How can recommender algorithms become explainable and transparent?

This is where eXplainable Artificial Intelligence (XAI) comes into play. XAI is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning models. In practice, this explainability refers to the ability of platforms to provide clear and understandable explanations to end users about why a particular recommendation, such as a product, news article, or video, was made.

There are several explainability techniques available, of which most are post-hoc explainers (used to generate explanations after the model was created and made a prediction). In addition, explanations can also take various forms, such as textual descriptions, visual representations, or even interactive interfaces.

An example of how an explainer can look to the end user is shown below.

In this mock online vacation booking platform, offerings are tailored to end users based on a recommender system. In order to meaningfully inform the user, for every given listing, explainability is used to share the most important variables in the ranking algorithm.

In the background, the explainability method determines what type of information has the largest weight, and returns that weight as output. That output is translated to a UI element, as shared in the visual above. As such, even if the platform chooses to only be transparent about the workings of the recommender systems through its static terms & conditions, the platform can still use the explainability to proof it complies with these terms & conditions. This is not only relevant towards the users who are presented with the recommender system, but also towards the businesses whose products or services are presented through the recommender system. Under the already applicable <span style=”font-weight: 400;”>Platform to Business Regulation</span>, platforms are required to describe, and adhere to, the ranking parameters as included in their terms & conditions towards these businesses.

By incorporating XAI methods, platforms can bridge the gap between complex algorithmic processes and user comprehension, helping users make informed decisions and maintain trust in the platform’s recommendations while ensuring compliance with regulations.

To understand the process of building explainers, it’s important to first differentiate between “local” and “global” explainability methods. Local explainers focus on providing insights into individual predictions, helping users understand why a specific outcome was produced. Global explainers, on the other hand, offer a broader perspective, shedding light on the overall behavior and trends of the model. This versatility ensures that explanations are tailored to different needs.

In general, we take the following steps to develop an explainer method:

1. Define the Objective and design the Explainer Output: Identify the specific explanations that would be valuable for end users without disclosing any information that might be seen as a trade secret or can be used for manipulation of search results. For instance, is the goal to clarify why a particular piece of content received a higher or lower ranking? Or identify the requirements to test the recommender system for proof to business owners whose products or services are ranked.
2. Choose an Explainability Method: Evaluate various explainability methods and frameworks such as SHAP, LIME, Anchors, Partial Dependency Plots (PDP), MACE, and many others. Depending on the objective, opt for a method that suits your needs. In this case, we may choose SHAP to determine the most significant attributes influencing content ranking.
3. Train or Configure the Explainability Method: While some methods require training on (a subset of) the same data as the AI model, others, like integrated gradients, are configurable and don’t require training. Deeploy supports data scientists with templates on a wide range of explainability methods. This way, training or configuring an explainer is made easier.
4. Deploy and Integrate the Explainer: Deploy the explainer as a micro-service, and integrate it with the backend of your application. This can be done using software like Deeploy, which supports easy deployment of lots of explainer methods. After deployment, utilize the explainer API endpoint in the application backend to request explainer output. Consider the orchestration strategy—whether to calculate explanations in real-time or in advance. Additionally, decide whether to provide explanations for every ranking or only upon user request, bearing in mind computational costs and application performance.

Implementing explainability methods helps to comply with the DSA and take responsibility with respect to your role in the fair online economy and society. Furthermore, by enabling a feedback loop, users can actively steer and improve ranking algorithms, increasing the ROI of such models, and correcting unwanted behaviour. It’s crucial to have a human in the loop and monitor the feedback loop to decide on the best possible way to incorporate feedback in your machine learning systems.