What Is Black Box AI?
Black box AI is a type of artificial intelligence (AI) that is so complex its decision-making process cannot be understood or explained by humans. In practice, this means the user has no transparency over the variables used to make decisions.
Proprietary AI solutions like ChatGPT, Claude, and Gemini can be considered black box AI as the user has little insight into what data the chatbot was trained on and how it decides to generate its outputs.
We can consider black box AI the opposite of explainable AI systems, which are designed to offer greater transparency of how a model makes decisions and predictions.
Key Takeaways
- Black box AI is a type of AI that cannot easily be understood or explained by users.
- Well-known examples of black box AI include ChatGPT, Claude, and Gemini.
- These models are the opposite of explainable AI systems.
- Many companies maintain black box AI to enable complex applications and maintain a competitive advantage.
- Challenges of black box AI include a lack of transparency and the potential for cyberattacks.
How Black Box AI Models Work
Black box AI models function by using a deep learning model with an artificial neural network (ANN). This neural network is made up of tens of thousands of neurons, which process a large dataset and identify patterns. Analyzing these patterns allows the model to make predictions and decisions.
The problem is that often these neural networks become so complex that an AI engineer can struggle to understand why certain decisions are being made.
This is because deep neural networks (DNNs) and deep learning algorithms create thousands, and sometimes millions, of non-linear relationships between inputs and outputs. These complex relationships make it difficult to explain what interactions led to an output.
Likewise, if users enter input into the model via a chatbot, they have limited insight into why the model generates a certain output, particularly if they don’t have access to the source code or training data.
Why Black Box AI Models Are Used
So, what is black box AI used for? Black box models can process large datasets and make highly accurate decisions or predictions. Researchers will use black box models because they’re willing to sacrifice explainability for a model that makes more accurate decisions.
Black box AI models are also useful because they enable leading AI companies to maintain a competitive advantage as they can put out a high-performance model without having to disclose how it works under the hood.
For example, if OpenAI explained how ChatGPT works and what data it’s trained on, then other competitors would replicate this approach, so maintaining opaqueness is beneficial for building a moat around a model.
That being said, it is worth noting that even open source AI models can be considered as black box if users are unable to understand how its decisions are made within the model’s neural network.
Black Box AI Uses
As mentioned above, black box AI is used to build powerful models capable of making accurate decisions and predictions based on large datasets. Engineers don’t need to understand all the factors a mode takes into account to generate its output; they just need to understand the output itself.
Using a black box approach also helps to protect intellectual property from falling into the hands of competitors. For these reasons, many developers opt to use a black box approach, instead of creating a white box system.
Black Box AI vs. White Box AI
AI models are often characterized as black box or white box AI:
Feature | Black box AI | White box AI |
---|---|---|
Definition | An AI model where the user has limited transparency over internal operations | An AI model where the user has complete transparency over internal operations |
Key features | Complex, difficult to explain, non-linear | Simple, easy to explain, more linear |
Examples | ChatGPT | Linear regression, decision trees, rule-based systems |
Black Box AI Challenges
Arguably the biggest issue with black box AI development is that there’s little oversight over how decisions are made. Both researchers and users have to trust that the model has made decisions based on high-quality data without unfair bias or prejudice.
For example, if you ask ChatGPT a cultural or historical question, you have no way to check if political or ideological bias will influence the chatbot’s answer. This means you can’t afford to blind trust the output of the models.
At the same time, a lack of visibility into an AI model’s internal operations can lead to overlooked cybersecurity vulnerabilities. Threat actors can exploit those vulnerabilities in a model to perform prompt injection and data poisoning attacks, which can compromise sensitive information and or even attempt to damage the reliability of its training data.
To reduce some of these risks, it’s important to use AI systems with robust cybersecurity measures, such as antivirus software, to prevent potential breaches.
Black Box AI Pros & Cons
Developing black box AI has a number of pros and cons.
Some of these are as follows:
Pros
- Can analyze large volumes of data
- Capable of making more accurate decisions
- Prevents intellectual property from being exposed to third-party competitors
- Enables AI companies to be able to maintain a competitive advantage against other competitors
Cons
- Difficult to understand what is happening under the hood and how decisions are made
- Lack of oversight of a model’s operations makes it difficult to mitigate bias
- Users may find it more difficult to trust a model due to a lack of visibility
- Risk of vulnerabilities being exploited to conduct data poisoning and prompt injection attacks
The Future of Black Box AI
Given how lucrative the AI market is, we can expect to see black box AI development continue for the foreseeable future as providers like OpenAI and Anthropic continue to build the most complex and accurate models possible.
That being said, the growth of the open source community, and the movement of providers like Meta and Ai2 releasing a mix of model training data and weights publicly, we can expect to see explainability and transparency increase in the future.
The Bottom Line
In summary, the definition of black box AI is a type of AI whose internal workings can’t be easily understood or explained. Given the success of products like ChatGPT and Claude, we can expect to see opaque models remain dominant for the foreseeable future.