“If you cross the river Halys, a great empire will fall,” was the cryptic message the Oracle of Delphi gave King Croesus. Unfortunately for the king, he interpreted it to mean, if he attacked Persia by crossing the Halys River, the Persian Empire would fall. However, fate is a cruel jester, often laughing at us as it pulls the strings of our downfall. The prophecy is a classic case of an expression being enigmatic enough that its meaning is opaque. Interpretation matters. The prophecy didn’t say which great empire would fall. King Croesus projected his desire to be victorious on the prophecy and that sealed his fate, his naivete and overconfidence blinding him to the disastrous risks he undertook.
This allegory is often cited as a lesson in the dangers of misreading ambiguous messages, but it also speaks of the dangers of accepting information from opaque messengers who speak in riddles. In many ways, business executives could be making a similar mistake with a technology that is filled if not with riddles, then certainly with mystery and opaqueness — artificial intelligence (AI).
In its Winning the Race, America’s AI Action Plan
What is Black Box AI?
AI refers to the development of computer systems that can perform tasks that typically require human intelligence – such as visual perception, speech recognition, and decision-making. AI systems, including black box AI models, have the potential to automate complex tasks and improve predictive capabilities. However, the lack of transparency in black box AI systems poses significant challenges, including data security issues, bias, and the difficulty in validating results.
Black box AI refers to a system where the internal decision-making modeling process is opaque and difficult to understand, even for the developers who build it. The term comes from the idea of a “black box” in engineering: data goes in, results come out, but the inner mechanisms remain a mystery. These mechanisms are usually machine learning or deep learning models.
Deep Learning
According to SAS , deep learning is “a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing.”
Deep learning uses multilayered neural networks, which can have hundreds or even thousands of layers. “Each layer contains multiple neurons, which are bundles of code designed to mimic the functions of the human brain,” explains Kosinski. “Deep neural networks can consume and analyze raw, unstructured big data sets with little human intervention,” he adds.
However, these deep neural networks are inherently opaque. Users, including the developers who created them, can see what happens at the input and output layers, but not what happens outside of that. They don’t know “what it means when a certain combination of neurons activates, or exactly how the model finds and combines vector embeddings to respond to a prompt,” says Matthew Kosinski in his article, What is black box artificial intelligence (AI)?
Key Characteristics
Opacity: Users, developers, and modelers can’t inspect how a model reaches its conclusion.Complexity: Utilizes advanced algorithms, frequently involving millions of parameters and many processing layers.Data-driven learning: Learns to identify patterns and correlations in massive datasets through training rather than fixed rules.Lacks explainability: Users cannot trace the specific logic or features responsible for an outcome.Superior predictive power: Excellent at making accurate predictions and classifications in complicated scenarios.
Ghosted by the Algorithm: Erasing Women from the Hiring Pool
Kosinski considers the example of “a black box model that evaluates job candidates’ resumes. Users can see the inputs—the resumes they feed into the AI model. And users can see the outputs—the assessments the model returns for those resumes. But users don’t know exactly how the model arrives at its conclusions—the factors it considers, how it weighs those factors and so on.”
Unfortunately, these models can be highly problematic and baised against female candidates. According to the BBC , “Amazon.com Inc’s machine-learning specialists uncovered a big problem: their new recruiting engine did not like women.” An unsurprising result considering the system’s model trained mostly on men’s resumes, a stark contrast to traditional models which were often more balanced “In effect, Amazon’s system taught itself that male candidates were preferable. It penalized resumes that included the word ‘women’s,’ as in ‘women’s chess club captain.’ And it downgraded graduates of two all-women’s colleges, according to people familiar with the matter. They did not specify the names of the schools,” adds Reuters .
“These complex black boxes can deliver impressive results, but the lack of transparency can sometimes make it hard to trust their outputs. Users cannot easily validate a model’s output if they don’t know what’s happening under the hood. Furthermore, the opacity of a black box model can hide cybersecurity vulnerabilities, biases, privacy violations and other problems,” says Kosinski.
