In a nutshell:
- Model explainability is crucial for understanding how AI works and how decisions are made.
- Explainable AI benefits include better accuracy, enhanced QA measures, and discouraging bias.
- It supports data governance, reduces risk, and cultivates trust with customers.
- Pecan's Predictive GenAI platform offers detailed explanations for model transparency.
Imagine a predictive model that catches a pre-cancerous lump before it becomes life-threatening. Or an AI-powered financial review system that helps you qualify for an otherwise unattainable loan thanks to its ability to consider multiple criteria beyond the standard application process.
That’s the power of AI.
But while AI's insights and predictions can be incredibly powerful and helpful, they can also bring scrutiny and damaging consequences when people don’t understand how they work. As AI increasingly intersects with our personal lives, people want — and deserve — to know how AI came up with its answers and conclusions.
The solution is explainable AI, the purposeful engineering of transparent and interpretable machine learning models with increasing benefits for a society inundated with AI. In this blog, we explore explainable AI and its importance for organizations.
What is model explainability?
When AI is explainable, developers and end users understand how it works and how its models made certain decisions and came to specific conclusions. It’s the opposite of being “opaque,” or when models perform tasks and create outputs, but users aren’t sure how they arrived at certain conclusions.
Explainable AI (XAI) has been a goal for data engineers and professionals for some time, especially as algorithms become more advanced and it becomes more difficult to retrace AI’s steps. Interest has also grown among the general population recently (even if they are unfamiliar with the term XAI due to media coverage around AI ethics, bias, and trust).
Not all AI is explainable due to its complexity or how it was built. For example, the neural networks used in powerful AI applications are built layer upon layer, and these parts cannot be easily separated to be studied or reverse-engineered. Another example is highly complex applications, such as genome sequencing and algorithms, where the data volume and features make it difficult to see how decisions came about.
However, the practice of model explainability can and should be pursued wherever applicable and possible because it offers numerous advantages to both the company building the AI system and the customers it serves.
Model explainability in business applications
Any business can benefit from explainable AI models, as without them, there can’t be trust in a model’s outputs. If you used a “black box” model, one where you don’t know how an AI or predictive model worked, it would be like running your business with a highly accurate Magic 8 Ball. It might get some things right, but you wouldn’t know why. And when it got questions wrong, the results could be catastrophic.
Additionally, understanding the features that are most important in a model can inspire better questions and more effective business insights. Two ways greater transparency happens are through global and local explainability.
- Global explainability is using AI modeling for larger business strategies, such as customer churn across your entire e-commerce business. Explainable AI models let you see the factors most likely to predict churn, such as the price of your product or personalized recommendations on the checkout page.
- Local explainability looks at the reasons for a single outcome, such as trying to keep an individual customer in Florida from churning. This more granular approach looks at what factors are likely to cause them to leave and alerts you before they leave. Then, you can take action for this customer and others like them before it’s too late.
Because you understand how the model works, you can drill down to individual factors and apply them broadly or narrowly according to your needs. However, here’s the big caveat: Explainable AI only applies if you have quality data.
Even the best ML models won’t make good predictions if the data is flawed, outdated, error-filled, or corrupt. Spending extra time on data QA — before, during, and after the modeling process — is time well spent to ensure your models perform well!
8 benefits of explainable AI
More transparent AI leads to data professionals and business users understanding how an AI model came to its conclusions and, therefore, builds trust in its outputs. This can lead to the following advantages:
Explainable AI Benefit #1: Provides better model accuracy
When you know how a model makes decisions, you can better connect features and the inputs needed to impact predictions. This helps you fine-tune the model to be more current at even the most granular levels, which is needed for specialized fields.
Explainable AI Benefit #2: Enhances QA measures
Imagine trying to fix a car that won’t start while not knowing how the electricity gets to the starter or what happens to the gas after it’s put into the tank. This is the dilemma faced by quality assurance teams when they have to debug AI models of the “black box” variety (those that aren’t explainable.) They can’t possibly begin to fix an algorithm with no knowledge of how it works or even quite what it does.
On the other hand, explainable AI helps data professionals draw clearer lines between input and output so that when something goes wrong, they can trace their steps to fix the model and monitor outputs to ensure the model keeps performing as planned. This is especially useful for targeting model drift and degradation, such as when a models change after multiple uses and produce different outcomes than the initial training data suggested.
