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AI Teaming and Fairness

NEW! Our demo paper about deploying ULTRA in two geographical regions of the world has been accepted to CODS-COMAD'2024.

NEW! Our paper that presents a novel system to recommend teams using a variety of AI methods has been accepted to IAAI-AAAI'2024 and will receive the Innovative Application award.

Group Recommendation and Fairness

Authors: Biplav Srivastava, Siva Likitha Valluru, Michael Huhns, Sriraam Natarajan

Group Recommendation - Research Potential, Methods, and FairnessWe study the problem of group recommendation, an information exploration paradigm that retrieves interesting items for users based on their profiles and past interactions/activities/history. Existing literature encourages using greedy methods, genetic and heuristic algorithms, topic diversification, and cost constraint bi-objective optimizations. Our objective is to build novel methods and useful tools for group recommendation with fairness, and drive different use cases (e.g., meal recommendation).

The underlying research directions and applications are summarized in the poster to the right (can be enlarged).

Team Formation

Technical Lead: Biplav Srivastava
Collaborators over the years: Siva Likitha Valluru, Sai Teja Paladi, Michael Widener, Rohit Sharma, Owen Bond, Ronak Shah, Austin Hetherington
External Collaborators: Aniket Gupta, Siwen Yan, Sriraam Natarajan, Tarmo Koppel, Sugata Gangopadhyay
Advisors: Michael Matthews, Paul Ziehl, Michael Huhns, Danielle McElwain

A poster of ULTRA.We introduce ULTRA (University Lead Team Builder from RFPs and Analysis), an novel AI-based system for assisting team formation when researchers respond to RFPs from funding agencies. This is an instance of the general problem of building teams when demand opportunities come periodically and potential members may vary over time. The novelties of our approach are that we: (a) extract technical skills needed about researchers and calls from multiple open data sources and normalize them using NLP techniques, (b) build teaming solutions based on constraints, (c) computationally and qualitatively evaluate our system in two diverse settings (US, India) to establish generality of our approach, and (d) create and publish a dataset that others can use.

(This research study has been certified as exempt from the IRB per 45 CFR 46.104(d)(3) and 45 CFR 46.111(a)(7) by University of South Carolina IRB#Pro00127449.)

Representative Publications

  • [2023] ULTRA: Exploring Team Recommendations in Two Geographies Using Open Data in Response to Call for Proposals.
    ACM India Joint International Conference on Data Science and Management of Data (CODS-COMAD-2024)
    [Paper - Coming Soon]

  • [2023] Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals
    The Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24)
    [Tool Websites at UofSC, IIT-R] [Demo Videos for UofSC, IIT-R] [Paper] [BibTex]


  • [2022] ULTRA: A Data-driven Approach for Recommending Team Formation in Response to Proposal Calls.
    IEEE ICDM Workshop on AI for Nudging and Personalization (WAIN)
    [Paper] [BibTex]

    • Figure 1: Teaming setup of ULTRA.
    Figure 2: System architecture of ULTRA.

Additional Tools

Collaborators over the years: Aniket Gupta, Biplav Srivastava, Karan Aggarwal, Sai Teja Paladi

Here, we describe some of the important tools that we have developed as part of the ULTRA effort. They started out as useful features that we then made into stand-alone capabilities recognizing their potentia for wider usage:

  • KITE - An Unsupervised, Effective and Inclusive Approach for Textual Content Exploration.KITE (right) is an unsupervised system for exploring textual data which can generate insights from a general as well as a domain-dependent perspective consisting of holistic views, entity-centric view, events view, domain-specific interpretation using industry taxonomies and a detailed full-text view transparently connecting the document to insight elements.

  • We also developed a text-to-classification mapper, a tool that takes the input as a text and matching threshold as a number and returns the ACM or JEL classification codes and description based on the input text.

Representative Publications