Blackbox AI is an incredible AI model whose internal mechanisms are hidden from the users and developers. It is unclear how this model specifically operates. You can send input data and produce output but the logic behind processing those results isn’t accessible.
Stephanie Pulford said: “You can see what goes into it, you can see what comes out, but you can’t pop it open to see the inner workings,”
The Significance of Black Box AI:
Blackbox AI plays a significant role in the AI industry as it offers innovative decision-making capabilities. However, it has a few issues that are lack of transparency and interpretability. This has raised some serious concerns about reliability and fairness.
o Highly capable:
Blackbox AI is highly capable of performing numerous tasks from facial recognition for unlocking the phone, to powering the hiring algorithms for screening job candidates. It can analyze large amounts of complex or unstructured data such as natural language generation, and predictive analytics.
They let the AI voice assistants understand the user’s saying as in Google Assistant and Alexa. AI models like Chat GPT conduct a fluent conversation with their users. The systems can detect anomalies in medical diagnosis.
o Transparency concerns:
Along with the success and usage of Blackbox AI, more people eagerly want to know how its systems work. As, if a company is using AI to deny a loan or screen someone out. Then that person will demand the reason.
With the continuous examination of Black Box AI, a push has driven the research of explainable AI. It is a field that revolves around understanding how the model of Blackbox AI has driven the outputs to make its use more reliable, accurate, and trustworthy. However, to get a robust solution, we still need to wait for years. But many promising techniques have emerged so far.
Many researchers tinker with the inputs to observe the changes in decision-making or outputs. Some are trying to probe the model’s behavior by specialized algorithms.
How does the Blackbox AI work?
Blackbox AI model due to its enigmatic nature refers to the use of deep learning by which the model might be learning on its own independently and can improve without any programming.
In order to achieve that deep learning system will rely on neural networks, which copy the functionality of the human brain by a series of interconnected nodes as neurons in humans. When data is added to the model it is processed by layers of these neurons by performing complex calculations to identify the patterns of data. Normally, the steps identified in the model help a model to make decisions and predictions, but different patterns used to arrive at those outputs can be hard to decode.
You can understand it by a model that is trained by deep learning that has to identify images of a panda. However, it is unclear what checkpoints this model is checking to decide whether there is a panda or not. It may have trained on the physical features of the panda can be different black and white fur patterns or it may also focus on some less prominent characteristics that can bamboo tree to identify a panda. But, this type of wrong checkpoint may incorrectly classify the images of bamboo trees as panda, or hence it can fail to fulfill its task of identifying the panda.
Duncan Curtis, senior vice president of AI, product, and technology at AI company, Sama said; “We can’t always trace why a particular answer was derived,”. He added; “It could come from bias in the training dataset, it could come from things that aren’t necessarily obvious to a human.”
What are the advantages of Blackbox AI:
Black box AI models have some advantages that can prove very beneficial.
o Higher Accuracy:
The Blackbox AI model can process and analyze large volumes of complex data and make predictions and decisions with high accuracy. Hence Pulford believes blackbox AI works better than interpretable models.
Pulford said; “Some of our best algorithms require the computer to devise logic that’s not understandable by humans, but is still extremely effective — often more effective than we could get with a model we fully understood,”. He added to this; “The main incentive for using a black box model is that you often get good results fast.”
o Problem Solving:
Processing information by Blackbox AI is quite different from the way we humans do to make decisions. Instead, they focus on solving the problem by utilizing complex algorithms to identify connections and relationships. From a large set of data find novel solutions to problems that might have not been thought of before.
o Securing intellectual property:
Blackbox can significantly help developers protect their intellectual property by obscuring the details of their algorithm and training data. They make it harder for the competitors to replicate or reverse engineer their products.
Rao said; “If I’m a provider that’s sharing my models more ubiquitously, I want to be able to protect my secret sauce,” Rao said; “From that perspective, there may be a commercial advantage for me to have a more black box model.”
