Workshop Goals.

Human-Centered Machine Learning (HCML) emerges in response to the rapid adoption of Machine Learning (ML), to provide the understandings and means to put ML at the service of people in a way that is accessible, useful, and trustworthy to all. This workshop’s goals are to continue past efforts in HCML and bring together a community of diverse practitioners and ideas to articulate an updated agenda for doing human-centered ML research.

In particular, the workshop will host sessions where participants will present position papers focusing on emerging ideas about experiences when people engage with ML in richer ways than merely being a source of labels, teaching to machines, intelligible systems, explainable decisions, and algorithmic fairness. For more details on these themes, see the Call for Papers section of this website.

After the paper sessions, panelists and audience will collaborate in participatory design activity to explore one or more ideas that emerged from the prior panels.

The workshop’s final event will consist of a town-hall-like discussion among the participants and the attendees. During this event, we will discuss the best ways to best distribute the outcome of the day’s activities. This includes expanding the day’s presentations into longer papers to be included into a special issue of a journal such as TOCHI, or online venues such as distill. In addition to considering dissemination plans, we will discuss how to best maintain and grow the HCML community. We would like to propose the formation of a Special Interest Group (SIG) on HCML, and the continuation of workshops in the area in venues focusing on HCI, Design, and ML themes.