In general, AI developers understand how data moves through every layer of a network, and they have a general sense of what the models do with the data they ingest, but the specifics elude them, says Kosinski. “Even open-source AI models that share their underlying code are ultimately black boxes because users still cannot interpret what happens within each layer of the model when it’s active,” adds Kosinski.
The Black Box Dilemma
The black box dilemma refers to the lack of transparency and accountability in AI systems, particularly in black box AI models. The most advanced AI, ML, and deep learning models are extremely powerful, but their power comes at a price — lower interpretability, contends Kosinski . In many cases, “organizations cannot solve the black box problem by simply using more explainable, traditional AI tools. Traditional AI models can perform many functions, but there are some things that only an advanced AI model can do,” reiterates Kosinski. The black box AI problem arises from the complexity of deep learning algorithms and neural networks, which can make it difficult to understand how the system makes decisions.
Black box AI Generative AI models, like Chat GPT, Google’s Gemini, and Anthropic’s Claude, “rely on complex neural networks to respond to natural language commands, solve novel problems and create original content, but it’s difficult to interpret what happens inside those networks,” says Kosinski. Simple, rule-based AI models can be easy to explain, but they are usually less powerful and less flexible than generative AI models,” claims Kosinski.
The black box problem can lead to a lack of trust, bias, and security flaws, which can have significant consequences in applications such as criminal justice and healthcare. “While there might be practical reasons to use black box machine learning models, the lack of transparency can be an obstacle to getting the full value from these advanced models,” warns Kosinski.
Examples of Black Box AI
Most of today’s advanced machine learning and deep learning models are black box AIs. This includes large language models (LLMs), like ChatGPT, Gemini, Claude, DeepSeek, and Perplexity. These LLMs train “on massive data sets through complex deep learning processes, and even their own creators do not fully understand how they work,” claims Kosinski. These complex machine learning models often utilize deep neural networks. Their internal workings are opaque and difficult to interpret. These systems generate human-like text, but the systems can’t explain why they choose their word.
When using black box AI models, users can observe the input data as well as the output results, but they can’t see or even easily ascertain how internal decisions, predictions, or classifications are made. However, these black box models are widely used because they deliver high levels of accuracy in tasks, such as with image and speech recognition, natural language processing, fraud detection, and autonomous driving. These models regularly outperform simpler, more transparent models.
Other black box AI systems include algorithmic decision systems that can also be highly opaque. Loan approval AIs reject applicants without giving clearly defined reasons. Predictive policing tools can flag “high-risk” neighborhoods based on hidden biases. Autonomous vehicles can brake suddenly for no rhyme or reason. Still to this day, engineers struggle to replicate this strange car behavior.
Today, if AI misdiagnoses a patient or causes a fatal car crash, it is unclear who holds responsibility for this mistake. The engineers, the company, the AI itself, nobody knows. Fatal Tesla Autopilot crashes raise important legal and ethical questions about liability and the courts have yet to decide who ultimately pays the price. These are questions the courts currently struggle with. When AI causes harm, it’s unclear who deserves blame. This is another reason why AI desperately needs more transparency.
Hallucinations
Today, “many of the most advanced AI technologies, including generative AI tools, are what one might call ‘organic black boxes.’ The creators of these tools do not intentionally obscure their operations. Rather, the deep learning systems that power these models are so complex that even the creators themselves do not understand exactly what happens inside them,” contends Kosinski. This is especially problematic for LLMs because they often “hallucinate”, which refers to the phenomenon whereby a model generates a plausible sounding but factually incorrect answer. Sometimes these answers are fabricated or completely nonsensical, yet they often come with fake citations that look all too real. Querying an LLM about the veracity of their claim often reveals the truth, but LLMs can present their claims with a certainty that makes them seem correct.
