April 2019 – Human Robot Interaction

Advise for the young at heart

What advice would you give to your younger self?

Completing a PhD is a major accomplishment but it is only the start of an academic career. A study by The Royal Society found that only 3.5% of students that complete a PhD secure a permanent research position at a university. Of those lucky few, only 12% (or 0.45% of the total) make it to professor level. Those lucky few that make it must surely have some wisdom to share. I asked academics in HRI what advice they would give their younger selves. While talking to Rob Sparrow, Alan Wagner, Mari Velonaki, Guy Hoffman, Kimmo Vanni, Amit Kumar Pandey, and Franziska Kirstein about their careers certain patterns emerged. Perseverance, focus, patience and luck are amongst the factors that influenced their academic careers.

ISSN 2703-4054

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Art in Human-Robot Interaction

What and how does art contribute to HRI?

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Many disciplines contribute to the success of human-robot interaction. Computer scientists and psychologist are amongst the most frequent contributors, but their contributions are not necessarily the most interesting ones. Today we are going to discuss what and how art can contribute to human-robot interaction. Robots have been used in the theater, exhibitions, comics and music, to name just a few. Getting them into the limelight does take various skills. Team work is often a challenge, but collaborating across disciplines adds a layer of complexity to the dynamics. I talked with Mari Velonaki, Guy Hoffman, and David St-Onge about art and its relationship to HRI.

Transcript

The transcript of the episode is available as a PDF. You can also follow the episode with subtitles through Descript.

HRI-Podcast-Episode-006-Human-Robot-Interaction-Art-Transcript

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Robots and Racism

Why are robots subject to racial and gender bias?

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Racism is a major problem in our society and it is an issue from which the HRI community cannot shy away from. Several studies showed that people transfer their racial biases onto robots. In this episode we will talk about these difficult topics of racism and sexism. My guest are Kumar Yogeeswaran, Friederike Eyssel and Megan Strait. They all work on racism among humans and towards robots. Besides identifying the biases we also talk about how and if robots might be able to help reducing them.

Transcript

The transcript of the episode is available as a PDF. You can also follow the episode with subtitles through Descript.

HRI-Podcast-Episode-005-Robots-And-Racism-Transcript

ISSN 2703-4054

Relevant links

Social categorization of social robots: Anthropomorphism as a function of robot group membership by Friederike Eyssel and Dieta Kuchenbrandt
Robots Racialized in the Likeness of Marginalized Social Identities are Subject to Greater Dehumanization than those racialized as White by Megan Strait, Ana Sánchez Ramos, Virginia Contreras, Noemi Garcia
Robots and racism by Christoph Bartneck, Kumar Yogeeswaran, Qi Min Ser, Graeme Woodward, Robert Sparrow, Siheng Wang, Friederike Eyssel

Addendum

In an earlier version of this post the work of Megan Strait on racism among humans and towards robots was described as “extensive”. Upon Megan’s request the qualifier “extensive” was removed.

Megan Strait would like to also note that:

I do not endorse the idea of using robots to reduce bias, as I do not find the premise to respect existing understanding and literature. If racism were readily solvable via intergroup exposure, there would not be such movement on “AI ethics” as is readily apparent in the discourse of mainstream media. With respect to combatting racism, my perspective is that robots have potential value in the role they could play in moderating of social dynamics (see, for example, Campos, Martinho, & Paiva 2018, Hoffman, Zuckerman, Hirschberger, & Shani-Sherman 2015, and Martelaro, Jung, & Hinds 2015). Applications in this manner have particular potential to address social inequities (e.g., the placement of responsibility largely on people of color to combat manifestations of racism). But that does not specifically serve toward attenuating individual and institutional bias.