Ever since generative AI became the talk of the town with ChatGPT, there has been a renewed and vigorous focus on AI adoption in general. In fact, studies estimate that the market for AI will surpass USD 1.8 trillion in value by 2030 globally. From finance to healthcare, top-level executives will agree that AI is already playing or will play a key role in their decision-making.
As AI becomes more deeply ingrained into critical thinking and decision-making frameworks across businesses, there are also rising concerns about trusting them. This comes primarily due to the ambiguity that exists in the approaches an AI model takes for making inferences and conclusions. In several sectors, there is apprehension from stakeholders on placing full trust in conclusions driven by AI because they were deduced without specifying what factors were considered for computations happening behind the scenes.
This is where both explainability and interpretability become critical elements in the AI value chain. Let us have a look at what they mean.
What is Interpretability in AI?
Interpretability refers to the ease of understanding that an AI system exhibits for its intricate computational model. In other words, if an AI model offers transparent insights into all parameters considered by it before making an outcome from a given data input, then the model is said to be interpretable.
Interpretability is needed to a large degree in critical areas like for example, an AI-powered credit rating system. Such a tool should let operators know the exact parameters that it evaluates before deeming an applicant as creditworthy or rejecting their application.
Building interpretable AI models is a great way to build trust for businesses. It guarantees the removal of bias from decision-making. Enterprises have more control over the accuracy of predictions and outcomes made by interpretable AI models. But on the downside, when the underlying computations are complex and heavy, it becomes extremely difficult and expensive to maintain an interpretable AI model.
What is Explainability in AI?
In simple terms, explainability refers to the ability of AI models to provide a fair human understanding of how they arrive at an outcome from a set of input data without exposing too much of its finer granular computational approaches, parameters considered, etc.
Let’s discuss with an example:
Consider a media house that publishes news in all categories in an online blog or video publishing platform. Imagine if they leverage an AI algorithm to automatically tag news categories in each content piece that is published. News arises about a popular football club investing in high-end software to track their on-field player movements and use it for training and performance improvement initiatives. The AI classifier could assign the tag of “technology” to this news even though it is in a sports category. However, the AI provides a reasonable explanation that says the news has a relevant mention of the software and its features.
This approach of explainability is akin to a black box system wherein there is a reasonable justification of how input data is turned into a prediction or an outcome.
The Explainability-Interpretability Trade-off in AI
Even for the most successful businesses that have mastered AI benefits, a key challenge that they often face is achieving a balance between the explainability and interpretability of their AI models. From the explanations given above, it is fair to assume that you would have understood the difficulties that AI engineers will face when they try to make models with higher interpretability. There is no doubt that from the perspective of trust, building more interpretable AI models is the best option. However, the associated complexity would render hard and complex scenarios to drift away from AI computations. Simpler calculations will be easier for engineers to use while designing interpretable models.
Building an explainable AI model is far easier for businesses as they will be less concerned about the internal nuances of the computational model. It will have a fair degree of transparency as any user of the end outcome or prediction will be able to understand why the outcome occurred.
How can Businesses Make a Call on the Degree to Which Interpretability is Necessary?
For businesses, getting a delicate balance between consumer trust and performance is essential to grow sustainably. Deciding on the degree to which interpretability is needed boils down to a couple of questions that they need to ask themselves like below:
- Are our datasets extremely complex and large like large audio-visual streams, layered data streams, etc.?
If yes, then going for an explainability-focused AI model will be beneficial as the interpretability route will be very expensive and time-consuming if you are a small or medium-sized business.
- Is interpretability a core consideration of customers using our AI service?
If yes, then you must emphasize on making your AI models as transparent as possible in their inner working approaches.
- Are explainable models doing justice to customers in allying their fears of biased results while providing them with services simultaneously?
If yes, then you can safely proceed with explainability as the core focus to begin with. Eventually, when AI tools and capabilities evolve, you can look at options to bring in better interpretability as well.
The Bottom Line
The explainability-interpretability trade-off will continue to be a hot topic of discussion as AI evolves and deepens its roots in mainstream businesses. Enterprise leaders need to follow a sustainable and proven roadmap in integrating the right AI approach in their line of business. This is where a knowledgeable partner like Wissen can be a great asset. Get in touch with us to know more.