Types of Hallucinations
Type Example Factual Errors Claiming “The Eiffel Tower is in London” (confidently, with fake citations). Fabricated Sources Inventing academic papers or news articles that don’t exist. Logical Nonsense “The FBI used Spotify to find kidnap victims.” Misinterpretation Translating “French doors” as “doors that speak French.”
Why Black Box AI Exists
Kosinski argues black box AI exists for two reasons; either the developers created them on purpose, or the black box AI element of the model matured through training. Neural networks can have millions of parameters, which interact with each other in linear and nonlinear ways, making them incredibly opaque. Powerful, yes, but also hard to understand. Higher accuracy often comes at the cost of interpretability. This is the “accuracy vs. explainability” dilemma. Some tech companies, like Google, protect their AI’s logic as they would their other intellectual property, i.e., to the fullest extent of the law.
In addition, some AI developers and programmers obscure the inner workings of their AI tools before releasing them publicly, says Kosinski. “This tactic is often meant to protect intellectual property. The system’s creators know exactly how it works, but they keep the source code and decision-making process a secret. Many traditional, rule-based AI algorithms are black boxes for this reason,” says Kosinski.
Implications
The opacity of black box AI models raises concerns about accountability, trust, fairness, and ethics of the model’s data. As a result, the field of “explainable AI” (XAI) has emerged to make AI systems more transparent and understandable to human users. As Blackwelltech explains in its article, The Explainability Imperative: Why Black Box AI Is Dead in Enterprise
The opacity of black box AI can hide biases. Hidden biases in training data can lead to unfair outcomes. Apple Card’s gender bias scandal showed that decisions based on AI can unfairly deny loans to minorities. According to Wired Wired .
Ultimately, black box AI erodes trust. People distrust systems they can’t understand. A 223 Pew study found 58% of Americans fear AI’s opacity in critical decisions. Companies using black-box AI risk scandals as well as alienating their customers.
Legal & Regulatory Risks
Using unexplainable AI systems can expose organizations to legal liabilities in areas of negligence, anti-discrimination, and misleading conduct. These risks are heightened by regulations that increasingly demand transparency and fairness, especially in finance, healthcare, and human resources. GDPR in the EU and the EU AI Act mandate’s “right to explanation” for automated decisions. Black boxes transgress this directive.
In his article, GDPR a challenge to AI black boxes
“We need to find a way to design and use machine learning algorithms in a way that is compliant with the GDPR, because they will generate value for both service providers and data subjects if done correctly,” says Guarino. The GDPR principle of data minimisation is also a potential challenge to machine learning algorithms because any processing activity is required to process only the data needed for a specific purpose, says Guarino.
The Digital Welfare State: When Fraud isn’t Fraud
Governments around the world have turned to AI to help with the growing deluge of welfare claims as well as to try to reduce fraud, but the transition to AI has been anything but smooth. Countries around the world have implemented bad black box AI models that are causing more harm than good, often resulting in embarrassing and financially devastating ways against the government agencies pushing the AI. In his article, Australian Robodebt scandal shows the risk of rule by algorithm
“Running from July 2015 to November 2019, the scheme used algorithms to calculate the overpayments, raising more than A$1.7 billion ($1.2 billion), which the government was forced to repay or wipe when a court ruled the scheme unlawful in 2019,” says Starcevic.
In Australia, 400,000 welfare recipients in Australia were wrongly accused of misreporting income to the welfare agency and repeatedly slapped with fines. “The errors were the result of an automated debt recovery scheme, known as Robodebt, set up by the former conservative coalition government, which wrongly calculated that the welfare recipients owed money and so issued a ream of debt notices,” says Starcevic.
When the inquiry was announced, Government Services Minister Bill Shorten called Robodebt a “shameful chapter in the history of public administration” as well as a “massive failure of policy and law”.
Tapani Rinta-Kahila, a professor at the University of Queensland who studies the use of AI in the public sector, agrees. He said algorithmic schemes are “inherently problematic” because they lack humanity. No matter how good or how sophisticated the algorithim is, it still cannot address fundamental human issues as sensitive as welfare, Rinta-Kahila said.
