12 research outputs found
The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements
Prior work has established the importance of integrating AI ethics topics
into computer and data sciences curricula. We provide evidence suggesting that
one of the critical objectives of AI Ethics education must be to raise
awareness of AI harms. While there are various sources to learn about such
harms, The AI Incident Database (AIID) is one of the few attempts at offering a
relatively comprehensive database indexing prior instances of harms or near
harms stemming from the deployment of AI technologies in the real world. This
study assesses the effectiveness of AIID as an educational tool to raise
awareness regarding the prevalence and severity of AI harms in socially
high-stakes domains. We present findings obtained through a classroom study
conducted at an R1 institution as part of a course focused on the societal and
ethical considerations around AI and ML. Our qualitative findings characterize
students' initial perceptions of core topics in AI ethics and their desire to
close the educational gap between their technical skills and their ability to
think systematically about ethical and societal aspects of their work. We find
that interacting with the database helps students better understand the
magnitude and severity of AI harms and instills in them a sense of urgency
around (a) designing functional and safe AI and (b) strengthening governance
and accountability mechanisms. Finally, we compile students' feedback about the
tool and our class activity into actionable recommendations for the database
development team and the broader community to improve awareness of AI harms in
AI ethics education.Comment: 37 pages, 11 figures; To appear in the proceedings of EAAMO 202
Soundify: Matching Sound Effects to Video
In the art of video editing, sound helps add character to an object and
immerse the viewer within a space. Through formative interviews with
professional editors (N=10), we found that the task of adding sounds to video
can be challenging. This paper presents Soundify, a system that assists editors
in matching sounds to video. Given a video, Soundify identifies matching
sounds, synchronizes the sounds to the video, and dynamically adjusts panning
and volume to create spatial audio. In a human evaluation study (N=889), we
show that Soundify is capable of matching sounds to video out-of-the-box for a
diverse range of audio categories. In a within-subjects expert study (N=12), we
demonstrate the usefulness of Soundify in helping video editors match sounds to
video with lighter workload, reduced task completion time, and improved
usability.Comment: Full paper in UIST 2023; Short paper in NeurIPS 2021 ML4CD Workshop;
Online demo: http://soundify.c
VideoMap: Video Editing in Latent Space
Video has become a dominant form of media. However, video editing interfaces
have remained largely unchanged over the past two decades. Such interfaces
typically consist of a grid-like asset management panel and a linear editing
timeline. When working with a large number of video clips, it can be difficult
to sort through them all and identify patterns within (e.g. opportunities for
smooth transitions and storytelling). In this work, we imagine a new paradigm
for video editing by mapping videos into a 2D latent space and building a
proof-of-concept interface.Comment: Accepted to NeurIPS 2022 Workshop on Machine Learning for Creativity
and Design. Website: https://chuanenlin.com/videoma
Videogenic: Video Highlights via Photogenic Moments
This paper investigates the challenge of extracting highlight moments from
videos. To perform this task, a system needs to understand what constitutes a
highlight for arbitrary video domains while at the same time being able to
scale across different domains. Our key insight is that photographs taken by
photographers tend to capture the most remarkable or photogenic moments of an
activity. Drawing on this insight, we present Videogenic, a system capable of
creating domain-specific highlight videos for a wide range of domains. In a
human evaluation study (N=50), we show that a high-quality photograph
collection combined with CLIP-based retrieval (which uses a neural network with
semantic knowledge of images) can serve as an excellent prior for finding video
highlights. In a within-subjects expert study (N=12), we demonstrate the
usefulness of Videogenic in helping video editors create highlight videos with
lighter workload, shorter task completion time, and better usability.Comment: Accepted to NeurIPS 2022 Workshop on Machine Learning for Creativity
and Design. Website: https://chuanenlin.com/videogeni
Pulling Back the Curtain on the Wizards of Oz
The Wizard of Oz method is an increasingly common practice in HCI and CSCW studies as part of iterative design processes for interactive systems. Instead of designing a fully-fledged system, the âtechnical workâ of key system components is completed by human operators yet presented to study participants as if computed by a machine. However, little is known about how Wizard of Oz studies are interactionally and collaboratively achieved in situ by researchers and participants. By adopting an ethnomethodological perspective, we analyse our use of the method in studies with a voice-controlled vacuum robot and two researchers present. We present data that reveals how such studies are organised and presented to participants and unpack the coordinated orchestration work that unfolds âbehind the scenesâ to complete the study. We examine how the researchers attend to participant requests and technical breakdowns, and discuss the performative, collaborative, and methodological nature of their work. We conclude by offering insights from our application of the approach to others in the HCI and CSCW communities for using the method
Team Learning as a Lens for Designing Human-AI Co-Creative Systems
Generative, ML-driven interactive systems have the potential to change how
people interact with computers in creative processes - turning tools into
co-creators. However, it is still unclear how we might achieve effective
human-AI collaboration in open-ended task domains. There are several known
challenges around communication in the interaction with ML-driven systems. An
overlooked aspect in the design of co-creative systems is how users can be
better supported in learning to collaborate with such systems. Here we reframe
human-AI collaboration as a learning problem: Inspired by research on team
learning, we hypothesize that similar learning strategies that apply to
human-human teams might also increase the collaboration effectiveness and
quality of humans working with co-creative generative systems. In this position
paper, we aim to promote team learning as a lens for designing more effective
co-creative human-AI collaboration and emphasize collaboration process quality
as a goal for co-creative systems. Furthermore, we outline a preliminary
schematic framework for embedding team learning support in co-creative AI
systems. We conclude by proposing a research agenda and posing open questions
for further study on supporting people in learning to collaborate with
generative AI systems.Comment: ACM CHI 2022 Workshop on Generative AI and HC
Setting the Stage with Metaphors for Interaction - Researching Methodological Approaches for Interaction Design of Autonomous Vehicles
Development of autonomous vehicles is progressing. As automation levels increase, the roles of both the driver and the vehicle are changing, meaning that they need to forge a new relationship to each other as the vehicle gains more agency. We believe this requires approaches that address that relationship early in the design process. One such approach is choosing a metaphor as a guiding principle for the interaction to set the preconditions for the relationship. Another approach is early evaluation of designs between system concept prototypes and the user. The aim of this one-day workshop is to explore the use of metaphors and evaluation though enactment in the design of human-vehicle interaction. This will be done through a short concept development process, where participants are asked to reflect on the process. Outcomes will be an evolved understanding of using the design approaches, as well as identified collaboration and research needs
A video-based automated driving simulator for automotive UI prototyping, UX and behaviour research
The lack of automated cars above SAE level 3 raises challenges for conducting User Experience Design (UXD) and behaviour research for automated driving. User-centred methods are critical to ensuring a human-friendly progress of vehicle automation. This work introduces the Immersive Video-based Automated Driving (IVAD) Simulator. It uses carefully recorded 180/360° videos that are played back in a driving simulator. This provides immersive driving experiences in visually realistic and familiar environments. This paper reports learnings from an iterative development of IVAD, and findings of two user studies: One simulator study (N=15) focused on the immersive experience; and one VR study (N=16) focused on rapid prototyping and the evaluation of Augmented Reality (AR) concepts. Overall, we found the method to be a useful, versatile and low budget UXD tool with a high level of immersion that is uniquely aided by the familiarity of the environment. IVAD's limitations and future improvements are discussed in relation to research applications within AutoUI