Request for Information (RFI): Community Influence on Human Judgment During Information Processing Tasks

This is a Request for Information (RFI) issued solely for information and planning purposes and does not constitute a solicitation. Respondents are advised that the Army Research Lab (ARL) is under no obligation to acknowledge receipt of the information received or provide feedback to respondents with respect to any information submitted under this RFI. Responses to this notice are not offers and cannot be accepted by the Government to form a binding contract. Respondents are solely responsible for all expenses associated with responding to this RFI. ARL will not provide reimbursement for costs incurred in responding to this RFI. It is the respondent's responsibility to ensure that the submitted material has been approved for public release by the information owner. The Government does not intend to award a contract on the basis of this RFI or to otherwise pay for the information solicited, nor is the Government obligated to issue a solicitation based on responses received. Neither proprietary nor classified concepts or information should be included in the submittal. The Army Research Laboratory (ARL) is seeking information on interdisciplinary theories and models for collective influences on human judgment during information processing tasks and prior to collective decision making. This RFI is issued for planning purposes only, and it does not constitute a formal solicitation for proposals or suggest the procurement of any material, data sets, etc. The following sections of this announcement contain details on the specific scientific areas of interest, along with instructions for the submission of responses. Background and Scope: During information processing tasks, the formation of judgments on the utility of information from a wide variety of sources by analysts (working alone or in teams) is influenced by (a) other people in their networks, (b) the available information they consume, (c) potential interactions with virtual agents, and (d) their own internal predispositions based on background, training, and values. These analysts capabilities and performance on such tasks can be expanded and augmented through intelligent systems or information agents leveraging technologies such as Large Language Models (LLMs) or through additional information gained from social media and social networking platforms. However, such human-agent teaming approaches can lead to the emergence of new biases and challenges surrounding the reliability of such judgments made to accept/reject each piece of information being processed. These phenomena reside at the conscious and subconscious levels, further expounding the challenges to understand their impact on judgment formation. This RFI seeks to elicit interdisciplinary perspectives on the judgment formation process in the context of analysts being embedded in larger communities and inundated with algorithmically driven information.