Bridging Boundaries: How to Foster Effective Research Collaborations Across Affiliations in the Field of Trust and Safety

• URL: http://arxiv.org/abs/2507.13008v1
• Date: Thu, 17 Jul 2025 11:27:32 GMT
• Title: Bridging Boundaries: How to Foster Effective Research Collaborations Across Affiliations in the Field of Trust and Safety
• Authors: Amanda Menking, Mona Elswah, David J. Grüning, Lasse H. Hansen, Irene Huang, Julia Kamin, Catrine Normann,
• Abstract summary: This paper examines how cross-affiliation research partnerships can be structured to overcome misaligned incentives, timelines and constraints.<n>We propose a practical, step-by-step framework for initiating and managing effective collaborations.
• Score: 0.5300037515002964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the field of Trust and Safety in digital spaces continues to grow, it has become increasingly necessary - but also increasingly complex - to collaborate on research across the academic, industry, governmental and non-governmental sectors. This paper examines how cross-affiliation research partnerships can be structured to overcome misaligned incentives, timelines and constraints while delivering on the unique strengths of each stakeholder. Drawing on our own experience of cross-sector collaboration, we define the main types of affiliation and highlight the common differences in research priorities, operational pressures and evaluation metrics across sectors. We then propose a practical, step-by-step framework for initiating and managing effective collaborations, including strategies for building trust, aligning goals, and distributing roles. We emphasize the critical yet often invisible work of articulation and argue that cross-sector partnerships are essential for developing more ethical, equitable and impactful research in trust and safety. Ultimately, we advocate collaborative models that prioritize inclusivity, transparency and real-world relevance in order to meet the interdisciplinary demands of this emerging field.

Related papers

• Beyond Brainstorming: What Drives High-Quality Scientific Ideas? Lessons from Multi-Agent Collaboration [59.41889496960302]
  This paper investigates whether structured multi-agent discussions can surpass solitary ideation.<n>We propose a cooperative multi-agent framework for generating research proposals.<n>We employ a comprehensive protocol with agent-based scoring and human review across dimensions such as novelty, strategic vision, and integration depth.
  arXiv  Detail & Related papers  (2025-08-06T15:59:18Z)
• Towards a Closer Collaboration Between Practice and Research in Agile Software Development Workshop: A Summary and Research Agenda [2.0207347646756646]
  Agile software development principles and values have been widely adopted across various industries, influencing products and services globally.<n>Despite its increasing popularity, a significant gap remains between research and practical implementation.<n>This paper presents the findings of the first international workshop designed to foster collaboration between research and practice in agile software development.
  arXiv  Detail & Related papers  (2025-07-14T20:26:50Z)
• A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes [2.2680216975955134]
  This article presents a comprehensive survey of all three domains, defined under the formalism of Federal Reinforcement Learning (RL), Decentralized RL, and Noncooperative RL, respectively.<n>We include the formulations as well as known theoretical guarantees and highlights and limitations of numerical performance.
  arXiv  Detail & Related papers  (2025-07-08T13:47:40Z)
• Ten Simple Rules for Catalyzing Collaborations and Building Bridges between Research Software Engineers and Software Engineering Researchers [3.2667114928245646]
  This paper presents ten strategic guidelines aimed at fostering productive partnerships between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs)<n>The guidelines emphasize the importance of recognizing and respecting the cultural and operational differences between RSEs and SERs.<n>They advocate for identifying shared challenges, maintaining openness to emerging problems, ensuring mutual benefits, and serving as advocates for one another.
  arXiv  Detail & Related papers  (2025-06-03T15:51:17Z)
• OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models [55.21589313404023]
  OpenReview is a continually evolving repository of research papers, peer reviews, author rebuttals, meta-reviews, and decision outcomes.<n>We highlight three promising areas in which OpenReview can uniquely contribute: enhancing the quality, scalability, and accountability of peer review processes; enabling meaningful, open-ended benchmarks rooted in genuine expert deliberation; and supporting alignment research through real-world interactions reflecting expert assessment, intentions, and scientific values.<n>We suggest the community collaboratively explore standardized benchmarks and usage guidelines around OpenReview, inviting broader dialogue on responsible data use, ethical considerations, and collective stewardship.
  arXiv  Detail & Related papers  (2025-05-24T09:07:13Z)
• Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration [51.452664740963066]
  Collaborative Gym is a framework enabling asynchronous, tripartite interaction among agents, humans, and task environments.<n>We instantiate Co-Gym with three representative tasks in both simulated and real-world conditions.<n>Our findings reveal that collaborative agents consistently outperform their fully autonomous counterparts in task performance.
  arXiv  Detail & Related papers  (2024-12-20T09:21:15Z)
• Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives [48.48294460952039]
  This survey offers comprehensive descriptions of the privacy, security, and fairness issues in federated learning. We contend that there exists a trade-off between privacy and fairness and between security and sharing.
  arXiv  Detail & Related papers  (2024-06-16T10:31:45Z)
• Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice [10.979734542685447]
  An emerging body of research indicates that ineffective cross-functional collaboration represents a major barrier to addressing issues of fairness in AI design and development. We conducted a series of interviews and design workshops with 23 industry practitioners spanning various roles from 17 companies. We found that practitioners engaged in bridging work to overcome frictions in understanding, contextualization, and evaluation around AI fairness across roles.
  arXiv  Detail & Related papers  (2023-06-10T23:42:26Z)
• A Framework for Incentivized Collaborative Learning [15.44652093599549]
  We propose ICL, a general framework for incentivized collaborative learning. We show the broad applicability of ICL to specific cases in federated learning, assisted learning, and multi-armed bandit.
  arXiv  Detail & Related papers  (2023-05-26T16:00:59Z)
• Parsing Objects at a Finer Granularity: A Survey [54.72819146263311]
  Fine-grained visual parsing is important in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms. We conduct an in-depth study of the advanced work from a new perspective of learning the part relationship.
  arXiv  Detail & Related papers  (2022-12-28T04:20:10Z)
• 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)

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.