What is Black Box in AI?
The term Black Box AI refers to AI systems whose internal workings and decision-making systems are not transparent or understandable by users or even the creator of that AI system.
The term “black box” is used because the decision-making process is opaque, much like the contents of a sealed opaque box.
The Black Box AI models take in inputs and generate outputs, but the logic and data used to reach those results are not accessible, making it difficult or even impossible to fully understand how they operate.
But the question is, why is there no transparency in Black Box AI? Well, there are multiple reasons.
Reasons Behind Black Box AI Problem
Think of deep neural networks; they are large and super complex, and after developing them to a certain point, you’d not be able to predict how they came up with the output.
Here are several reasons why Black Box AI has no opacity:
- Complexity: Many AI models, especially deep neural networks, consist of thousands of artificial neurons working together in a diffuse manner. This complexity makes it difficult to trace how specific inputs lead to specific outputs.
- Dimensionality: Some machine-learning algorithms, like support vector machines, rely on geometric relationships among many variables that humans cannot easily visualise or understand.
- Lack of traceability: Systems like deep learning models are criticised for lack of traceability. These models are trained over huge amounts of data and involve adjusting millions of parameters. Once trained, there’s close to zero chance you can trace back the specific data points or features that influenced a particular decision.
Applications of Black Box AI
Despite having zero transparency, there are multiple applications of Black Box AI. Here are top 4 applications of Black Box AI:
- Algorithmic Trading: Black box AI models are extensively used in algorithmic trading to analyse market data and make trading decisions. These models can process vast amounts of financial data to identify patterns and execute trades at high speeds, generally outperforming human traders.
- Cryptocurrency Trading Bots: They use black box AI to analyse market data and make trading decisions automatically. These bots can process large datasets and execute trades based on complex algorithms that are not easily interpretable. The lack of transparency in these models helps maintain the competitive edge of the trading strategies employed by the bots.
- Neural Processing Units (NPUs): NPUs are specialised hardware designed to accelerate neural network computations. These devices are trained using huge amounts of data for advanced use cases, and once they are trained, it will not be possible to trace the source of the outcome, even for the engineer who trained the NPU.
- Recommendation Systems: Platforms like Amazon and Netflix use collaborative filtering, matrix factorisation, and deep learning models to recommend products and content. As it sits on the data of millions of users, recommendation algorithms are scaled in such a way that it is not possible to track which data set leads to which recommendation.
Challenges of Black Box AI
Black Box AI, being not transparent, poses some challenges, making it difficult to adapt.
- Lack of transparency and interpretability: Black Box AI models are inherently complex. Which means you can not trace the output to its source and how it was processed. This opacity can lead to a lack of trust and accountability.
- Biasesness: AI models have their biases based on their training data. As Black Box AI won’t let you trace back to the dataset output, you can never be sure if the generated output is biased or not.
- Healthcare: AI systems used for medical diagnostics can provide highly accurate results but often lack transparency, making it difficult for doctors to understand and trust the AI’s recommendations.
- Error Handling: When AI systems produce unwanted or erroneous outcomes, the black box nature makes it difficult to diagnose and fix the underlying issues. This can lead to a lack of confidence in the system’s reliability and robustness.
- User Trust: The inability to understand how Black Box AI models arrive at their decisions can erode user trust. Consumers and businesses may be hesitant to adopt AI-powered products and services that are not transparent about how their data is being used and processed.
While Black Box AI has some challenges, it reflects on how far we have come in terms of development, and we are slowly losing track of what’s going on under the hood.
Sure, for some people, it’d pull the trigger, but we as humanity should strive to solve issues we have with Black Box AI and try to make it transparent, so much so that it is no longer labelled as Black Box AI.
Difference Between Black Box and White Box in AI
| Key Difference | Blackbox AI | White Box AI |
| Decision-making process | hidden | fully visible |
| Output Predictability | Not predictable | Predictable |
| Complexity | Difficult to Understand | Transparent and easy |
| Accuracy | High | Low |
