injection, can compromise the model and lead to incorrect decisions." This stochastic nature makes it harder to verify AI outputs, especially in scenarios where human lives or significant financial decisions are at stake.
The lack of transparency becomes even more evident in large language models, which generate human-like text based on vast amounts of training data. These models produce responses that often seem logical and well-formed, yet tracing a specific statement back to its exact training source is nearly impossible. AI may provide an answer that appears correct but has no clear explanation for how it arrived at that response — leading to what is often referred to as the "black box" problem. Despite ongoing efforts to improve AI transparency, balancing complexity with explainability remains a persistent challenge.
The Risks of Opaque AI Decisions
AI’s ability to make complex decisions at scale brings undeniable benefits, but when its decision-making process remains undefinable, significant risks emerge. One of the most pressing concerns is bias and discrimination. AI models learn from historical data, and if that data contains biases — whether related to race, gender or socioeconomic status — AI can unknowingly perpetuate or even amplify these biases.
This has led to well-documented issues in hiring algorithms that favor certain demographics, lending systems that unfairly deny loans to minority applicants, and predictive policing models that disproportionately target specific communities. In healthcare, biased AI models have contributed to disparities in treatment recommendations, reinforcing systemic inequalities rather than eliminating them.
Some prominent cases include:
- Amazon having to scrap its AI recruiting tool (that applicants were unaware of) due to its bias against women
- Apple's algorithm determining credit limits for the Apple Card being investigated by the NY Department of Financial Services due to its gender bias
- Northpointe's COMPAS algorithm used to assess recidivism in the US court systems, which was found to be biased against black defendants
- An algorithm from Optum designed to prioritize patients for extra care, which systematically favored white patients over black patients
Another major challenge is the question of accountability. When an AI system makes a flawed or harmful decision, who is responsible? Unlike traditional software with clear lines of code dictating behavior, AI models generate outcomes based on probabilistic reasoning (a method of decision-making that relies on probability theory to handle uncertainty), often without human oversight. This has already led to real-world consequences — such as automated fraud detection systems mistakenly flagging legitimate transactions, self-driving cars making fatal errors and AI-powered recruitment tools unfairly rejecting qualified candidates. Without clear accountability frameworks in place, businesses and regulatory bodies struggle to determine who should be held liable when AI-driven decisions go wrong.
When AI makes high-impact decisions, the consequences of a lack of transparency can be severe. Chris Brown, president at Intelygenz, told VKTR that "AI is an amplifier of decision-making, not a replacement for human judgment. In high-stakes scenarios — like hiring, lending, fraud detection or legal sentencing — trusting AI without understanding how it reaches its conclusions is irresponsible."
Misinformation and manipulation pose yet another serious risk. AI-generated content, from deepfake videos to AI-written propaganda, has made it increasingly difficult to distinguish between real and fabricated information. Social media platforms have become battlegrounds where AI-driven algorithms amplify misleading content, influencing public opinion and even election outcomes. As AI becomes more sophisticated, the challenge of detecting and alleviating misinformation grows, raising concerns about how society can safeguard truth and integrity.
Related Article: AI Risks Grow as Companies Prioritize Speed Over Safety
Efforts to Make AI Explainable
As AI becomes more embedded in critical decision-making processes, the demand for transparency has given rise to the field of explainable AI (XAI). Researchers and developers are working to build models that not only deliver accurate predictions but also provide clear reasoning behind their decisions. The goal is to create AI systems that users — whether business leaders, regulators or everyday consumers — can understand and trust.
Several techniques have emerged to improve AI transparency. Approaches include:
- Model Distillation: Simplifies complex models into more interpretable versions without significantly sacrificing accuracy
- SHAP (Shapley Additive Explanations) Values: Assign a level of importance to each feature in an AI model’s decision, offering a clearer picture of why one outcome was chosen over another
- LIME (Local Interpretable Model-Agnostic Explanations): Generates approximations of AI predictions in a way that makes them easier for humans to interpret
As the demand for explainability grows, researchers are refining techniques that provide clearer insights into AI’s decision-making process. Rao told VKTR that "for modern applications that use LLMs, explainability becomes much more difficult due to the sheer size of the models. Techniques like SHAP and LIME can be tweaked to handle a sequence of tokens, but they may not be highly effective."
