BlackBox AI
What is Black Box AI?
Black box AI is any artificial intelligence system whose inputs and operations aren’t visible to the user or another interested party. A black box, in a general sense, is an impenetrable system.
Black box AI models arrive at conclusions or decisions without explaining how they were reached.
As AI technology has evolved, two main types of AI systems have emerged: black box AI and explainable (or white box) AI. The term black box refers to systems that are not transparent to users. Simply put, AI systems whose internal workings, decision-making workflows, and contributing factors are invisible or unknown to human users are black-box AI systems.
The lack of transparency makes it hard for humans to understand or explain how the system’s underlying model arrives at its conclusions. Black box AI models might also create problems related to flexibility (updating the model as needs change), bias (incorrect results that may offend or damage some groups of humans), accuracy validation (hard to validate or trust the results), and security (unknown flaws make the model susceptible to cyberattacks).
How do Black Box Machine Learning Models Work?
When a machine learning model remains developed, the learning algorithm takes millions of data points as inputs and correlates specific data features to produce outputs.
The process typically includes these steps:
- Sophisticated AI algorithms examine extensive data sets to find patterns. The algorithm ingests many data examples, enabling it to experiment and learn independently through trial and error. As the model gets more and more training data, it self-learns to change its internal parameters until it reaches a point where it can predict the exact output for new inputs.
- As a result of this training, the model is finally ready to make predictions using real-world data. Fraud detection using a risk score is an example use case for this mechanism.
- The model scales its method, approaches, and body of knowledge and produces progressively better output as additional data is gathered and fed over time.
In many cases, the inner workings of black box machine learning models are mainly self-directed and not readily available. This is why it’s challenging for data scientists, programmers, and users to understand how the model generates its predictions or trust its results’ accuracy and veracity.
How do Black Box Deep Learning Models Work?
Many black box AI models are based on deep learning, a branch of AI- notably, machine learning- in which multilayered or deep neural networks mimic the human brain and simulate its decision-making ability. The neural networks comprise multiple layers of interconnected nodes known as artificial neurons.
In black box models, these deep networks of artificial neurons disperse data and decision-making across tens of thousands or more of neurons. The neurons work together to process the data and identify patterns, allowing the AI model to predict and arrive at certain decisions or answers.
These predictions and decisions result in a complexity that can be just as difficult to understand as the complexity of the human brain. As with machine learning models, it’s complex for humans to identify a deep learning model’s “how” or the specific steps it took to make those predictions or arrive at those decisions. For all these reasons, such deep learning systems are known as black-box AI systems.
Issues with Black Box AI
While black-box AI models are appropriate and highly valuable in some circumstances, they can pose several issues.
1. AI bias
AI bias can remain introduced into machine learning algorithms or deep learning neural networks as a reflection of conscious or unconscious prejudices on the part of the developers. Bias can also creep in through undetected errors or from training data when details about the dataset are unrecognized. Usually, the results of a biased AI system will be skewed or outright incorrect, potentially in a way that’s offensive, unfair, or downright dangerous to some people or groups.
Example
An AI system used for IT recruitment might rely on historical data to help HR teams select candidates for interviews. However, because history shows that most IT staff in the past were male, the AI algorithm might use this information to recommend only male candidates, even if the pool of potential candidates includes qualified women. Simply put, it displays a bias toward male applicants and discriminates against female applicants. Similar issues could occur with other groups, such as candidates from certain ethnic groups, religious minorities, or immigrant populations.
With black box AI, it’s hard to identify where the bias comes from or if the system’s models are unbiased. If the inherent bias results in consistently skewed results, it might damage the organization’s reputation using the system. It might also result in legal actions for discrimination. Bias in black box AI systems can also have a social cost, leading to the marginalization of, harassment of, wrongful imprisonment of, and even injury to, or death of, certain groups of people.
AI developers must build transparency into their algorithms to prevent such damaging consequences. They must also comply with AI regulations, hold themselves accountable for mistakes, and commit to promoting AI’s responsible development and use.
In some cases, techniques such as sensitivity analysis and feature visualization can provide a glimpse into how the internal processes of the AI model are working. Even so, in most cases, these processes remain opaque.
2. Lack of Transparency and Accountability
The complexity of black-box AI models can prevent developers from adequately understanding and auditing them, even if they produce accurate results. Some AI experts, even those who were part of some of the most groundbreaking achievements in the field of AI, don’t fully understand how these models work. Such a lack of understanding reduces transparency and minimizes a sense of accountability.
These issues can be highly problematic in high-stakes fields like healthcare, banking, military, and criminal justice. Since the choices and decisions made by these models cannot be trusted, the eventual effects on people’s lives can be far-reaching and not always in a good way. It can also be challenging to hold individuals responsible for the algorithm’s judgments if it is using hazy models.
3. Lack of flexibility
Another big problem with black box AI is its lack of flexibility. If the model needs to be changed for a different use case — to describe a different but physically comparable object — determining the new rules or bulk parameters for the update might require a lot of work.
4. Difficult to validate results
The results black box AI generates are often difficult to validate and replicate. How did the model arrive at this particular result? Why did it arrive only at this result and no other? How do we know that this is the best/most correct answer? It’s almost impossible to find the answers to these questions. And to rely on the generated results to support human actions or decisions. This is one reason why it’s not advisable to process sensitive data using a black-box AI model.
5. Security flaws
Black box AI models often contain flaws that threat actors can exploit to manipulate the input data. For instance, they could change the data to influence the model’s judgment so it makes incorrect or even dangerous decisions. Since there’s no way to reverse engineer the model’s decision-making process.
It’s almost impossible to stop it from making bad decisions.
It’s also difficult to identify other security blind spots affecting the AI model. One familiar blind spot remains because third parties access the model’s training data. Suppose these parties fail to follow good security practices to protect the data. In that case, it’s hard to keep it out of the hands of cybercriminals. Who might gain unauthorized access to manipulate the model and distort its results.
