Kappa is one of the research lines in the Web Information Systems group at Delft University of Technology. In Kappa, we investigate Crowd Computing and Human-Centered AI: two core areas which are instrumental in developing the next generation of data-driven AI systems. Encompassing Human-in-the-loop computing, Human-AI interaction, and User Modeling and Explainability, these areas consider
- AI by humans: the computational role of humans for AI systems
- AI for humans: and the interactional role of humans with AI systems
Papers presented at HCOMP 2022
- Goal-Setting Behavior of Workers on Crowdsourcing Platforms: An Exploratory Study on MTurk and Prolific
- CHIME: Causal Human-in-the-Loop Model Explanations
- Gesticulate for Health’s Sake! Understanding the Use of Gestures as an Input Modality for Microtask Crowdsourcing
- It Is like Finding a Polar Bear in the Savannah! Concept-Level AI Explanations with Analogical Inference from Commonsense Knowledge
- SignUpCrowd: Using Sign-Language as an Input Modality for Microtask Crowdsourcing
Goal-Setting Behavior of Workers on Crowdsourcing Platforms: An Exploratory Study on MTurk and Prolific
Authors: Tahir Abbas and Ujwal Gadiraju
Synopsis
A wealth of evidence across several domains indicates that goal setting improves performance and learning by enabling individuals to commit their thoughts and actions to goal achievement. Recently, researchers have begun studying the effects of goal setting in paid crowdsourcing to improve the quality and quantity of contributions, increase learning gains, and hold participants accountable for contributing more effectively. However, there is a lack of research addressing crowd workers' goal-setting practices, how they are currently pursuing them, and the challenges that they face. This information is essential for researchers and developers to create tools that assist crowd workers in pursuing their goals more effectively, thereby improving the quality of their contributions. This paper addresses these gaps by conducting mixed-method research in which we surveyed 205 workers from two crowdsourcing platforms -- Amazon Mechanical Turk (MTurk) and Prolific -- about their goal-setting practices. Through a 14-item survey, we asked workers regarding the types of goals they create, their goal achievement strategies, potential barriers that impede goal attainment, and their use of software tools for effective goal management. We discovered that (a) workers actively create intrinsic and extrinsic goals; (b) use a combination of tools for goal management; (c) medical issues and a busy lifestyle are some obstacles to their goal achievement; and (d) we gathered novel features for future goal management tools. Our findings shed light on the broader implications of developing goal management tools to improve workers' well-being.
Authors: Shreyan Biswas, Lorenzo Corti, Stefan Buijsman, and Jie Yang
Synopsis
Explaining the behaviour of Artificial Intelligence models has become a necessity. Their opaqueness and fragility are not tolerable in high-stakes domains especially. Although considerable progress is being made in the field of Explainable Artificial Intelligence, scholars have demonstrated limits and flaws of existing approaches: explanations requiring further interpretation, non-standardised explanatory format, and overall fragility. In light of this fragmentation, we turn to the field of philosophy of science to understand what constitutes a good explanation, that is, a generalisation that covers both the actual outcome and, possibly multiple, counterfactual outcomes. In this work led by Shreyan, we propose CHIME: a human-in-the-loop, post-hoc approach focused on creating such explanations by establishing the causal features in the input. We first elicit people's cognitive abilities to understand what parts of the input the model might be attending to. Then, through Causal Discovery we uncover the underlying causal graph relating the different concepts. Finally, with such a structure, we compute the causal effects different concepts have towards a model's outcome. We evaluate the Fidelity, Coherence, and Accuracy of the explanations obtained with CHIME with respect to two state-of-the-art Computer Vision models trained on real-world image data sets. We found evidence that the explanations reflect the causal concepts tied to a model's prediction, both in terms of causal strength and accuracy. We think exploring the intersection between Explainable AI and Causal Inference is beneficial to build better explanation methods.
