This workshop deals with methodological aspects of the investigation of diversity in human-robot interaction. Especially for application fields in public spaces, the target audience in human-robot interaction varies and robots meet people with a wide range of diversity features. In order to create a human-centered interaction, these features need to be taken into account, as a) robots must be able to interact with any user independent of their individual characteristics and b) it is desirable that the robot can adapt to specific needs in dependence of these individual characteristics. However, there are still various challenges in detecting (cf. algorithmic biases), interacting, and responding to various audiences that need to be addressed in future research considering ethical, technical, and methodological aspects. With this workshop, we like to discuss state-of-the-art challenges, possible solutions, and best practices.

The organizers will contribute short overview talks in the beginning to frame and inspire the discussion. We welcome prospective participants to submit extended abstracts (up to 4 pages). Manuscripts should conform to the RO-MAN 2023 guidelines. Submissions will be checked for alignment to the workshop’s themes and goals.
Topics of interest include but are not limited to the following questions:

How can people with different diversity features be recruited for studies without discrimination?
How to select and choose between diversity features?
How to detect technical/algorithmic biases?
What kind of specific ethical approaches are needed for this kind of research?
How to research human responses to algorithmic biases in an ethical way?
How can research ensure including diverse perspectives without focusing too much on the needs of a specific group?
How can different needs and biases be negotiated to reach the lowest common denominator in an interactive and technical sense?
What are cost-efficient and innovative ways of exploring functionality, attributes, and reception of robot characteristics (e.g. virtual reality labs, wizard of oz scenarios, etc.)?