On the Road to Designing Responsible AI Systems in Military Cyber Operations

Chockalingam

2023

iccws

The ongoing decade was believed to be a peaceful one. However, contemporary conflicts, and in particular, ongoing wars prove the opposite as they show the increase in context complexity when defining their goals as well as execution strategies for building means and methods for achieving them by gaining advantage against their adversaries through the engagement of well-established targets. At the core of the engagement decision relies the principle of proportionality which brings in a direct relation the expected unintended effects on civilian side with the anticipated intended effects on military side. While the clusters of effects involved in the proportionality assessment are clear, the process itself is subjective, governed by different dimensions of uncertainty, and represents the responsibility of military Commanders. Thus, a complex socio-technical process where different clusters of influential factors (e.g., military, technical, socio-ethical) play a role in the decisions made. Having said that, the objective of this research is to capture and cluster these factors, and further to model their influence in the proportionality decision-making process. This decision support system produces military targeting awareness to the agents involved in the processes of building, executing, and assessing military operations. To accomplish the aim of this research, a Design Science Research methodological approach is taken for capturing and modelling the influential factors as a socio-technical artefact in the form of a Bayesian Belief Network (BBN) model. The model proposed is further evaluated through demonstration on three different cases in respect to real military operations incidents and scenarios existing in the scientific literature in this research field. Hence, through this demonstration, it is illustrated and interpreted how the factors identified influence proportionality decisions when assessing target engagement as being proportional or disproportional. In these cases, corresponding measures for strengthening proportionality and reducing disproportionality in military operations are considered.

Section: Research Background and Related Researchmentioning

confidence: 99%

Modelling the Influential Factors Embedded in the Proportionality Assessment in Military Operations

Chockalingam

2023

iccws

“…Due to the increase in innovation, computation, and (multi-source) data availability, the developments and solutions proposed based on AI techniques that match and even go further beyond human cognition and decision-making through different ML approaches, either have completely changed some societal domains or are in the process of doing that (Janiesch, Zschech & Heinrich, 2021;Maathuis, 2022b). Currently, at the core of development in the field of ML is building intelligent (complex) neural architectures, i.e., deep learning, which while is being perceived as a novelty, "is around for decades" (Ng, 2017).…”

Section: Cnn and Bert Modelling Techniquesmentioning

confidence: 99%

Social Media Manipulation Deep Learning based Disinformation Detection

Godschalk²

2023

iccws

The rapid grow and use of different social platforms enhanced communication between different entities and their audiences plus the transformation through digitalization of existing, e.g., ideas and businesses, or the creation of new ones fully existing or depending on this digital environment. Nevertheless, next to these promising aspects, social media is a vulnerable digital environment where a diverse plethora of cyber incidents are planned and executed engaging a diverse range of targets. Among these, social media manipulation through threats like disinformation and misinformation produce a broad span of effects that cross digital borders into the human realm by influencing and altering human believes, behaviour, and attitudes towards specific ideas, institutions, or people. To tackle these issues, existing academic, social platforms, dedicated organizations, and institutions efforts exist for building specific advanced and intelligent solutions for detecting and preventing them. Regardless, these efforts embed defender’s perspective and are focused locally, at target level, without being designed to fit a broader agenda of producing and/or strengthening social media security awareness. On this behalf, this research proposes a deep learning-based disinformation detection solution for facilitating and/or enhancing social media security awareness in respect to offender’s perspective. To achieve this objective, a Data Science approach is taken based on the Design Science Research methodology, and the results obtained are discussed with a keen on further field developments regarding intelligent, transparent, and responsible solutions countering social manipulation through realistic participation and contribution of different stakeholders from different disciplines.

“…Because of ongoing social, economic, and technological developments directly reflected in different directions of digital innovation, the increase and availability of high computing resources and multi-source data support the expansion of AI at theoretical and practical levels (Janiesch, Zschech & Heinrich, 2021;Maathuis, 2022a). Currently, the most innovative sub-field of AI is deep learning which implies in the context of social media building (complex) neural architectures relying on (large) datasets having analytical or predictive purposes for tackling existing or upcoming issues and challenges of this domain.…”

Section: Deep Learning Modelling Techniquesmentioning

confidence: 99%

Design Lessons from Building Deep Learning Disinformation Generation and Detection Solutions

Kerkhof

Godschalk

et al. 2023

eccws

In its essence, social media is on its way of representing the superposition of all digital representations of human concepts, ideas, believes, attitudes, and experiences. In this realm, the information is not only shared, but also {mis, dis}interpreted either unintentionally or intentionally guided by (some kind of) awareness, uncertainty, or offensive purposes. This can produce implications and consequences such as societal and political polarization, and influence or alter human behaviour and beliefs. To tackle these issues corresponding to social media manipulation mechanisms like disinformation and misinformation, a diverse palette of efforts represented by governmental and social media platforms strategies, policies, and methods plus academic and independent studies and solutions are proposed. However, such solutions are based on a technical standpoint mainly on gaming or AI-based techniques and technologies, but often only consider the defender’s perspective and address in a limited way the social perspective of this phenomenon becoming single angled. To address these issues, this research combines the defenders’ perspective with the one of the offenders by (i) building a hybrid deep learning disinformation generation and detection model and (ii) capturing and proposing a set of design recommendations that could be considered when establishing patterns, requirements, and features for building future gaming and AI-based solutions for combating social media manipulation mechanisms. This is done using the Design Science Research methodology in Data Science approach aiming at enhancing security awareness and resilience against social media manipulation.