AI-driven models are increasingly deployed in operational analytics
solutions, for instance, in investigative journalism or the intelligence
community. Current approaches face two primary challenges: ethical and privacy
concerns, as well as difficulties in efficiently combining heterogeneous data
sources for multimodal analytics. To tackle the challenge of multimodal
analytics, we present MULTI-CASE, a holistic visual analytics framework
tailored towards ethics-aware and multimodal intelligence exploration, designed
in collaboration with domain experts. It leverages an equal joint agency
between human and AI to explore and assess heterogeneous information spaces,
checking and balancing automation through Visual Analytics. MULTI-CASE operates
on a fully-integrated data model and features type-specific analysis with
multiple linked components, including a combined search, annotated text view,
and graph-based analysis. Parts of the underlying entity detection are based on
a RoBERTa-based language model, which we tailored towards user requirements
through fine-tuning. An overarching knowledge exploration graph combines all
information streams, provides in-situ explanations, transparent source
attribution, and facilitates effective exploration. To assess our approach, we
conducted a comprehensive set of evaluations: We benchmarked the underlying
language model on relevant NER tasks, achieving state-of-the-art performance.
The demonstrator was assessed according to intelligence capability assessments,
while the methodology was evaluated according to ethics design guidelines. As a
case study, we present our framework in an investigative journalism setting,
supporting war crime investigations. Finally, we conduct a formative user
evaluation with domain experts in law enforcement. Our evaluations confirm that
our framework facilitates human agency and steering in security-sensitive

Exploring the Challenges of Ethical and Multimodal Analytics in Operational Intelligence

In the evolving landscape of operational analytics, AI-driven models are playing an increasingly crucial role. Their applications range from investigative journalism to intelligence community operations. However, the deployment of these models faces two primary challenges: ethical concerns and difficulties in effectively combining heterogeneous data sources for multimodal analytics.

Ethical and privacy concerns have become paramount in recent years, particularly when it comes to the use of AI in sensitive domains. The potential for bias, discrimination, and violation of privacy rights has raised significant questions about the responsible deployment of these technologies.

The second challenge revolves around the complex task of integrating multiple data sources to enable comprehensive multimodal analytics. In operational intelligence exploration, it is essential to extract actionable insights from various types of data, such as text, images, and network connections. However, efficiently combining and analyzing these diverse sources can be a daunting task.

The Holistic Approach of MULTI-CASE

To address the challenges of ethical and multimodal analytics, researchers have developed the MULTI-CASE framework. This visual analytics framework aims to facilitate ethics-aware and multimodal intelligence exploration while ensuring an equal partnership between human analysts and AI systems.

MULTI-CASE leverages the power of Visual Analytics to enable human analysts to explore and assess heterogeneous information spaces. It provides a set of linked components, including a combined search function, annotated text view, and graph-based analysis. These components allow the exploration of different types of data in a cohesive and interconnected manner.

One key aspect of MULTI-CASE is its fully-integrated data model. By leveraging a unified approach to data representation, it enables seamless integration and analysis of diverse data sources. This integrative approach ensures that analysts can explore and compare information from various modalities, leading to a more comprehensive understanding of the analyzed domain.

The Role of AI and Language Models

MULTI-CASE incorporates AI capabilities, particularly through the use of a RoBERTa-based language model. This language model is fine-tuned to meet the specific requirements of the users, ensuring optimal performance in entity detection and analysis of textual information.

The underlying AI components complement the human analysts’ expertise, assisting in the identification and extraction of relevant entities and information. This collaborative approach allows analysts to leverage the power of AI while maintaining full control over the decision-making process and ensuring transparency in the analysis.

Evaluation and Case Study

To validate the effectiveness of MULTI-CASE, comprehensive evaluations were conducted. The benchmarking of the language model on relevant Named Entity Recognition (NER) tasks demonstrated state-of-the-art performance, attesting to its efficacy in entity detection.

The demonstrator’s intelligence capability was assessed using standardized evaluation methods, while the methodology was evaluated based on established ethics design guidelines. These evaluations provided insights into the framework’s strengths and opportunities for further improvement.

A case study was also presented, focusing on the framework’s application in an investigative journalism setting for supporting war crime investigations. This case study showcased MULTI-CASE’s ability to empower human analysts in complex and security-sensitive domains.

The final formative user evaluation involved domain experts from law enforcement. Their feedback provided valuable insights into the usability, effectiveness, and practical implications of the framework in real-world operational scenarios.

MULTI-CASE and the Wider Field of Multimedia Information Systems

The MULTI-CASE framework exemplifies the multi-disciplinary nature of multimedia information systems. It combines elements from visual analytics, artificial intelligence, and information retrieval to tackle the challenges of ethical and multimodal analytics in operational intelligence.

Furthermore, it is closely related to the domains of animations, artificial reality, augmented reality, and virtual realities. The incorporation of AI components, including language models, allows for enhanced virtual experiences and augmented decision-making capabilities.

The developments in the MULTI-CASE framework contribute to the ongoing evolution of multimedia information systems by providing a comprehensive and human-centered approach to ethics-aware and multimodal intelligence exploration. Its potential impact on operational analytics, investigative journalism, and intelligence community operations is significant and highlights the importance of responsible and collaborative deployments of AI-driven models.

Read the original article