As artificial intelligence continues to evolve, countless new terms are emerging for consumers, businesses, and regulators to define and understand. Black box AI, often referenced alongside explainable AI (XAI) and responsible AI is one such (somewhat confusing) term.
On a broad level, “black box AI” is just a term used to refer to any AI system that doesn’t make its systems, processes, and operations “visible” to a user. In other words – we can’t fully see how the AI model works or why it reaches certain conclusions.
That’s more of a problem than you might think as new AI regulations (like the EU AI Act) and governance strategies emerge – focusing on the importance of transparency and explainability.
Here’s everything you need to know about black box AI, how it works, its risks and potential benefits – and how you can navigate the possible challenges.
What is Black Box AI? A Quick Definition
Not to be confused with the Blackbox AI chatbot model, Black box AI is a term used to describe any artificial intelligence system that isn’t “transparent” to users. With these AI tools, you can’t really “follow” what the AI is doing – how it processes information or makes decisions.
For instance, imagine an AI model designed to help a company filter through resumes to choose the right candidate for a role. The users could see what they “input” into the model (such as resumes and job descriptions). However, they might not see how the model makes decisions that allow it to rank applicants based on their potential for the role.
Many regulators consider black box AI a problem when trying to govern how companies and consumers use artificial intelligence, but it’s still incredibly common. As developers build more advanced machine learning and deep learning models, understanding how they work is not always easy.
Some of the world’s most popular AI tools are examples of “black box AI”, such as OpenAI’s ChatGPT, Google’s Gemini, and even Meta’s Llama. We know they’re trained on massive data sets and use a range of deep learning processes. But even their creators don’t fully understand how they work. That’s why they implement guardrails to make the models “safe”.
While complex black box models can deliver great results, the lack of transparency means we’re never sure if we can “trust” their output. We don’t know what’s happening behind the scenes or whether there are any security issues, privacy problems, or biases.
How Does Black Box AI Work? Machine and Deep Learning
The whole problem with black box AI is that we don’t fully know how these models work. We only have a basic knowledge of the algorithms and processing strategies they use. For instance, when a company develops a machine learning model, they infuse it with a “learning algorithm” that assesses millions of data points and makes connections between them.
We know the machine learning model examines the data sets its given, experimenting with how different points work together through trial and error. As the model is given more data, it corrects its assumptions and “learns” over time. Eventually, this enables the model to make predictions based on real world data, and can scale its method and approaches progressively.
However, we don’t know exactly what methods the model uses to improve over time. Similarly, many black box AI models leverage deep learning – which is a branch of AI that can be tough to understand. With deep learning, models use deep neural networks to mimic the decision-making abilities of the human brain. Networks of artificial neurons disperse decision making processes across multiple neurons, allowing the AI to arrive at specific predictions and decisions.
Just as we can’t always identify how human brains reach decisions, it’s not always easy to see the specific steps a deep learning model takes to process data and arrive at a result.
Why Does Black Box AI Exist?
Obviously, most developers want to understand how their AI models work. That’s not just for compliance or ethical purposes. These developers need to understand the inner workings of their models so they can fix potential issues over time. However, there are cases when developers create black box AI systems on purpose.
For instance, before releasing a tool to the public, a developer or programmer might obscure the inner workings to protect intellectual property. In this case, the model isn’t truly black box AI because the creators know how it works—but other people don’t.
Organic black box AI models occur more commonly and unintentionally as a result of the training methods used to create these tools. Companies building powerful generative AI tools don’t necessarily make them difficult to understand on purpose. They simply use deep learning models that are hard to explain.
As mentioned above, these models give AI systems a lot of potential, ensuring they can complete tasks like processing language and creating content with near-human intelligence. But deep learning models are inherently opaque. Users can only see what’s happening in the model’s “visible layers” (the input and output layers), but they can’t see the hidden layers.
Even if they know (broadly) what’s going on as data moves through each network layer, they don’t know the specific combination of neuron activities that lead to an output. Even open-source AI models that share their code with other users can be black boxes because users don’t fully understand what’s happening throughout each model layer.
