ReEnTrust are recruiting for a research study about Algorithms and Explanations

How much do you trust the books or hotels that online platforms are recommending to you?

Recommender systems help users find information items more efficiently. They are widely used in our daily life, for example, helping us find books (Amazon), music (Spotify) or hotels. However, these systems are facing increasing scrutiny in recent years, due to biases in the recommendations, such as books by particular authors or hotels of specific price ranges.

As a result, users may be discriminated against and lose trust in recommendation results generated by computers. The working hypothesis is that users would establish a better belief of the recommender systems if they could have a better understanding of how specific recommendations are generated. However, little evidence has demonstrated that a better understanding of recommender algorithms can indeed lead to positive confidence or trust of these systems.

Therefore, the Algorithm Playground study in the ReEnTrust project aims to explore exactly that.

You are invited to participate in a short interview (~1 hour) in-person in our Oxford lab (Parks Road) and London (location TBC). During the study, you will first be asked to complete an online experiment in person and respond to a few short interview questions. You will be asked about how different explanation styles may help you understand the algorithms and how this may make you feel about these algorithms.

The study should last less than 60 minutes. No prior knowledge is required, as long as you are over 18 years old and are a regular user of the Internet.

The workshops will take place in the Department of Computer Science,39a St Giles, OX1 3LN, between 5th September and 4th October. The specific date and time can be agreed upon on an individual basis.

For more information, please download the information sheet or contact the following researchers:

This study has received Oxford University’s Research Ethics Approval: CS_C1A_19_039.

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.