When people hear the term ‘black box,’ they think of aircraft recorders to analyze aviation accidents or some secret systems whose operation remains hidden to anyone. In the field of artificial intelligence, it has a special connotation and importance.
A black box in artificial intelligence describes the situation when you know how the data was fed into the system and its output but are unsure how exactly the process occurred. And this uncertainty poses a range of issues for users and developers concerning the problems related to safety, privacy, and accountability.
Let’s consider why it’s crucial to pay attention to the AI black box problem, and what it is.
What Is an AI Black Box?
AI black box can be described as the system whose logic is concealed to users or developers who did not create it.
You provide data as an input, and you get the output; however, the process of reasoning between those two operations is not visible for users.
Machine Learning: Why Is the Process of Reasoning Not Visible?
Nowadays, most AI solutions operate with the help of machine learning, which is one of the branches of AI.
ML uses the following elements in order to create a working system:
Algorithm: set of rules by which a solution will work
Training data (images, texts, etc)
Model – a solution that interacts with the users and performs actions based on the provided data
The process of ML algorithm training is usually carried out separately, and it is impossible for users to understand its outcome until the system is deployed and starts to function.
For instance, if your algorithm was taught how to recognize images containing animals, and a user provides an image to check whether there’s a dog on the photo, the program will either say that it contains a dog or not.
Why is the Process of Reasoning a Black Box?
Each of these elements in a ML system can be regarded as a black box for developers or other people, who are not interested in the details of its creation:
1. Algorithm is public and can be learned by anyone
2. Model is usually stored privately to protect proprietary technology
3. Training data also remains confidential
Thus, people usually place at least one component of their solution into the ‘black box’ to protect their ideas from piracy and reverse engineering.
Glass Box AI vs. Black Box AI: The Difference
Glass box can be considered a direct opposite of the black box.
Below, you can compare the features of glass box systems with those having hidden logic.
Characteristics of Black Box AI
Internal operations of a system are not revealed
Reasoning of a system is not easy to comprehend
Systems focus on performing a task with maximum efficiency
Characteristics of Glass Box AI
All operations are clear to understand
Logic, algorithms, training data are available for analysis
Models are easily auditable
Nonetheless, even glass box AI doesn’t necessarily imply full visibility of the system since the algorithms that were used to train it can be too complicated for human analysis.
Why Understanding How AI Works Is Important?
Even if the person knows how the algorithm was created, it is not always obvious how the AI operates due to the following reasons:
1. Huge amounts of data that are processed by algorithms
2. Complex patterns of operation
3. Specific ways by which the system identifies objects and analyzes them
Therefore, nowadays, a separate field exists, which studies ways to make black box AI solutions understandable. It is referred to as Explainable Artificial Intelligence.
How Does AI Black Box Matter to Us in Everyday Life?
The problem of black box is relevant to us all since it can significantly affect our life as follows:
1. Diagnosis of a disease: doctors and patients require an explanation
2. Finances: you need a reason why your request to take out a loan was denied
3. Other important legal and ethical matters: if AI decides to refuse you employment or insurance coverage, you’ll definitely want to know why
Security Implications of Black Box AI Systems
Once, people thought that concealing the logic of the operation would increase the safety of software. But now it’s proved wrong.
It turns out that a hacker can use a method of reverse engineering to investigate how a piece of software functions and identify possible flaws in its logic.
On the contrary, a glass box system will be more useful because developers will be able to do the following:
Detect vulnerabilities
Find potential threats
Improve the overall security of a system
Consequences: Better Security Due to Collaboration
Thus, it can be argued that transparent systems are safer for users since the entire community of cybersecurity professionals will be involved in the improvement of a particular product or algorithm.
The Importance of Transparency: Problem of Trust
A crucial issue connected with black box systems is a question of trust.
People are more likely to trust software that provides answers to the following questions:
1. What algorithm was used to perform certain operations
2. What kind of training data was used
3. Is there any possibility that AI might be biased
Without transparency, an efficient system will still remain suspicious to people.
Balance of Interests in Creating Black Box Systems
As mentioned above, black box systems are usually created to protect the intellectual property of software companies. However, such secrecy can undermine the confidence of customers and cause distrust in AI.
Thus, developers have to find the optimal balance between providing maximum security and transparency to users.
Conclusion
AI black boxes are one of the most significant concerns for specialists engaged in developing intelligent software.
Future developments will be dedicated to the creation of black boxes in terms of explainable artificial intelligence. It means that there should be more opportunities to visualize decision-making processes.
Main Points
AI black boxes conceal operations of the algorithm
ML models based on large volumes of data are used to develop them
There is little visibility of operations in black box AI
Explainable AI is the area where significant progress can be achieved