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Other methods, he said, are gaining prominence, including:
- Counterfactual Analysis: A way of understanding AI decisions by asking ‘what if’ questions
- Attention-Weight Visualization: A technique used to understand how AI models focus on different parts of the input data when making decisions
One compelling example of XAI in action comes from the healthcare sector. Researchers at Mount Sinai Hospital developed an AI model for diagnosing diseases based on medical imaging. While deep learning models often operate as black boxes, the team used interpretability techniques to highlight the specific areas of an image that influenced a diagnosis. This allowed radiologists to verify AI-generated insights, improving both accuracy and trust in the system. Similar efforts are being made in credit scoring, where AI-powered lending models must justify their decisions to ensure fairness and regulatory compliance.
Balancing AI Performance With Interpretability
One of the biggest challenges in AI development is finding the right balance between performance and transparency. Many of the most advanced AI models, especially deep learning systems, deliver exceptional accuracy but operate in ways that are difficult to decipher, making it challenging to trace the reasoning behind their outputs. In many industries, this trade-off raises critical ethical and regulatory questions:
- How much explainability should be sacrificed for performance?
- At what point does a lack of transparency become unacceptable?
Different stakeholders have varying perspectives on this issue. Tech companies, particularly those developing AI for commercial applications, often prioritize performance, arguing that increasing transparency can sometimes reduce accuracy and limit real-world effectiveness. Regulators, on the other hand, emphasize the need for transparency, particularly in high-stakes industries where opaque AI decisions can have life-altering consequences. Ethicists and AI researchers advocate for a middle ground, pushing for systems that remain highly functional while also providing meaningful explanations that end users can trust.
While many assume that performance and transparency are at odds, the tradeoff is not as rigid as it may seem. In some cases, simpler, more interpretable models can deliver high-quality results without sacrificing accuracy. Paul Ferguson, founder of Clearlead AI Consulting, said, "The idea that we have to sacrifice transparency for performance is not entirely accurate. Some AI models, like tree-based methods, provide a view of their decision-making process without compromising on performance.”
Several potential solutions are emerging to address this dilemma. One approach is hybrid AI models that combine interpretable techniques — such as decision trees or rule-based algorithms — with deep learning models, ensuring that at least part of the decision-making process remains understandable.
Regulatory frameworks, such as the EU’s AI Act, are also setting transparency requirements that push businesses to prioritize explainability, particularly in cases where AI is making significant decisions about individuals’ lives. Another promising strategy is human-AI collaboration, such as the example of Mount Sinai Hospital, where AI assists in decision-making but allows human oversight, ensuring that complex or ambiguous cases are reviewed by people rather than automated systems.
As AI continues to evolve, finding the right balance between power and interpretability will be a central concern for businesses, regulators and society as a whole. While no single solution will solve all transparency challenges, ongoing research and regulation will shape how AI is designed, deployed and trusted in the years ahead.
Kathryn Wifvat, founder of Nuubi, told VKTR that AI regulation should not be seen as a burden but as a safeguard against the illusion of objectivity in automated decisions. "People need to understand the limitations of AI and not fully trust everything it produces. It is a powerful tool for gaining insight rapidly, but it must be countered with human rationale, subject matter experts and fact-checking."
Related Article: Cultivating a Culture of Innovation in the GenAI Era
Beyond the Black Box: Building AI We Can Trust
The AI black box remains a major challenge as we strive to balance powerful capabilities with transparency and accountability. While explainable AI and hybrid approaches offer progress, there is no single solution that fully eliminates the issue.
As AI influences critical decisions in healthcare, finance and other critical industries, making these systems more transparent — without compromising their effectiveness — will be key to ensuring they serve society’s best interests.
