Information Retrieval and Artificial Intelligence

Akermi, Imen; Pasi, Gabriella; Abdulahhad, Karam

doi:10.1007/978-3-030-06170-8_5

Cited by 6 publications

(5 citation statements)

References 165 publications

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“…In addition, one meaning can be expressed by many keywords 49 . There is a long‐running research program of trying to addressing these problems 50,51 . The push towards integration of semantic context according to the user's information need and the user's understanding of documents in the collection into IR systems is one of the main topics of current IR research 49 .…”

Section: Resultsmentioning

confidence: 99%

“…49 There is a long-running research program of trying to addressing these problems. 50,51 The push towards integration of semantic context according to the user's information need and the user's understanding of documents in the collection into IR systems is one of the main topics of current IR research. 49 On the medical side, knowledge extraction may prove fundamental for accelerating the bench to bedside passage in pharmacological research.…”

Section: Information Retrievalmentioning

confidence: 99%

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Artificial intelligence methods for a Bayesian epistemology‐powered evidence evaluation

Pretis

Landes

Peden

2021

Evaluation Clinical Practice

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Rationale, aims and objectives: The diversity of types of evidence (eg, case reports, animal studies and observational studies) makes the assessment of a drug's safety profile into a formidable challenge. While frequentist uncertain inference struggles in aggregating these signals, the more flexible Bayesian approaches seem better suited for this quest. Artificial Intelligence (AI) offers great promise to these approaches for information retrieval, decision support, and learning probabilities from data.Methods: E-Synthesis is a Bayesian framework for drug safety assessments built on philosophical principles and considerations. It aims to aggregate all the available information, in order to provide a Bayesian probability of a drug causing an adverse reaction. AI systems are being developed for evidence aggregation in medicine, which increasingly are automated.Results: We find that AI can help E-Synthesis with information retrieval, usability (graphical decision-making aids), learning Bayes factors from historical data, assessing quality of information and determining conditional probabilities for the so-called 'indicators' of causation for E-Synthesis. Vice versa, E-Synthesis offers a solid methodological basis for (semi-)automated evidence aggregation with AI systems.Conclusions: Properly applied, AI can help the transition of philosophical principles and considerations concerning evidence aggregation for drug safety to a tool that can be used in practice.

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Section: Resultsmentioning

confidence: 99%

Section: Information Retrievalmentioning

confidence: 99%

Artificial intelligence methods for a Bayesian epistemology‐powered evidence evaluation

Pretis

Landes

Peden

2021

Evaluation Clinical Practice

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“…Information retrieval (IR) is one of the computer science fields that is closely connected to the developments in the domain of artificial intelligence (AI). Defined as the process of selecting items that are deemed relevant for the user information needs based on the user input [1], IR has been argued to be a particularly promising area of applying AI [2,3]. The integration of AI can benefit different aspects of IR, ranging from knowledge representation to content indexing and matching [6] to relevance modelling [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Defined as the process of selecting items that are deemed relevant for the user information needs based on the user input [1], IR has been argued to be a particularly promising area of applying AI [2,3]. The integration of AI can benefit different aspects of IR, ranging from knowledge representation to content indexing and matching [6] to relevance modelling [3,4]. Consequently, there is a long history of research on AI-driven IR applications, starting with rule-based approaches in the 1980s [2] and ending with the neutral network-based approaches discussed in the 2020s [4].…”

Section: Introductionmentioning

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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“…image, text, or sound; Epstein et al 2023). The ability to produce new content instead of retrieving already existing content is a major distinction between earlier forms of non-generative AI primarily used in the field of information retrieval (Boughanem et al 2020). However, memory communication is also happening between robotic agents themselves without the immediate supervision of humans.…”

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confidence: 99%

Shall the robots remember? Conceptualising the role of non-human agents in digital memory communication

Makhortykh

2024

Mem. Mind & Media

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The rise of digital technology has led to fundamental changes in how individual and collective perspectives on the past are transmitted and engaged. An immediate implication of these changes relates to the shift away from human communication as a single form of communication about memory towards multiple models which involve non-human (or robotic) agents. These non-human agents are primarily constituted by artificial intelligence (AI)-driven systems, such as search engines and conversational agents, which retrieve information about the past for human users and are increasingly used to generate memory-related content. To account for the growing complexity of memory-related digital communication, the article introduces three agency-based models of such communication: (1) human-to-human; (2) human-to-robot; and (3) robot-to-robot. It discusses examples of communication practices enabled by these models and scrutinises their implications for individual and collective memory transmission. The article concludes by outlining several directions for memory communication research increasingly shaped by non-human agents.

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Information Retrieval and Artificial Intelligence

Cited by 6 publications

References 165 publications

Artificial intelligence methods for a Bayesian epistemology‐powered evidence evaluation

Artificial intelligence methods for a Bayesian epistemology‐powered evidence evaluation

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

Shall the robots remember? Conceptualising the role of non-human agents in digital memory communication

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