According to Gartner, enterprises leveraging AI systems will add an additional business value of over USD 2.9 Trillion by 2021. Today, AI is no longer an experimental prototype that companies show off to the world during tech events, but a mainstream component of customer experience. From smart assistants on mobile phones like Siri, Google Now, and Alexa to intelligent weather prediction and clinical decision support, artificial intelligence and machine learning has found their way into mainstream use cases across nearly every sector. Combined with other emerging technologies and existing innovations, AI is unlocking new possibilities through autonomous decision making, faster computing, and better integration.
So how can AI systems be truly bias-free?
The solution lies in making machines produce results that have a transparent explanation of how the decision was made and the reasons why it chose to leverage a particular logic to process data for the result. This principle is gaining popularity today as Explainable AI or XAI.
Related article on How to tackle biases in Artificial Intelligence
Explainable AI or XAI is a collection of procedures and strategies that enable human users to grasp and trust the results and output generated by machine learning algorithms.
As humans, we tend to rely more on machines and data today, thanks to the growth of AI. But then comes the question: Are AI systems deducing the right insight or decision from data always?
AI systems engage in continuous learning from datasets and gradually make their own inference about data behavior and patterns. Although eliminating bias in decision-making is considered a key feature of AI systems, they are only as unbiased as the data that is fed to them to learn. They take in data and process them with rules defined by human agents. Over time they begin to arrive at decisions by evolving their rule-based learning. However, in most cases, there is little visibility on how the machine arrived at a particular decision and the logic applied to arrive at the same. This creates a question of trust, especially when AI systems are increasingly being used in critical scenarios like healthcare decision support and autonomous driving.
With Explainable AI, the question of trusting AI systems gets a clear answer. It provides an understanding of how black-box decisions are made by AI which improves the confidence levels for trusting their decisions for real-life applications. One of the best ways to achieve explainability in AI systems is to have them leverage models for data processing. These models are either inherently explainable or they are allowed to perform tasks and impose decision logic by adhering to a model that can create a transparent explanation of why a particular decision was made. According to the US Defense Advanced Research Project Agency (DARPA), the 3 traits that XAI needs to have are:
It is important for organizations to clearly understand how different technology systems work together or individually to enable customer experiences. AI systems are no exception. With XAI, businesses can impose model monitoring and accountability on AI software deployed across different channels and ensure that they are in alignment with the organization’s larger focus on data and computing transparency. Let us examine some key benefits that an organization can enjoy while leveraging Explainable AI:
Government regulations on using citizen data often pose huge challenges for digital channels that businesses operate. Fines and penalties can quickly run into millions of dollars and can even put them on the brink of bankruptcy for failure to comply. Having AI systems that mask the logic on how data is used, which logic was applied, and no explanation of how a particular decision was arrived at, can be a recipe for disaster in this regard. With explainable AI, businesses can transparently adhere to data privacy and regulatory frameworks by having AI models that can provide explanations on how data is used when demanded by compliance authorities.
As XAI offers an explainable code of conduct for their operational model, it is easier for organizations to eliminate biased decision-making by ensuring that only the right weightage is given for different data points that are studied by the AI system to arrive at a decision. This improves the accountability factor for AI systems thereby creating more confidence for businesses to use them in mainstream applications.
Related article on How to mitigate AI bias in healthcare applications.
XAI brings trust and confidence to AI models that businesses leverage and hence allows it to be offered for direct market consumption faster than usual. In the traditional AI mainstream application scenario, businesses must undertake years of experimentation and classification of results from AI systems before certifying that the system is fit for consumer exposure. XAI is a more accountable version of AI and hence can guarantee that unexpected surprises will not pop up while in mainstream deployment.
Related article on How to implement Artificial Intelligence in your business.
AI systems deployed in critical fields such as medicine, law enforcement, and finance have extremely limited scope for prediction errors. A minor flaw in the result can mean havoc. With XAI, organizations operating in these domains have more visibility on the model that the system uses, and it also offers traceability for the inference model. It helps to identify the root cause of issues if any and prevent them from entering production for critical decision making.
Explainable AI is a primary source of ensuring an ethical progression of AI capabilities within an organization. Having a model code that exuberates trust and transparently maintains operational ethics will be ideal in integrating new use cases for mainstream applications. Get in touch with us to see how your business can leverage XAI for improved success.
Reach out to us today and get started!