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It ’ s just distributed computing: Rethinking AI governanceIndia (TRAI · 2025What we now lump under the unitary label “artificial intelligence” is not a single technology, but a highly varied set of machine learning appli­ cations enabled and supported by a globally ubiquitous system of distributed computing. The paper introduces a 4 part conceptual framework for analyzing the structure of that system, which it labels the digital ecosystem. What we now call “AI” is then shown to be a general functionality of distributed computing. "AI” has been present in primitive forms from the origins of digital computing in the 1950s. Three short case studies show that large-scale machine learning applications have been present in the digital ecosystem ever since the rise of the Internet. and provoked the same public policy concerns that we now associate with “AI.” The governance problems of “AI” are really caused by the development of this digital ecosystem, not by LLMs or other recent applications of machine learning. The paper then examines five recent proposals to “govern AI”and maps them to the constituent elements of the digital ecosystem model. This mapping shows that real-world attempts to assert governance authority over AI capabilities requires systemic control of all four elements of the digital ecosystem: data, computing power, networks and software. “Governing AI,” in other words, means total control of distributed computing. A better alternative is to focus governance and regulation upon specific appli­ cations of machine learning. An application-specific approach to governance allows for a more decentralized, freer and more effective method of solving policy conflicts.

UX of AI & PersonalizationResearch Papers

Educating Artificial Intelligence following the Child Learning Development Trajectories2024Artificial Intelligence is spreading in most daily activities. However, its develop- ment and deployment raise issues related to biases, such as gender and disability, mainly stemming from biased or incomplete datasets and lack of transparency and accountability in its algorithms. To overcome these issues, it is necessary to revert to a human-centered mindset, trying to educate algorithms rather than only train them. Adopting a human-centered approach in AI has been a first step, but it is necessary a step ahead. Indeed, recent theoretical perspectives suggest that edu- cating AI algorithms also need a profound understanding of the context of use in which it operates, adopting an approach like those in which a child is educated from birth following its developmental trajectory. By incorporating well-established educational models into the training of AI algorithms, intelligent systems based on those AI algorithms can better align with human learning trajectories, reducing bi- ases and making them more contextually aware. This paper goes in this direction, presenting an educational human-centered approach as a design methodology for artificial intelligence algorithms used within the European FRACTAL project. This proposal would pave the way toward developing more educated artificial intelligence algorithms since they are adapted to the real context of use.

UX of AI & PersonalizationResearch Papers

Designing Safe and Engaginf AI Experiences for Children - Towards the Definition of Best Practices in UIUX designGrazia Ragone, Paolo Buono, Rosa Lanzilotti · 2024This workshop proposal focuses on best practices in UI/UX design for AI applications aimed at children, emphasising safety, engagement, and ethics. It aims to address the challenge of measuring the safety, trustworthiness, and reliability of interactions between children and AI systems. Through collaborative discussions, participants will explore effective design strategies and ethical guidelines while developing methodologies for assessing the safety and reliability of AI interactions with children. This proposal seeks to foster responsible and child-centered AI design practices within the CHI community.

UX of AI & PersonalizationResearch Papers

Inevitable challenges of autonomy: ethical concerns in personalized algorithmic decision-makingWencheng Lu · 2024Breakthroughs in Artificial Intelligence (AI) have transformed human decision-making processes. Individuals increasingly rely on algorithms to assist with various tasks such as searching and shopping. However, the widespread use of personalized algorithmic decisionmaking has raised numerous ethical concerns, specifically its impact on user autonomy. This article examines these concerns and argues that algorithmic decision-making presents several challenges to user autonomy that are difficult to eliminate. These challenges include the fact that algorithms deviate from a user's authentic self, create self-reinforcing loops that narrow the user's self, and lead to a decline in the user's capacities. This article attributes these challenges to the ontological differences between users and AI, as well as their unawareness regarding the ethical risks associated with algorithms, asserting that the sense of autonomy users experience in algorithmic decision-making is merely an illusion. From the perspective of human-AI interaction, it is proposed a strategy that achieves a balance between human insight and AI through a human-in-the-loop approach to mitigate these challenges. This approach maximizes the strengths of AI in processing and analyzing data while ensuring that users retain agency.