Gesticulate for Health’s Sake! Understanding the Use of Gestures as an Input Modality for Microtask Crowdsourcing
Authors: Garrett Allen, Andrea Hu, and Ujwal Gadiraju
Synopsis
Human input is pivotal in building reliable and robust artificial intelligence systems. By providing a means to gather diverse, high-quality, representative, and cost-effective human input on demand, microtask crowdsourcing marketplaces have thrived. Despite the unmistakable benefits available from online crowd work, the lack of health provisions and safeguards, along with existing work practices threatens the sustainability of this paradigm. Prior work has investigated worker engagement and mental health, yet no such investigations into the effects of crowd work on the physical health of workers have been undertaken. Crowd workers complete their work in various sub-optimal work environments, often using a conventional input modality of a mouse and keyboard. The repetitive nature of microtask crowdsourcing can lead to stress-related injuries, such as the well-documented carpal tunnel syndrome. It is known that stretching exercises can help reduce injuries and discomfort in office workers. Gestures, the act of using the body intentionally to affect the behavior of an intelligent system, can serve as both stretches and an alternative form of input for microtasks. To better understand the usefulness of the dual-purpose input modality of ergonomically-informed gestures across different crowdsourced microtasks, we carried out a controlled 2 x 3 between-subjects study (N=294). Considering the potential benefits of gestures as an input modality, our results suggest a real trade-off between worker accuracy in exchange for potential short to long-term health benefits.
It Is like Finding a Polar Bear in the Savannah! Concept-Level AI Explanations with Analogical Inference from Commonsense Knowledge
Authors: Gaole He, Agathe Balayn, Stefan Buijsman, Jie Yang, and Ujwal Gadiraju
Synopsis
With recent advances in explainable artificial intelligence (XAI), researchers have started to pay attention to conceptlevel explanations, which explain model predictions with a high level of abstraction. However, such explanations may be difficult to digest for laypeople due to the potential knowledge gap and the concomitant cognitive load. Inspired by recent work, we argue that analogy-based explanations composed of commonsense knowledge may be a potential solution to tackle this issue. In this paper, we propose analogical inference as a bridge to help end-users leverage their commonsense knowledge to better understand the concept-level explanations. Specifically, we design an effective analogy-based explanation generation method and collect 600 analogy-based explanations from 100 crowd workers. Furthermore, we propose a set of structured dimensions for the qualitative assessment of analogy-based explanations and conduct an empirical evaluation of the generated analogies with experts. Our findings reveal significant positive correlations between the qualitative dimensions of analogies and the perceived helpfulness of analogy-based explanations. These insights can inform the design of future methods for the generation of effective analogy-based explanations. We also find that the understanding of commonsense explanations varies with the experience of the recipient user, which points out the need for further work on personalization when leveraging commonsense explanations.
Authors: Aayush Singh, Sebastian Wehkamp, and Ujwal Gadiraju
Synopsis
Different input modalities have been proposed and employed in technological landscapes like microtask crowdsourcing. However, sign language remains an input modality that has received little attention. Despite the fact that thousands of people around the world primarily use sign language, very little has been done to include them in such technological landscapes. We aim to address this gap and take a step towards the inclusion of deaf and mute people in microtask crowdsourcing. We first identify various microtasks which can be adapted to use sign language as input, while elucidating the challenges it introduces. We built a system called ‘SignUpCrowd’ that can be used to support sign language input for microtask crowdsourcing. We carried out a between-subjects study (N=240) to understand the effectiveness of sign language as an input modality for microtask crowdsourcing in comparison to prevalent textual and click input modalities. We explored this through the lens of visual question answering and sentiment analysis tasks by recruiting workers from the Prolific crowdsourcing platform. Our results indicate that sign language as an input modality in microtask crowdsourcing is comparable to the prevalent standards of using text and click input. Although people with no knowledge of sign language found it difficult to use, this input modality has the potential to broaden participation in crowd work. We highlight evidence suggesting the scope for sign language as a viable input type for microtask crowdsourcing. Our findings pave the way for further research to introduce sign language in real-world applications and create an inclusive technological landscape that more people can benefit from.