The AI black box problem algorithm – when transparency was needed, scary stories & more

The AI black box issue makes this barrier even more difficult. Artificial intelligence doesn't reveal how it functions. It doesn't state clearly how or why it came to its conclusions. We only know that some all-knowing algorithm has spoken. A black box, in a general sense, is an impermeable system.

Black box algorithm development is frequently used for deep learning modeling: The algorithm correlates particular data properties from millions of input data points to generate an output. It is typically challenging for data scientists, programmers, and consumers to interpret that process because it is mainly self-directed. One of the biggest hurdles that artificial intelligence faces today is a public acceptance that it will always be hesitant about trusting the technology until AI programmers can get rid of these layers of obscurity. People often struggle to trust the decisions. Here, we describe the AI black box, its causes, and the reason why it's a problem.

What is AI Black Box?

The deep learning model typically employs "black box" development: To produce an output, the algorithm correlates specific data attributes from millions of input data points. Since that process is primarily self-directed, it might be difficult for data scientists, programmers, and users to interpret it.

Why AI Black Box Exists?

What then is the root of the AI black box issue? Deep learning and/or artificial neural network-based techniques are the ones that are most frequently affected by the black box problem.

A buried layer of nodes makes up artificial neural networks. Each of these nodes processes the input and sends its output to the layer of nodes below it. A massive artificial neural network with many of these hidden layers, deep learning "learns" on its own by identifying patterns. And the complexity of this might be endless. What the nodes have "learned" is hidden from view. Only the conclusion is shown; we don't see the output between levels. We are therefore faced with the AI black box since we are unable to see how the nodes are analyzing the data.

The AI Black Box Problem

We are aware of both the nature and root causes of the AI black box. However, why is it a concern?

The consequences of AI's actions get worse as its capability permeates more of our tools. Police, medics, and banks are being informed by AI functions. They have an impact on whether you receive that loan or whether you require X treatment. After a facial recognition AI identifies you as a criminal, you might even find the cops showing up at your house to question you.

Ignoring the AI black box dilemma raises ethical questions because of its potential impact. AI can make blunders just like humans, after all. There is no moral code attached to AI technology. In the same way that a human does, it does not "understand" the output it produces. An AI won't recognize a biased outcome when it is produced. Humans must therefore do so instead, which is challenging given that we cannot comprehend the logic underlying the outcome.

What makes us think that an AI decision is the better one, then? Accepting AI is tough without this trust. People won't feel comfortable using it unless its internal workings are clear.

Solving the AI Black Box Problem

AI developers are increasingly focusing on finding a solution as the AI black box problem grows in importance. Explainable AI holds the key to the solution. As the term implies, explainable AI refers to AI tools that yield outcomes that a human can comprehend and explain.

However, until such functionality is made available, the black box issue gives us a cause to be wary of AI. AI-powered conclusions should serve as recommendations rather than final judgments.

Here are a couple of AI Black Box Horror Stories

Recruiting:

In one well-known instance, Amazon created an AI hiring tool that looked back and examined ten years' worth of job applications to establish a system that automatically detected traits of high-performing employees and assessed fresh prospects against those standards. When it was discovered that the algorithm favored male candidates as a result of cultural pressures like gender bias and wage discrepancies in technology positions, the tool made news in 2018.

Patient Diagnosis:

The Deep Patient software proved to be a bit of a mystery. For instance, it proved remarkably adept in predicting the onset of psychiatric diseases like schizophrenia. But it was only logical to question how this was possible given how difficult it is for doctors to anticipate schizophrenia. Sadly, the new tool did not indicate how it accomplishes this. To convince doctors that a technological solution like Deep Patient is correct and to support any changes in the prescription medications a patient is taking, it truly needs to offer a level of transparency by justifying its predictions.