What is Human-Centered about Human-Centered AI? A Map of the Research Landscape

Capel, Tara; Brereton, Margot

doi:10.1145/3544548.3580959

Cited by 36 publications

(4 citation statements)

References 296 publications

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“…Given the unknowable level of AI contribution to a piece of student work, Lodge, et al call for assessment to make learning processes less opaque and better evidenced; it is here that LA can assist in tracking learners' processes in privacy-respecting ways, such as in AI-augmented writing (Shibani et al, 2023) or teamwork (Echeverria et al, 2024;Schneider et al, 2021). We suggest that HCLA can navigate these dual opportunities and threats more effectively by deepening the dialogue with our sister communities in humancentred AI (Capel & Brereton, 2023;Shneiderman, 2022) and critical data/AI studies in education (Holmes & Porayska-Pomsta, 2022;Selwyn, 2019;Williamson & Eynon, 2020;Woolf, 2022).…”

Section: Hcl a 2025 -30?mentioning

confidence: 87%

Human‐Centred Learning Analytics: 2019–24

Buckingham Shum,

Martínez‐Maldonado,

Dimitriadis

et al. 2024

Brit J Educational Tech

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Human‐Centred Learning Analytics (HCLA) has emerged in the last 5 years as an active sub‐topic within Learning Analytics, drawing primarily on the theories and methods of Human‐Computer Interaction (HCI). HCLA researchers and practitioners are adopting and adapting HCI theories/methods to meet the challenge of meaningfully engaging educational stakeholders in the LA design process, evaluating systems in use and researching the sociotechnical factors influencing LA successes and failures. This editorial introduces the contributions of the papers in this special section, reflects more broadly on the field's emergence over the last five years, considers known gaps and indicates new opportunities that may open in the next five years.

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Section: Hcl a 2025 -30?mentioning

confidence: 87%

Human‐Centred Learning Analytics: 2019–24

Buckingham Shum,

Martínez‐Maldonado,

Dimitriadis

et al. 2024

Brit J Educational Tech

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“…This process entails progressing from general concepts to detailed elements, with iterative adjustments and refinements made along the way [41]. In an AI-assisted design approach, the goal is to minimize repetitive tasks while enhancing the designer's creativity, rather than replacing them [1,5]. To achieve this, the system needs to enhance its ability to interact with designers, accepting their guidance and actively suggesting possible solutions, thus completing a feedback loop in the creation process [40,41,54].…”

Section: Design Goalsmentioning

confidence: 99%

PlantoGraphy: Incorporating Iterative Design Process into Generative Artificial Intelligence for Landscape Rendering

Huang,

Lin,

Chen

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Landscape renderings are realistic images of landscape sites, allowing stakeholders to perceive better and evaluate design ideas. While recent advances in Generative Artificial Intelligence (GAI) enable automated generation of landscape renderings, the end-to-end methods are not compatible with common design processes, leading to insufficient alignment with design idealizations and limited cohesion of iterative landscape design. Informed by a formative study for comprehending design requirements, we present PlantoGraphy, an iterative design system that allows for interactive configuration of GAI models to accommodate human-centered design practice.A two-stage pipeline is incorporated: first, concretization module transforms conceptual ideas into concrete scene layouts with a domain-oriented large language model; and second, illustration module converts scene layouts into realistic landscape renderings using a fine-tuned low-rank adaptation diffusion model. PlantoGraphy has undergone a series of performance evaluations and user studies, demonstrating its effectiveness in landscape rendering generation and the high recognition of its interactive functionality. CCS Concepts: • Human-centered computing → Interactive systems and tools; Interaction design; • Computing methodologies → Artificial intelligence.

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“…Enabling effective contestation in ML systems like computer vision is an ongoing challenge (Alfrink et al, 2023; Capel & Brereton, 2023; Denton et al, 2020; Vaccaro et al, 2019; Yurrita et al, 2023). Perhaps a user wants to correct an error made on the way the system has classified them—this may work well in real‐time, where the user's immediate correction re‐annotates the image before it is databased and parsed.…”

Section: Technical and Ethical Challenges To Implementing Gender Dive...mentioning

confidence: 99%

Contesting efficacy: Tensions between risk and inclusion in computer vision technology

Scheuerman

2024

Future Humanities

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| INTRODUCTION AND BACKGROUNDMachine learning (ML) methods are now commonly used to make automated predictions about human beings-their lives and their characteristics. Vast amounts of individual data are aggregated to make predictions about people's shopping preferences, health status, or likelihood to recommit a crime. Computer vision, an ML task for training a computer to metaphorically 'see' specific objects, is a pertinent domain for examining the interaction between ML and human identity. Facial analysis (FA), a subset of computer vision trained to complete tasks like facial classification and facial recognition, is trained to read visual data to make classifications about innate human identities. Identities like age (Lin et al., 2006), gender (Khan et al., 2013), ethnicity (Lu & Jain, 2004) and even sexual orientation (Wang & Kosinski, 2017). Often, decisions about identity characteristics are made without explicit user input-or even user knowledge. Users, effectively, become 'targets' of the system, having no ability to contest these classifications. Surrounding these identity classifications are concerns about bias (e.g., Buolamwini & Gebru, 2018), representation (e.g., Hamidi et al., 2018;Keyes, 2018) and the embracing of pseudoscientific practices like physiognomy (e.g., Agüera y Arcas et al., 2017).In this short paper, I present several considerations for contestability for computer vision. By contestability, I refer to the agency that an individual has to contest the inputs and outputs of a computer vision system-including how one's data is collected, defined and used. I specifically focus on one identity trait for which to ground consideration: gender. Gender is a salient characteristic to consider given that criticisms of computer vision have stemmed from concerns of both sexism and cissexism, discrimination against transgender and nonbinary communities (Hibbs, 2014). Gender in computer vision has largely been presented as binary (i.e., male vs. female) and has been exclusive of genders beyond the cisgender norm (e.g., in automatic gender recognition (AGR) systems that classify gender explicitly [Hamidi et al., 2018;

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What is Human-Centered about Human-Centered AI? A Map of the Research Landscape

Cited by 36 publications

References 296 publications

Human‐Centred Learning Analytics: 2019–24

Human‐Centred Learning Analytics: 2019–24

PlantoGraphy: Incorporating Iterative Design Process into Generative Artificial Intelligence for Landscape Rendering

Contesting efficacy: Tensions between risk and inclusion in computer vision technology

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