High Recall Retrieval Via Technology-Assisted Review

被引:0
|
作者
Gray, Lenora [1 ]
Lewis, David D. [1 ]
Pickens, Jeremy [1 ]
Yang, Eugene [2 ]
机构
[1] Redgrave Data, Chantilly, VA 20151 USA
[2] Johns Hopkins Univ, HLTCOE, Baltimore, MD USA
关键词
text classification; human-in-the-loop; active learning; generative; AI; statistical evaluation;
D O I
10.1145/3626772.3661376
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
High Recall Retrieval (HRR) tasks, including eDiscovery in the law, systematic literature reviews, and sunshine law requests focus on efficiently prioritizing relevant documents for human review. Technology-assisted review (TAR) refers to iterative human-in-theloop workflows that combine human review with IR and AI techniques to minimize both time and manual effort while maximizing recall. This full-day tutorial provides a comprehensive introduction to TAR. The morning session presents an overview of the key technologies and workflow designs used, the basics of practical evaluation methods, and the social and ethical implications of TAR deployment. The afternoon session provides more technical depth on the implications of TAR workflows for supervised learning algorithm design, how generative AI is can be applied in TAR, more sophisticated statistical evaluation techniques, and a wide range of open research questions.
引用
收藏
页码:2987 / 2988
页数:2
相关论文
共 50 条
  • [31] Technology-assisted dietary assessment
    Zhu, Fengqing
    Mariappan, Anand
    Boushey, Carol J.
    Kerr, Deb
    Lutes, Kyle D.
    Ebert, David S.
    Delp, Edward J.
    COMPUTATIONAL IMAGING VI, 2008, 6814
  • [32] TECHNOLOGY-ASSISTED IMPLEMENTATION RESEARCH
    Houston, Thomas
    Sadasivan, Rajani
    English, Thomas
    ANNALS OF BEHAVIORAL MEDICINE, 2015, 49 : S156 - S156
  • [33] A perspective on technology-assisted collaboration
    Conen, W
    Neumann, G
    COORDINATION TECHNOLOGY FOR COLLABORATIVE APPLICATIONS: ORGANIZATIONS, PROCESSES, AND AGENTS, 1998, 1364 : 1 - 7
  • [34] Technology-assisted quantification of movement to predict infants at high risk of motor disability: A systematic review
    Redd, Christian B.
    Karunanithi, Mohan
    Boyd, Roslyn N.
    Barber, Lee A.
    RESEARCH IN DEVELOPMENTAL DISABILITIES, 2021, 118
  • [35] Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review
    Yang, Eugene
    MacAvaney, Sean
    Lewis, David D.
    Frieder, Ophir
    ADVANCES IN INFORMATION RETRIEVAL, PT I, 2022, 13185 : 502 - 517
  • [36] Technology-Assisted Weight Loss Interventions in Primary Care: A Systematic Review
    Levine, David M.
    Savarimuthu, Stella
    Squires, Allison
    Nicholson, Joseph
    Jay, Melanie
    JOURNAL OF GENERAL INTERNAL MEDICINE, 2015, 30 (01) : 107 - 117
  • [37] Technology-Assisted Home Care for People With Dementia and Their Relatives: Scoping Review
    Palmdorf, Sarah
    Stark, Anna Lea
    Nadolny, Stephan
    Eliass, Gerrit
    Karlheim, Christoph
    Kreisel, Stefan H.
    Gruschka, Tristan
    Trompetter, Eva
    Dockweiler, Christoph
    JMIR AGING, 2021, 4 (01)
  • [38] ECONOMIC BENEFIT OF STROKE TECHNOLOGY-ASSISTED REHABILITATION (STAR): A SCOPING REVIEW
    Samkharadze, T.
    Cheng, H. J.
    Kager, S.
    Chua, K. S. G.
    Lambercy, O.
    Wenderoth, N.
    VALUE IN HEALTH, 2023, 26 (06) : S8 - S8
  • [39] Engaging Parents in Technology-Assisted Interventions for Childhood Adversity: Systematic Review
    Aldridge, Grace
    Tomaselli, Alessandra
    Nowell, Clare
    Reupert, Andrea
    Jorm, Anthony
    Yap, Marie Bee Hui
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2024, 26
  • [40] Confidence Sequences for Evaluating One-Phase Technology-Assisted Review
    Lewis, David D.
    Gray, Lenora
    Noel, Mark
    PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND LAW, ICAIL 2023, 2023, : 131 - 140