Query-by-Browsing (QbB) now includes local explanations so that you can explore in detail how the AI generated query relates to dataset items.
- Play with the latest web version of Query-by-Browsing
- See the QbB labs page for more about its story and how to use it
Query-by-Browsing is the system envisaged in my 1992 paper that first explored the dangers of social, ethnic and gender bias in machine-learning algorithms. QbB generates queries, in SQL or decision tree form based on examples of records that the user does or does not want. A core feature has always been the dual intensional (query) and extensional (selected data) to aid transparency.
QbB has gone through various iterations and a simple web version has been available for twenty years, and was updated last year to allow you to use your own data (uploaded as CSV files) as well as the demo datasets.
The latest iteration also includes a form of local explanation. If you hover over a row in the data table it shows which branch of the query meant that the row was either selected or not.
Similarly hovering over the query shows you which data rows were selected by the query branch. 
However, this is not the end of the story!
In about two weeks Tommaso will be presenting our paper “Talking Back: human input and explanations to interactive AI systems” at the Workshop on Adaptive eXplainable AI (AXAI) at IUI 2025 in Cagliari, Italy, A new version of QbB will be released to coincide with this. This will include ‘user explanations’, allowing the user to tell the system why certain records are important to help the machine learning make better decisions.
Watch this space …