Explainable AI Benefit #3: Discourages bias
It’s shocking to see when GenAI art tools go awry with bias, whether by displaying certain ethnicities negatively, taking stereotypes to extremes, or not including marginalized groups at all. Rather than eliminating these tools or waiting for AI to “catch up” with practical use expectations, developers can dig deeper into how these issues came about using explainable AI.
With a full look at the decisions made by AI to get to these false assumptions, engineers can see if it’s an algorithmic problem or one of training data. Both issues must be addressed to create bias-free and truly useful AI results, but without explainability, they won’t know where to start.
Explainable AI Benefit #4: Encourages human interaction
People tend to ignore issues they don’t understand, and AI is no exception. However, with a better understanding of how AI works, engineers and data professionals can feel empowered to step in more frequently to provide corrective feedback. This human-in-the-loop (HITL) approach helps solve many other issues, including accuracy and bias. People will be likelier to say something when they see it because they know the problem is solvable. It also presents new input for the algorithm to learn from, as humans come with their own unique approach to how they perceive and process information.
HITL may also be the only way to handle certain AI tasks, as regulations and laws around certain data uses require at least some human discernment for more nuanced tasks.
Explainable AI Benefit #5: Supports data governance and compliance
Data governance frameworks create accountability for your data collection, storage, and processing workflows, including those that use AI. With explainable AI at the center of your data governance metrics and plan, you can ensure that each use of AI meets your internal standards and also complies with regulatory rules, such as those from the EU’s General Data Protection Regulation (GDPR). If asked how your organization follows the rules, you can easily demonstrate it since you’ll have an in-depth understanding of your AI applications and data usage and how they comply with guidelines.
Explainable AI Benefit #6: Reduces risk
As Amazon discovered when an AI hiring model it created for engineer roles accidentally favored men over women, unchecked AI comes with big risks. Whether it’s the threat of a lawsuit or the fines for breaking privacy laws, most companies can’t afford to let AI go off the rails. XAI offers insight into how unwanted outcomes came to be and may be the best way to mitigate harm in a world of increasing legal data woes. Congressional oversight will likely increase, as well, so AI developers should be prepared to show their work when defending their AI models.
Explainable AI Benefit #7: Advances in learning and development
We can learn much from taking things apart. XAI offers researchers and data scientists new ideas for better models since it gives them the blueprint for what’s working for them to build upon. It’s highly collaborative, too, as the explanations serve even those without a formal data science background; marketers can see how a model is built and gain insights into the kind of new data they should collect or future features they should request.
Explainable AI also makes AI more accessible by breaking down its mystery. People who see these ML models as decodable may become more interested in AI. This can encourage AI literacy and bring more diversity to the community.
Explainable AI Benefit #8: Cultivates trust
Finally, with so many conversations around whether AI is good or bad, companies must be good stewards of AI and what it does with customer data. It’s nearly impossible to look consumers in the eye and tell them you are being ethical with AI without understanding what it does or how it works.
It’s one of the better ways to stand behind mission statements of ethical responsibility. You’ll not only be able to know that data models are aligned with your values, but you can honestly answer when asked about how you’re using AI. Talking honestly about this builds trust with the public and can even make you a leader in a world where business leaders don’t often know where their AI models come from.
Additionally, as we mentioned before, you can’t trust insights from a model you don’t understand. Using technology with high levels of explainability helps you know you’re making informed decisions.
How Pecan handles model explainability
A low-code Predictive GenAI platform like Pecan does more than just save time and help data professionals build models quickly. It also shows the thinking behind each model for enhanced explainability.
Pecan offers detailed, granular documentation of how each model works. These explanations via Pecan’s user-friendly dashboards help your team — and even those without any data science background — quickly understand how different variables (features) contribute to the model's predictions. With scores for important metrics, you can see how important each factor is in the prediction model and adjust accordingly. These actionable predictive insights can inspire new business actions, building new models, or finding new data sources for even more powerful, accurate predictions.
With Pecan, building machine-learning models becomes as easy as typing. You just need to ask the right questions and supply the data.
Contact us for a free demo of our predictive platform with explainable AI built in so you can guide your business forward with trusted predictive insights.
