Respecting Human Autonomy through Human-Centered AI

Sankaran, Supraja; Zhang, Chao; Lopez, Marisela Gutierrez; Väänänen-Vainio-Mattila, Kaisa

doi:10.1145/3419249.3420098

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Diverse AI-based applications are now embedded in our daily lives from domotics such as smart thermostats and robotic vacuum cleaners that simplify chores, entertainment, and shopping platforms that guide our choices, navigation systems to maneuver through traffic optimally, social media apps that have transformed communication methods, and typing assistants that are enabling us to write emails quickly or fill out our resumes automatically. As these applications are gradually supplanting human-decision making, streamlining choices, and modifying communication methods, people gradually begin to perceive a threat to their autonomy (André et al, 2018 ; Sankaran et al, 2020a , b ; Pizzi et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Exploring Peoples' Perception of Autonomy and Reactance in Everyday AI Interactions

et al. 2021

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Applications using Artificial Intelligence (AI) have become commonplace and embedded in our daily lives. Much of our communication has transitioned from human–human interaction to human–technology or technology-mediated interaction. As technology is handed over control and streamlines choices and decision-making in different contexts, people are increasingly concerned about a potential threat to their autonomy. In this paper, we explore autonomy perception when interacting with AI-based applications in everyday contexts using a design fiction-based survey with 328 participants. We probed if providing users with explanations on “why” an application made certain choices or decisions influenced their perception of autonomy or reactance regarding the interaction with the applications. We also looked at changes in perception when users are aware of AI's presence in an application. In the social media context, we found that people perceived a greater reactance and lower sense of autonomy perhaps owing to the personal and identity-sensitive nature of the application context. Providing explanations on “why” in the navigation context, contributed to enhancing their autonomy perception, and reducing reactance since it influenced the users' subsequent actions based on the recommendation. We discuss our findings and the implications it has for the future development of everyday AI applications that respect human autonomy.

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Section: Introductionmentioning

confidence: 99%

Exploring Peoples' Perception of Autonomy and Reactance in Everyday AI Interactions

et al. 2021

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“…Our work is situated within the emerging area of human-centered AI, which focuses on understanding how AI technologies can augment and enhance human performance and promote human agency [27,74,79,[86][87][88]103]. Many studies have examined the collaborative relationship between people and AI systems when working on tasks such as decision making or artifact creation.…”

Section: Related Workmentioning

confidence: 99%

Better Together? An Evaluation of AI-Supported Code Translation

Weisz¹,

Müller²,

Ross³

et al. 2022

27th International Conference on Intelligent User Interfaces

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Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from code, and auto-completing methods. Yet, state-of-the-art models often produce code that is erroneous or incomplete. In a controlled study with 32 software engineers, we examined whether such imperfect outputs are helpful in the context of Java-to-Python code translation. When aided by the outputs of a code translation model, participants produced code with fewer errors than when working alone. We also examined how the quality and quantity of AI translations affected the work process and quality of outcomes, and observed that providing multiple translations had a larger impact on the translation process than varying the quality of provided translations. Our results tell a complex, nuanced story about the benefits of generative code models and the challenges software engineers face when working with their outputs. Our work motivates the need for intelligent user interfaces that help software engineers effectively work with generative code models in order to understand and evaluate their outputs and achieve superior outcomes to working alone.CCS Concepts: • Human-centered computing → HCI theory, concepts and models; • Software and its engineering → Designing software; • Computing methodologies → Generative and developmental approaches.

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“…Although AI can increase the feasibility of developing truly personalized systems, there are ethical issues that developers grapple with. These include, but are not limited to, transparency (Yu and Alì, 2019 ), privacy (Chowdhary, 2020 ), safety and performance (Behera et al, 2022 ), accessibility to artificial intelligence (Morris, 2020 ), moral agency (Swanepoel, 2021 ), biases stemming from data collection process, data, and algorithm (Li et al, 2019 ), threats to human autonomy (Sankaran et al, 2020 ), environmental and ecological challenges arising from hardware and energy consumptions required to develop and operate AI technologies (Li et al, 2019 ), liability issues in event of a damage or harm (Cerka et al, 2015 ), and social acceptability (Morris, 2020 ).…”

Section: Role Of Artificial Intelligencementioning

confidence: 99%

Mitigating Issues With/of/for True Personalization

Oinas-Kukkonen

Pohjolainen

Agyei

2022

Front. Artif. Intell.

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A common but false perception persists about the level and type of personalization in the offerings of contemporary software, information systems, and services, known as Personalization Myopia: this involves a tendency for researchers to think that there are many more personalized services than there genuinely are, for the general audience to think that they are offered personalized services when they really are not, and for practitioners to have a mistaken idea of what makes a service personalized. And yet in an era, which mashes up large amounts of data, business analytics, deep learning, and persuasive systems, true personalization is a most promising approach for innovating and developing new types of systems and services—including support for behavior change. The potential of true personalization is elaborated in this article, especially with regards to persuasive software features and the oft-neglected fact that users change over time.

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Respecting Human Autonomy through Human-Centered AI

Cited by 8 publications

References 6 publications

Exploring Peoples' Perception of Autonomy and Reactance in Everyday AI Interactions

Exploring Peoples' Perception of Autonomy and Reactance in Everyday AI Interactions

Better Together? An Evaluation of AI-Supported Code Translation

Mitigating Issues With/of/for True Personalization

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