ASSIGNING TRUST RATING TO AI SERVICES USING CAUSAL IMPACT ANALYSIS

    公开(公告)号:US20240062079A1

    公开(公告)日:2024-02-22

    申请号:US18448369

    申请日:2023-08-11

    CPC classification number: G06N5/022

    Abstract: A method and system relates to assigning ratings (i.e., labels) to convey the trustability of AI systems grounded in its cause-and-effect behavior of significant inputs and outputs of the AI. Sentiment Analysis Systems (SASs) are data-driven Artificial Intelligence (AI) systems that, given a piece of text, assign a score conveying the sentiment and emotion intensity. The present disclosure uses the approach that protected attributes like gender and race influence the output (sentiment) given by SASs or if the sentiment is based on other components of the textual input, e.g., chosen emotion words. The presently disclosed rating methodology assigns ratings at fine-grained and overall levels, to rate SASs grounded in a causal setup, and provides an open-source implementation of both SASs—two deep-learning based, one lexicon-based, and two custom-built models—for this rating implementation. This allows users to understand the behavior of SAS in real-world applications.

    SYSTEM AND METHOD FOR BUILDING TEAMS

    公开(公告)号:US20230112486A1

    公开(公告)日:2023-04-13

    申请号:US17953593

    申请日:2022-09-27

    Abstract: The disclosure deals with a system and method for building teams in response to a teaming opportunity. In one exemplary embodiment disclosed herewith, a system and method for building teams for Request for Proposals (RFPs) is described where potential team participants are researchers at one or more institutions. A computer-based method and computer system, given RFPs from funding agencies like NSF, DOE and NASA, recommends a team of experts from various faculties and departments of the organization, like a university, that would best fit the needs of the RFP and have a high chance of putting a successful proposal together. The system generates teams that may match the requirements of an RFP. In addition, the system optimizes the list of teams to maximize winning success and to reduce redundancy. The system input includes RFPs and the researchers' public information. The system output is a list of proposed teams, each team with two or more members. Optionally, each team will have an estimation of the team's budget and proposal success chances. The disclosed methodology is more broadly applicable to team-building opportunities in general.

    SYSTEM AND METHOD TO MEDIATE SOCIAL MEDIA PLATFORMS AUTOMATICALLY FOR USER SAFETY

    公开(公告)号:US20240291795A1

    公开(公告)日:2024-08-29

    申请号:US18588120

    申请日:2024-02-27

    CPC classification number: H04L51/52 G06Q50/01 H04L51/21

    Abstract: The disclosure deals with a system and method for mediating social media platforms automatically for user safety, including in particular for automatically or semi-automatically mediating social media platforms for user safety. People meet on online platforms and discuss a variety of topics. Moderators associated with those platforms play an important role in making the platform convenient and safe to users. This disclosure addresses detecting users who can act as potential moderators of an online group of support seekers and support providers in an online social media platform operating on the Internet. Such users are classified to identify the class of supportive users and class of non-supportive users of an online group. Received suggestions and other acquired data are used to identify a potential moderator for the online group. Acquired data on supportive and non-supportive users, as well as on harmful users, is then automatically supplied to the moderator.

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