The Challenges of Black Box AI: Why we Need Visibility
Black box AI tools are everywhere, from comprehensive GPT and generative AI solutions, to cutting-edge large language models. These models are often extremely powerful – but they’re not easy to interpret or understand. For instance, generative AI models rely on advanced neural networks to respond to commands and create content, but we don’t always know how they work exactly.
Simpler, rule-based AI models are more “explainable”, but they’re also not as flexible as powerful as generative AI models. This means companies can’t just eliminate the “black box issue” by using simpler models – not if they want to tap into more powerful AI tools.
The most significant risks with black box AI include:
Ethical Issues: The Risk of AI Bias
Black box AI models are more subject to the issue of “AI bias” than explainable, rule-based models. AI tools can reproduce human biases if introduced in the model’s training data or design. These biases often occur unintentionally because developers provide models with limited data sets or inaccurate information.
The trouble is, with black box models, it’s difficult to determine the causes of the bias, and what needs to be done to address it. Unfortunately, bias can lead to problematic, harmful, and even dangerous outcomes. For instance, a model used to screen job candidates that has an inherent bias may filter out talented female professionals if the training data highlights male candidates.
Similar issues can occur with other groups and in various settings. For instance, some criminal justice systems have experimented with AI to help them understand a person’s risk of reoffending. When these models are black boxes, it’s hard to see if they discriminate against specific people.
The Issue of AI Trust
For the world to embrace tools like Google Gemini or ChatGPT and use them to make decisions, they need to trust the model’s output and predictions. When we don’t know how a black box AI model makes these decisions, it’s difficult to know whether its trustworthy.
In some cases, black box models can arrive at “correct” conclusions for the wrong reason – the “Clever Hans Effect”. This term comes from a horse that showed it could count by stomping its hoof – but it was just responding to body language cues from its owner.
This issue can have serious implications when black box AI are applied in certain fields – like healthcare for instance. For instance, early AI models used during the COVID-19 pandemic were excellent at diagnosing based on lung X-Ray training data. Still, they didn’t perform as well in the real world – because the models were learning to identify the condition based on the wrong factors. One model even diagnosed COVID based on the annotations it say in an X-ray.
Lack of Transparency and Accountability
Accountability is important to the development of safe AI systems. However, the complexity of black box AI means developers can’t fully understand and audit them. Even AI experts don’t fully understand how the most advanced AI models work.
This causes issues when it comes to identifying why AI models make the wrong decisions, or who should be held accountable for dangerous outcomes. In high-stakes fields like finance, criminal justice, and healthcare, the lack of transparency and accountability can be a problem.
If an AI system makes a mistake when making decisions in this field, the impact on people’s lives can be astronomical.
Difficulties with AI Model Adjustments
If a black box AI model makes the wrong decisions regularly or constantly produces harmful or just inaccurate outputs, it’s often difficult to correct the behavior. After all, developers don’t really know what’s leading to the problem in the first place.
Again, the issues here are significant in certain industries. For instance, if a developer trained an AI system in a car to make real-time safety decisions for a driver, and it make the wrong decisions, the consequences could be fatal. If that happened, the developer couldn’t simply “update” the AI and assume it would work properly.
That’s one of the reasons why companies in the automotive industry augment AI tools with other, more transparent systems, like Lidar and Radar sensors.
Security Issues
Many black box AI models contain flaws that potential criminals could use to exploit and manipulate their performance and even input data. For instance, a criminal could change the data given to a model to influence how the solution makes decisions.
Since there isn’t an easy way for developers to “reverse engineer” the decision-making process, they can’t stop it from making bad choices when that happens. Plus, it’s difficult to identify potential security problems within the AI model.
One common “blind spot” is that third parties might have access to a model’s training data. If those parties don’t follow the same security and governance practices, they may be unable to keep this information away from criminals and bad actors.
Regulatory Compliance Problems
We’re still working towards a future with a clear set of rules for how AI solutions should be governed and regulated. However, many of the emerging regulations in the industry, such as the European Union’s AI Act, do set rules on how organizations can use personal data in an AI-powered tool designed for decision-making.