UX of AI & PersonalizationResearch Papers

Farsighted-Fostering Responsable AI awarness during AI application prototypingWang · 2024Prompt-based interfaces for Large Language Models (LLMs) have made prototyping and building AI-powered applications easier than ever before. However, identifying potential harms that may arise from AI applications remains a challenge, particularly during ∗The work was done when the authors were at Google Research. This work is licensed under a Creative Commons Attribution 4.0 International License. CHI ’24, May 11–16, 2024, Honolulu, HI, USA © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0330-0/24/05. [https://doi.org/10.1145/3613904.3642335](https://doi.org/10.1145/3613904.3642335) prompt-based prototyping. To address this, we present Farsight, a novel in situ interactive tool that helps people identify potential harms from the AI applications they are prototyping. Based on a user’s prompt, Farsight highlights news articles about relevant AI incidents and allows users to explore and edit LLM-generated use cases, stakeholders, and harms. We report design insights from a co- design study with 10 AI prototypers and findings from a user study with 42 AI prototypers. After using Farsight, AI prototypers in our user study are better able to independently identify potential harms associated with a prompt and find our tool more useful and usable than existing resources. Their qualitative feedback also highlights that Farsight encourages them to focus on end-users and think beyond immediate harms. We discuss these findings and reflect on 1 arXiv:2402.15350v2 [cs.HC] 2 Jul 2024 CHI ’24, May 11–16, 2024, Honolulu, HI, USA Zijie J. Wang, et al. their implications for designing AI prototyping experiences that meaningfully engage with AI harms. Farsight is publicly accessible at: [https://pair-code.github.io/farsight](https://pair-code.github.io/farsight). CCS CONCEPTS • Human-centered computing →Interactive systems and tools; • Computing methodologies →Machine learning.

UX of AI & PersonalizationResearch Papers

Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature ReviewTita A.Bach · 2024Ensuring quality human-AI interaction (HAII) in safety-critical industries is essential. Failure to do so can lead to catastrophic and deadly consequences. Despite this urgency, existing research on HAII is limited, fragmented, and inconsistent. We present here a survey of that literature and recommendations for research best practices that should improve the field. We divided our investigation into the following areas: 1) terms used to describe HAII, 2) primary roles of AI-enabled systems, 3) factors that influence HAII, and 4) how HAII is measured. Additionally, we described the capabilities and maturity of the AI-enabled systems used in safety-critical industries discussed in these articles. We found that no single term is used across the literature to describe HAII and some terms have multiple meanings. According to our literature, seven factors influence HAII: user characteristics (e.g., user personality), user perceptions and attitudes (e.g., user biases), user expectations and experience (e.g., mismatched user expectations and experience), AI interface and features (e.g., interactive design), AI output (e.g., perceived accuracy), explainability and interpretability (e.g., level of detail, user understanding), and usage of AI (e.g., heterogeneity of environments). HAII is most measured with user-related subjective metrics (e.g., user perceptions, trust, and attitudes), and AI-assisted decision-making is the most common primary role of AI-enabled systems. Based on this review, we conclude that there are substantial research gaps in HAII. Researchers and developers need to codify HAII terminology, involve users throughout the AI lifecycle (especially during development), and tailor HAII in safety-critical industries to the users and environments. INDEX TERMS Artificial intelligence, humans, measurement, methods, safety, safety-critical, society, survey, systematic literature review, technology readiness level, user.

