Deep Learning for Information Systems Research

• URL: http://arxiv.org/abs/2010.05774v1
• Date: Wed, 7 Oct 2020 15:23:05 GMT
• Title: Deep Learning for Information Systems Research
• Abstract summary: Artificial Intelligence (AI) has rapidly emerged as a key disruptive technology in the 21st century. Deep Learning (DL) is an emerging class of algorithms that has enabled today's platforms and organizations to operate at unprecedented efficiency, effectiveness, and scale. Despite significant interest, IS contributions in DL have been limited, which we argue is in part due to issues with defining, positioning, and conducting DL research.
• Score: 12.509120097898482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial Intelligence (AI) has rapidly emerged as a key disruptive technology in the 21st century. At the heart of modern AI lies Deep Learning (DL), an emerging class of algorithms that has enabled today's platforms and organizations to operate at unprecedented efficiency, effectiveness, and scale. Despite significant interest, IS contributions in DL have been limited, which we argue is in part due to issues with defining, positioning, and conducting DL research. Recognizing the tremendous opportunity here for the IS community, this work clarifies, streamlines, and presents approaches for IS scholars to make timely and high-impact contributions. Related to this broader goal, this paper makes five timely contributions. First, we systematically summarize the major components of DL in a novel Deep Learning for Information Systems Research (DL-ISR) schematic that illustrates how technical DL processes are driven by key factors from an application environment. Second, we present a novel Knowledge Contribution Framework (KCF) to help IS scholars position their DL contributions for maximum impact. Third, we provide ten guidelines to help IS scholars generate rigorous and relevant DL-ISR in a systematic, high-quality fashion. Fourth, we present a review of prevailing journal and conference venues to examine how IS scholars have leveraged DL for various research inquiries. Finally, we provide a unique perspective on how IS scholars can formulate DL-ISR inquiries by carefully considering the interplay of business function(s), application areas(s), and the KCF. This perspective intentionally emphasizes inter-disciplinary, intra-disciplinary, and cross-IS tradition perspectives. Taken together, these contributions provide IS scholars a timely framework to advance the scale, scope, and impact of deep learning research.

Related papers

• Deep Research: A Systematic Survey [118.82795024422722]
  Deep Research (DR) aims to combine the reasoning capabilities of large language models with external tools, such as search engines.<n>This survey presents a comprehensive and systematic overview of deep research systems.
  arXiv  Detail & Related papers  (2025-11-24T15:28:28Z)
• The Formalism-Implementation Gap in Reinforcement Learning Research [17.922282609849184]
  This paper argues that reinforcement learning research should stop focusing solely on demonstrating agent capabilities.<n>It argues that we need to be more precise on how our benchmarks map to the underlying mathematical formalisms.
  arXiv  Detail & Related papers  (2025-10-17T19:35:54Z)
• QUINTA: Reflexive Sensibility For Responsible AI Research and Data-Driven Processes [2.504366738288215]
  This paper presents a comprehensive framework grounded in critical reflexivity as intersectional praxis.<n>The framework centers researcher reflexivity to call attention to the AI researchers' power in creating and analyzing AI/DS artifacts through data-centric approaches.
  arXiv  Detail & Related papers  (2025-09-19T18:40:30Z)
• Understanding Large Language Models' Ability on Interdisciplinary Research [27.539601507270575]
  Large Language Models (LLMs) are powerful tools and collaborators in scientific discovery.<n>The lack of a dedicated benchmark that evaluates LLMs' ability to develop ideas in Interdisciplinary Research poses a critical barrier to fully understanding their strengths and limitations.<n>We introduce IDRBench -- a pioneering benchmark featuring an expert annotated dataset and a suite of tasks tailored to evaluate LLMs' capabilities.
  arXiv  Detail & Related papers  (2025-07-21T15:43:05Z)
• Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities [62.05713042908654]
  This paper provides a review of advances in Large Language Models (LLMs) alignment through the lens of inverse reinforcement learning (IRL)<n>We highlight the necessity of constructing neural reward models from human data and discuss the formal and practical implications of this paradigm shift.
  arXiv  Detail & Related papers  (2025-07-17T14:22:24Z)
• AI4Research: A Survey of Artificial Intelligence for Scientific Research [55.5452803680643]
  We present a comprehensive survey on AI for Research (AI4Research)<n>We first introduce a systematic taxonomy to classify five mainstream tasks in AI4Research.<n>We identify key research gaps and highlight promising future directions.
  arXiv  Detail & Related papers  (2025-07-02T17:19:20Z)
• Get on the Train or be Left on the Station: Using LLMs for Software Engineering Research [11.38769375570817]
  We argue that the SE research community must proactively engage with and shape the integration of Large Language Models into research practices.<n>This position paper employs McLuhan's Tetrad of Media Laws to analyze the impact of LLMs on SE research.
  arXiv  Detail & Related papers  (2025-06-15T02:25:50Z)
• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
  A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently. Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
  arXiv  Detail & Related papers  (2025-02-07T18:26:45Z)
• Automating Chapter-Level Classification for Electronic Theses and Dissertations [0.0]
  We propose a machine learning and AI-driven solution to automatically categorize ETD chapters. This solution is intended to improve discoverability and promote understanding of chapters. We aim to support interdisciplinary research and make ETDs more accessible.
  arXiv  Detail & Related papers  (2024-11-26T17:27:18Z)
• Synergizing Knowledge Graphs with Large Language Models: A Comprehensive Review and Future Prospects [5.851598378610756]
  This paper is a comprehensive dissection of the latest developments in integrating Knowledge Graphs with Large Language Models. We introduce a unifying framework designed to elucidate and stimulate further exploration among scholars engaged in cognate disciplines.
  arXiv  Detail & Related papers  (2024-07-26T02:39:30Z)
• A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
  Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation. Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge. RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
  arXiv  Detail & Related papers  (2024-05-10T02:48:45Z)
• A Survey on Knowledge Distillation of Large Language Models [99.11900233108487]
  Knowledge Distillation (KD) emerges as a pivotal methodology for transferring advanced capabilities to open-source models. This paper presents a comprehensive survey of KD's role within the realm of Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2024-02-20T16:17:37Z)
• A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research [23.966640472958105]
  This paper presents a systematic literature review of approaches that aim to improve the explainability of AI models within the context of Software Engineering. We aim to summarize the SE tasks where XAI techniques have shown success to date; (2) classify and analyze different XAI techniques; and (3) investigate existing evaluation approaches.
  arXiv  Detail & Related papers  (2024-01-26T03:20:40Z)
• Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators [78.63553017938911]
  Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks. However, community concerns abound regarding the factuality and potential implications of using this uncensored knowledge. We introduce CONNER, designed to evaluate generated knowledge from six important perspectives.
  arXiv  Detail & Related papers  (2023-10-11T08:22:37Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)
• Deep Learning for Face Anti-Spoofing: A Survey [74.42603610773931]
  Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs)
  arXiv  Detail & Related papers  (2021-06-28T19:12:00Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)
• Nose to Glass: Looking In to Get Beyond [0.0]
  An increasing amount of research has been conducted under the banner of enhancing responsible artificial intelligence. Research aims to address, alleviating, and eventually mitigating the harms brought on by the roll out of algorithmic systems. However, implementation of such tools remains low.
  arXiv  Detail & Related papers  (2020-11-26T06:51:45Z)
• A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research [22.21817722054742]
  An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL) This paper presents a systematic literature review of research at the intersection of SE & DL. We center our analysis around the components of learning, a set of principles that govern the application of machine learning techniques to a given problem domain.
  arXiv  Detail & Related papers  (2020-09-14T15:28:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.