Since users of black box AI models can’t validate the results a model reaches, or explain which data it used to make a decision, it’s difficult for developers to prove that their system is compliant. This could lead to a higher risk of fines and other issues in the future.
Are There Advantages to Black Box AI?
As mentioned above, black box AI is usually considered a “bad thing.” Relying on unexplainable AI systems has many dangers and disadvantages. However, advanced black box models also have potential benefits.
For instance, complex black box systems can sometimes make more “accurate” predictions than rule-based systems. That’s particularly true in natural language processing and computer vision, where these models can more effectively identify intricate patterns in data that humans miss.
Plus, black box systems can often make decisions faster because they’re built with a fixed set of rules and equations that allow them to run quickly. Some solutions even require less processing power because the processes they use are more straightforward.
In addition, black box AI solutions can be used in a wide range of versatile use cases. They’re often excellent for automating complex decision-making processes and removing the need for additional human intervention. For many companies in different industries, black box AI can help them save time and money.
Black Box AI vs White Box AI: The Difference
So, now you (hopefully) understand black box AI, how does it compare to white box AI? In simple terms, white box or “explainable AI” is the opposite of black box AI. Explainable AI (XAI) systems are designed to ensure an everyday person can understand the decision-making process and logic used by the model.
Rather than just knowing the inputs and outputs of a model (with black box AI), users of white box AI understand all of the system’s internal workings and how it comes to conclusions. This means the model’s results are easy to interpret, explain, and adjust.
The transparency of white box AI makes it a more appealing option for complex decision-making applications, such as financial analysis or medical diagnosis. In general, white box AI is also more “desirable” for other reasons. It can enable engineers and developers to audit the model and confirm it’s working as it’s supposed to.
A system that’s explainable allows those who are affected by its processes to challenge the outcome and make adjustments to the results. Explainability also helps to ensure the system complies with emerging regulatory standards.
Companies can use “explainable” AI to create more “responsible” systems that are less likely to suffer from AI hallucinations and bias, or cause issues as a result of data breaches.
Overcoming the Challenges of Unexplainable AI
Addressing the issue of “black box AI” isn’t easy. If we want to continue pushing the boundaries of what’s possible with artificial intelligence, and creating more sophisticated models, then we can’t simply overlook systems we don’t (yet) fully understand.
Some workflows will always require more sophisticated black-box AI tools – but there are some ways to make these models more trustworthy and potentially reduce their risks, such as:
Using Open-Source Models
As mentioned above, open-source models can still be classified as “black box AI”. However, they generally do give users more transparency and visibility into their operations and development. Open-source models provide access to code and frameworks, whereas closed-source solutions keep their architecture and processes private.
Embracing AI Governance
Embracing and implementing AI governance standards is the key to developing safer, more responsible, and more transparent AI solutions. AI governance involves using standards, processes, and guardrails to ensure that systems and tools operate safely and ethically. It helps organizations to establish comprehensive control strategies for AI strategies.
By implementing governance tools, companies and developers can sometimes gain more insight into how models operate, with automated monitoring, health scores, audit trails, and performance alerts. Although governance doesn’t always make a black box transparent, it can help users identify anomalies and fight back against negative outcomes.
Prioritizing AI Security
AI security strategies and tools can help developers identify and fix vulnerabilities and issues with AI models, data sets, and related applications. These tools can also provide insights into each AI system’s foundational models, deployment data, and application usage. This can help businesses understand when potential security risks and gaps are evident in a model.
The Future of AI: Within the Black Box
Ultimately, black box AI isn’t going anywhere—at least not anytime soon. While there are dangers to relying on unexplainable AI systems, there are also benefits to these sophisticated models. Going forward, however, AI users will need to be a lot more cautious about how they use AI.
Developers can’t afford to simply ignore the risks of black box AI. They’ll need to take measures to ensure they’re monitoring and tracking the performance of their tools. Plus, they’ll need to commit to following strict security and governance standards, and mitigating threats whenever possible.
Black box AI is a challenging issue, but its also one we’ll need to be prepared to tackle as regulatory standards in the AI space evolve.
