'Transparency is Meant for Control' and Vice Versa: Learning from Co-designing and Evaluating Algorithmic News Recommenders

Storms, Elias; Alvarado, Oscar; Monteiro-Krebs, Luciana

doi:10.1145/3555130

Cited by 14 publications

(3 citation statements)

References 29 publications

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“…We believe current work on training, adapting, and building applications around LLMs can take valuable lessons from these lines of research. More recent HCI studies on algorithmic transparency also highlight that providing transparency without supporting control leaves users frustrated, while effective, efficient, and satisfying control cannot be achieved without transparency (Smith-Renner et al, 2020;Storms et al, 2022).…”

Section: Transparency and Control Often Go Hand-in-handmentioning

confidence: 99%

AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap

Liao,

Wortman Vaughan

2024

Harvard Data Science Review

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The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are developed and deployed responsibly. It is paramount to pursue new approaches to provide transparency-a central pillar of responsible artificial intelligence (AI)-for LLMs, and years of research at the intersection of AI and human-computer interaction (HCI) highlight that we must do so with a human-centered perspective: Transparency is fundamentally about supporting appropriate human understanding, and this understanding is sought by different stakeholders with different goals in different contexts. In this new era of LLMs, we must develop and design approaches to transparency by considering the needs of stakeholders in the emerging LLM ecosystem, the novel types of LLM-infused applications being built, and the new usage patterns and challenges around LLMs, all while building on lessons learned about how people process, interact with, and make use of information. We reflect on the unique challenges that arise in providing transparency for LLMs, along with lessons learned from HCI and responsible AI research that has taken a human-centered perspective on AI transparency. We then lay out four common approaches that the community has taken to achieve transparency-model reporting, publishing evaluation results, providing explanations, and communicating uncertainty-and call out open questions around how these approaches may or may not be applied to LLMs. We hope this provides a starting point for discussion and a useful roadmap for future research.

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Section: Transparency and Control Often Go Hand-in-handmentioning

confidence: 99%

AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap

Liao,

Wortman Vaughan

2024

Harvard Data Science Review

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show abstract

“…We believe current work on training, adapting, and building applications around LLMs can take valuable lessons from this line of research. More recent HCI studies on algorithmic transparency have highlighted that providing transparency without supporting control leaves users frustrated, while effective, efficient, and satisfying control cannot be achieved without transparency [164,167]. More critically, scholars have called out the risk of algorithmic transparency without paths for actionability and contestability as creating a false sense of responsibility and user agency [5,98].…”

Section: Transparency and Control Often Go Hand-in-handmentioning

confidence: 99%

Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience

Liao

Subramonyam

Wang

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are developed and deployed responsibly. However, a central pillar of responsible AI-transparency-is largely missing from the current discourse around LLMs. It is paramount to pursue new approaches to provide transparency for LLMs, and years of research at the intersection of AI and human-computer interaction (HCI) highlight that we must do so with a human-centered perspective: Transparency is fundamentally about supporting appropriate human understanding, and this understanding is sought by different stakeholders with different goals in different contexts. In this new era of LLMs, we must develop and design approaches to transparency by considering the needs of stakeholders in the emerging LLM ecosystem, the novel types of LLM-infused applications being built, and the new usage patterns and challenges around LLMs, all while building on lessons learned about how people process, interact with, and make use of information. We reflect on the unique challenges that arise in providing transparency for LLMs, along with lessons learned from HCI and responsible AI research that has taken a human-centered perspective on AI transparency. We then lay out four common approaches that the community has taken to achieve transparency-model reporting, publishing evaluation results, providing explanations, and communicating uncertainty-and call out open questions around how these approaches may or may not be applied to LLMs. We hope this provides a starting point for discussion and a useful roadmap for future research.

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“…in terms of making their suffering equally visible via AI curation) and dealing with the problem of representation (e.g. in terms of filtering out and removing information distorting historical facts), it also makes the functionality of these systems less transparent, thus limiting the user control over the system [52].…”

Section: Rohingya Genocide)mentioning

confidence: 99%

No AI After Auschwitz? Bridging AI and Memory Ethics in the Context of Information Retrieval of Genocide-Related Information

Makhortykh

2023

Studies in Computational Intelligence

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The growing application of artificial intelligence (AI) in the field of information retrieval (IR) affects different domains, including cultural heritage. By facilitating organisation and retrieval of large volumes of heritage-related content, AI-driven IR systems inform users about a broad range of historical phenomena, including genocides (e.g. the Holocaust).However, it is currently unclear to what degree IR systems are capable of dealing with multiple ethical challenges associated with the curation of genocide-related information. To address this question, this chapter provides an overview of ethical challenges associated with the human curation of genocide-related information using a three-part framework inspired by Belmont criteria (i.e. curation challenges associated with respect for individuals, beneficence and justice/fairness). Then, the chapter discusses to what degree the above-mentioned challenges are applicable to the ways in which AI-driven IR systems deal with genocide-related information and what can be the potential ways of bridging AI and memory ethics in this context.

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'Transparency is Meant for Control' and Vice Versa: Learning from Co-designing and Evaluating Algorithmic News Recommenders

Cited by 14 publications

References 29 publications

AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap

AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap

Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience

No AI After Auschwitz? Bridging AI and Memory Ethics in the Context of Information Retrieval of Genocide-Related Information

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