Sharmila Rahman Prithula

arXiv2026

Ethical Risk Assessment of the Data Harnessing Process of LLM supported on Consensus of Well-known Multi-Ethical Frameworks

The rapid advancements in large language models (LLMs) have revolutionized natural language processing, unlocking unprecedented capabilities in communication, automation, and knowledge generation. However, the ethical implications of LLM development, particularly in data harnessing, remain a critical challenge. Despite widespread discussion about the ethical compliance of LLMs -- especially concerning their data harnessing processes, there remains a notable absence of concrete frameworks to systematically guide or measure the ethical risks involved. In this paper we discuss a potential pathway for building an Ethical Risk Scoring (ERS) system to quantitatively assess the ethical integrity of the data harnessing process for AI systems. This system is based on a set of assessment questions grounded in core ethical principles, which are, in turn, supported by commanding ethical theories. By integrating measurable scoring mechanisms, this approach aims to foster responsible LLM development, balancing technological innovation with ethical accountability.

cs.CY↗

arXiv2026

In Quest of an Extensible Multi-Level Harm Taxonomy for Adversarial AI: Heart of Security, Ethical Risk Scoring and Resilience Analytics

Harm is invoked everywhere from cybersecurity, ethics, risk analysis, to adversarial AI, yet there exists no systematic or agreed upon list of harms, and the concept itself is rarely defined with the precision required for serious analysis. Current discourse relies on vague, under specified notions of harm, rendering nuanced, structured, and qualitative assessment effectively impossible. This paper challenges that gap directly. We introduce a structured and expandable taxonomy of harms, grounded in an ensemble of contemporary ethical theories, that makes harm explicit, enumerable, and analytically tractable. The proposed framework identifies 66+ distinct harm types, systematically organized into two overarching domains human and nonhuman, and eleven major categories, each explicitly aligned with eleven dominant ethical theories. While extensible by design, the upper levels are intentionally stable. Beyond classification, we introduce a theory-aware taxonomy of victim entities and formalize normative harm attributes, including reversibility and duration that materially alter ethical severity. Together, these contributions transform harm from a rhetorical placeholder into an operational object of analysis, enabling rigorous ethical reasoning and long term safety evaluation of AI systems and other sociotechnical domains where harm is a first order concern.

cs.CY↗