In a world of ever-increasing complexity that runs on the force of algorithms, AI is increasingly becoming a black box. But what is a black box exactly?
As more complex, algorithmic models are built, it is easier than ever to be more efficient and increase human comfort due to AI. While this is true, there is a gap in understanding the processes and workings that take place between the input and output in any given program or system. This gap is known as the ‘black box’.
What is a black box AI and why is it important to us?
We have grown up on a cultural diet of films that propagate visions of science-fiction, in the near and distant future. Those visions are idyllic and hopeful, but also dystopian and autocratic, for obvious dramatic purposes. While we have enjoyed watching Terminator and Total Recall as kids, as adults we are starting to see the practical lessons and insights that these films have left upon us. The core insight of such fiction is rooted in public trust and acceptance of machines.
AI has to be accepted and trusted. As the planet gets increasingly automated, Artificial Intelligence will penetrate itself into every facet of life. Therefore, it is important to see how AI arrives at its decisions, since the wrong decisions will inadvertently impact our lives negatively.
With the black box, one has to trust the algorithm blindly. We don’t see the process that is generated between input and output. This is due to artificial neural networks that are designed to replicate human neural networks.
These networks consist of hidden layers of nodes. The nodes pass information to each other in a preset form of learning and application. We can’t see how they learn, we only see the output. Understanding this gap between input and output becomes increasingly important in major human applications that involve the public at large like finance, military operations, drones, automation, automobiles and security operations.
What is Explainable AI Explainable AI solves these concerns by making us familiar with the logic behind the algorithm’s conclusion. If we understand the logic behind the AI’s decision making process, it is considered as an Explainable AI or XAI system. As AI has evolved from highly explainable frameworks such as Bayesian networks, linear regression and decision trees to replicate self-learning neural networks that get increasingly complex, we have traded accuracy and complexity for explainability. This makes it harder for businesses to explain how and why they arrived at their respective findings when proposing solutions to potential clients or presenting their cases in legal or ethical quandaries. Tracing the methodology of the AI’s decisions can be done either by preemptive design or retrospective analysis in the form of dynamically generated graphs or textual descriptions. Therefore, it makes sense to implement Explainable AI in modern businesses. It has many direct benefits.
Benefits of XAI
1. Generates clarity of design and a better framework With Explainable AI, also known as XAI or Interpretable AI, clarity is part of the design. By understanding the machine learning techniques that generate output, it is possible for human users to understand, appropriately trust, and manage AI. This makes it easier to iterate and make changes, thus reducing robot error and eliminating AI bias.
As AI gets more powerful, XAI Allows for checks and balances to keep it under control, just like we do for the government or other bureaucratic organizations.
2. Acts as insurance against potential legal issues
This also extends to insuring a business against potential legal ramifications. Suppose a self-driving car hits a pedestrian, general legal principles are still held to point and require conclusive evidence. Quite a few laws and regulations, as well as regulatory frameworks on a transcontinental level have different forms that require the right to explanation. These laws necessitate explainability by holding artificial intelligence systems responsible for their decisions.
3. Solves the problem of AI bias
AI puts out what humans put in. And since humans themselves have been notoriously discriminatory over the years, it is fairly easy to predict that AI models will also be reflecting the same biases in their logarithmic models. AI bias can come up in any facet of the automated industry across different stratas of society, like gender, nationality or racial profiling. These are legal and ethical obligations that industries of the future will have to adhere to. Since it keeps the system accountable to the public, it directly improves decision making and allows businesses to appropriately scale. As ethical businesses, working towards a just, fair and equitable society of the future is crucial. AI should seek to work towards helping us build that world, instead of repeating the collateral mistakes of the past.
4. Builds reliability and trust in automated business decisions The core issue is trust. In order to fully accept the output, a customer will have to trust the algorithm. That can not happen if one does not know how the machine arrived at the conclusion in the first place. XAI aims to solve this problem of trust. While machines are getting increasingly ever-important in society, it is important to understand that the core factors of human decision making like trust and mutual codependency aligned with transparency are very much still the need of the hour. In financial sector businesses, small decisions can impact an organization’s sales numbers. To understand why a certain outcome was generated, it is imperative that businesses seek to get a broader, more detailed view of the entire process to summarize and judge correctly. This is significant for businesses across financial and real-time sectors, since wrong decisions can cost firms dearly. It also makes good business sense, as being able to explain the methodology helps in making sure the customers’ curiosity is satisfied, building integrity and reliability, in turn. All in all, a dynamic feedback loop focused on constant improvement like XAI is a great asset for any future-focused business, across multiple avenues.