Class Action Settlement
$1.2B
The class action is settled, with the Australian Government repaying more than $751 million in unlawfully claimed debts and $112 million in compensation to approximately 400,000 people.
Learning from the failures of Robodebt – building a fairer, client-centred social security system
Explainable AI
To address the black box AI challenge, researchers are developing explainable AI tools that balance model performance with the need for AI results transparency, says Kosinski . According to Blackwelltech Corp , “global explainable AI (XAI) interest is growing, mainly driven by highly regulated industries such as healthcare, finance, and the public sector.”
Trustworthy systems built upon understandable reasoning and human-centric accessibility will define the future of enterprise AI, claims Blackwelltech Corp. This requires integrating interpretability into model development from the start, adopting native explainability frameworks, and embedding human oversight into AI decision-making loops, argues Blackwelltech Corp. Governance structures prioritizing transparency must be created. “Explainable AI has become, very quickly, a basic need to trust Artificial Intelligence for Enterprises,” concludes Blackwelltech Corp.
Developed over the past decade, SHAP and LIME are two of the most widely used techniques that make complex black box AI models more explainable.
SHAP
In their paper, A Unified Approach to Interpreting Model Predictions
SHAP provides both global, entire dataset, and local, single prediction, explanations
LIME
In Why Should I Trust You? : Explaining the Predictions of Any Classifier
Also model-agnostic, the LIME technique produces
SHAP & LIME Key Differences
Aspect SHAP LIME Method Game theory (Shapley values) Local linear surrogate model Scope Local & Global explanations Local explanations only Model-agnostic Yes Yes Accuracy More theoretically rigorous Good local fidelity, less robust Computation Slower, especially on large data Faster for individual explanations Output Consistent, additive feature attributions Feature importances for a sample
SHAP gives a complete and theoretically justified picture of feature contributions, both locally and globally, while LIME provides quick, intuitive explanations for individual predictions but does not guarantee global consistency
Future of Black Box AI
The future of black box AI is uncertain, as the lack of transparency and accountability in AI systems poses significant challenges. Explainable AI and transparent models are essential to addressing these challenges and ensuring that AI systems are fair, accountable, and secure. AI developers must prioritize transparency, explainability, and security to maintain trust and accountability in AI systems. The development of new AI technologies and tools, such as edge AI and transfer learning, will continue to shape the future of black box AI.
The black box AI questions isn’t a “black vs. white” debate. It’s more subtle than that. It’s a question of how to balance accuracy with transparency, especially in high-stakes fields like healthcare, pharmaceuticals, law, and finance. Black box AI can enable faster, more accurate decision-making, often improving efficiency and optimizing operations in the process, but its opaqueness is a real problem.
AI: Riddle Inside a Mystery Wrapped Inside an Enigma
Winston Churchill once famously called the Soviet Union “a riddle wrapped in a mystery inside an enigma”. It is quote that well describes AI’s machine learning and deep learning modeling systems as well. Both had layers of complexity and mystery going on under the surface that made them almost impossible to fully understand.
The cautionary tale of King Croesus and the Delphic oracle mirrors today’s challenges with black box AI, a technology as enigmatic as an ancient prophecy delivered by a prophet. While AI’s opaque systems drive innovation, their risks demand solutions like explainable AI, regulatory frameworks such as the EU AI Act, and tools like SHAP and LIME to bridge the gap between performance and transparency.
“Winning the AI race will usher in a new golden age of human flourishing, economic competitiveness, and national security for the American people,” the White House promises. If AI is handled well, this might be true, but big risks remain.
King Croesus assumed the Oracle of Delphi’s prophesy about the destruction of an empire meant the Persians were vulnerable. However, in reality, his empire was the one in jeopardy and was, ultimately, destroyed. The future of AI rest upon not just accuracy but also accountability to ensure its decisions are fully transparent and don’t lead to catastrophic mistakes that might lead to the end of a glorious empire.
