By Pouya Khani
In the rapidly evolving world of data science and database management, the need for transparency and comprehensibility has become paramount. With the advent of Explainable Query Answering (XQA), several tools and concept evolved, allowing us to not only retrieve information from databases but also understand the underlying factors that influence query results. In this blog post, we’ll delve into the concept of XQA, focusing on the innovative methods such as Shapley values that are employed to calculate the contribution of each tuple or predicate to the final query result.
What is Explainable Query Answering?
Explainable Query Answering is an advanced approach in database management systems that aims to make query results more interpretable. Traditional query systems provide results without any explanation of how they were derived. XQA addresses this gap by offering insights into which data points or predicates influenced the outcome, thus providing a clearer understanding of the data and fostering trust in the system. Imagine we have a database containing the grade information of all students in Denmark. We want to calculate the average grade of all Masters students in Denmark. So, we run an aggregate query, and the result is 13! We know that this is incorrect and that the average grade should be higher. But which data are responsible for this wrong output?
This is where we need an explanation for the query answering system to highlight the most important and effective tuples in the database that contributed to the final aggregate query result. By identifying these key pieces of data, we can understand and correct the anomalies or errors in our data processing.
The Need for Explainability
In many applications, particularly in critical areas such as healthcare, finance, and legal domains, understanding the “why” behind query results is as important as the results themselves. Explainability ensures:
- Transparency: Users can see the factors influencing the results.
- Accountability: It becomes easier to trace errors and biases.
- Trust: Users are more likely to trust and rely on systems that provide clear explanations.
Shapley Values: A Game Theory Approach
Application of Shapley value [1] from cooperative game theory, Named after Lloyd Shapley, provides a fair distribution of payoff among players (or data points, in our context). Here’s how it works:
- Shapley Values Calculation: In the context of XQA, each tuple or predicate is considered a player in a game where the query result is the payoff. The Shapley value for each player indicates their contribution to the final result. This is calculated by considering all possible permutations of players and determining the marginal contribution of each player.
- Fair Contribution: Shapley values ensure that the contribution of each data point is fairly evaluated, taking into account the presence of other data points.
Practical Example: Using Shapley Values
Let’s illustrate this with a simple example. Suppose we have a query result influenced by three tuples: A, B, and C. The Shapley value for each tuple is calculated by evaluating its contribution across all permutations of A, B, and C.
- Permutations: We consider all possible orderings of the tuples: ABC, ACB, BAC, BCA, CAB, and CBA.
- Marginal Contributions: For each ordering, we calculate how much each tuple contributes when added to the combination of the preceding tuples.
- Average Contribution: The Shapley value for each tuple is the average of its marginal contributions across all permutations.
By using Shapley values, we can quantify the exact influence of each tuple on the query result, providing a transparent and fair explanation.
The Future of Explainable Query Answering
The field of EQA is rapidly advancing, with new tools and techniques continuously emerging. The integration of machine learning, statistical methods, and advanced analytical frameworks is paving the way for more sophisticated and comprehensive explanations. As data becomes increasingly complex and integral to decision-making, the demand for explainable systems will only grow.
By adopting XQA tools and methodologies, organizations can enhance their data transparency, improve decision-making processes, and build greater trust with stakeholders. Whether you’re a data scientist, a database administrator, or a curious enthusiast, understanding XQA and its methodologies opens up a new dimension of clarity and reliability in the realm of data management.
In summary, Explainable Query Answering represents a significant step forward in making database systems more transparent and trustworthy. Methods such as Shapley value, enable us to delve deep into the reasons behind query results. As we continue to advance in this field, the ability to explain and understand data queries will not only enhance the user experience but also drive better decision-making in critical applications.