UX of AI & PersonalizationResearch Papers

Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI ChallengesQiaosi Wang∗ · 2023Technology companies continue to invest in eforts to incorporate responsibility in their Artifcial Intelligence (AI) advancements, while eforts to audit and regulate AI systems expand. This shift towards Responsible AI (RAI) in the tech industry necessitates new practices and adaptations to roles—undertaken by a variety of prac­ titioners in more or less formal positions, many of whom focus on the user-centered aspects of AI. To better understand practices at the intersection of user experience (UX) and RAI, we conducted an interview study with industrial UX practitioners and RAI subject matter experts, both of whom are actively involved in addressing RAI concerns throughout the early design and development of new AI-based prototypes, demos, and products, at a large technology company. Many of the specifc practices and their associated chal­ lenges have yet to be surfaced in the literature, and distilling them ofers a critical view into how practitioners’ roles are adapting to meet present-day RAI challenges. We present and discuss three emerging practices in which RAI is being enacted and reifed in UX practitioners’ everyday work. We conclude by arguing that the emerging practices, goals, and types of expertise that surfaced in our study point to an evolution in praxis, with associated challenges that suggest important areas for further research in HCI. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI;

UX of AI & PersonalizationResearch Papers

UX designers pushing AI in the enterprise: a case for adaptive UIsJohn Zimmerman · 2020- → Adaptive user interfaces (AUIs) provide an untapped opportunity for UX designers to innovate with AI. - → AUIs seem well suited for enterprise applications, where it is easier to estimate the value of saving a worker time by automating mundane and repetitive tasks. - → AUIs raise new ethical questions about user privacy and about the unintended impacts of making workers faster at their work.

UX of AI & PersonalizationResearch Papers

Explanations as Mechanisms for Supporting Algorithmic TransparencyEmilee Rader · 2018Transparency can empower users to make informed choices about how they use an algorithmic decision-making system and judge its potential consequences. However, transparency is often conceptualized by the outcomes it is intended to bring about, not the specifics of mechanisms to achieve those out- comes. We conducted an online experiment focusing on how different ways of explaining Facebook’s News Feed algorithm might affect participants’ beliefs and judgments about the News Feed. We found that all explanations caused participants to become more aware of how the system works, and helped them to determine whether the system is biased and if they can control what they see. The explanations were less effective for helping participants evaluate the correctness of the system’s output, and form opinions about how sensible and consistent its behavior is. We present implications for the design of trans- parency mechanisms in algorithmic decision-making systems based on these results. Author Keywords algorithmic decision-making; transparency; explanations ACM Classification Keywords H.5.m. Information Interfaces and Presentation (e.g. HCI): Miscellaneous

UX of AI & PersonalizationResearch Papers

Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic managementMin Kyung Lee · 2013Algorithms increasingly make managerial decisions that people used to make. Perceptions of algorithms, regardless of the algorithms’ actual performance, can significantly influence their adoption, yet we do not fully understand how people perceive decisions made by algorithms as compared with decisions made by humans. To explore perceptions of algo- rithmic management, we conducted an online experiment using four managerial decisions that required either mechan- ical or human skills. We manipulated the decision-maker (algorithmic or human), and measured perceived fairness, trust, and emotional response. With the mechanical tasks, algorithmic and human-made decisions were perceived as equally fair and trustworthy and evoked similar emotions; however, human managers’ fairness and trustworthiness were attrib- uted to the manager’s authority, whereas algorithms’ fairness and trustworthiness were attributed to their perceived efficiency and objectivity. Human decisions evoked some positive emotion due to the possibility of social recognition, whereas algorithmic decisions generated a more mixed response – algorithms were seen as helpful tools but also possible tracking mechanisms. With the human tasks, algorithmic decisions were perceived as less fair and trustworthy and evoked more negative emotion than human decisions. Algorithms’ perceived lack of intuition and subjective judg- ment capabilities contributed to the lower fairness and trustworthiness judgments. Positive emotion from human decisions was attributed to social recognition, while negative emotion from algorithmic decisions was attributed to the dehumanizing experience of being evaluated by machines. This work reveals people’s lay concepts of algorithmic versus human decisions in a management context and suggests that task characteristics matter in understanding people’s experiences with algorithmic technologies.

UX of AI & PersonalizationResearch Papers