In the field of Natural Language Processing (NLP), a common approach for resolving human disagreement involves establishing a consensus among multiple annotators. However, previous research shows that overlooking individual opinions can result in the marginalization of minority perspectives, particularly in subjective tasks, where annotators may systematically disagree due to their personal preferences. Emerging Multi-Perspective approaches challenge traditional methodologies that treat disagreement as mere noise, instead recognizing it as a valuable source of knowledge shaped by annotators’ diverse backgrounds, life experiences, and values.This thesis proposal aims to (1) identify the challenges of designing disaggregated datasets i.e., preserving individual labels in human-annotated datasets for subjective tasks (2) propose solutions for developing Perspective-Aware by design systems and (3) explore the correlation between human disagreement and model uncertainty leveraging eXplainable AI techniques (XAI).Our long-term goal is to create a framework adaptable to various subjective NLP tasks to promote the development of more responsible and inclusive models.

How newspapers cover news significantly impacts how facts are understood, perceived, and processed by the public. This is especially crucial when serious crimes are reported, e.g., in the case of femicides, where the description of the perpetrator and the victim builds a strong, often polarized opinion of this severe societal issue. This paper presents FMNews, a new dataset of articles reporting femicides extracted from Italian newspapers. Our core contribution aims to promote the development of a deeper framing and awareness of the phenomenon through an original resource available and accessible to the research community, facilitating further analyses on the topic. The paper also provides a preliminary study of the resulting collection through several example use cases and scenarios.

The varied backgrounds and experiences of human annotators inject different opinions and potential biases into the data, inevitably leading to disagreements. Yet, traditional aggregation methods fail to capture individual judgments since they rely on the notion of a single ground truth. Our aim is to review prior contributions to pinpoint the shortcomings that might cause stereotypical content generation. As a preliminary study, our purpose is to investigate state-of-the-art approaches, primarily focusing on the following two research directions. First, we investigate how adding subjectivity aspects to LLMs might guarantee diversity. We then look into the alignment between humans and LLMs and discuss how to measure it. Considering existing gaps, our review explores possible methods to mitigate the perpetuation of biases targeting specific communities. However, we recognize the potential risk of disseminating sensitive information due to the utilization of socio-demographic data in the training process. These considerations underscore the inclusion of diverse perspectives while taking into account the critical importance of implementing robust safeguards to protect individuals’ privacy and prevent the inadvertent propagation of sensitive information.

Different ways of linguistically expressing the same real-world event can lead to different perceptions of what happened. Previous work has shown that different descriptions of gender-based violence (GBV) influence the reader’s perception of who is to blame for the violence, possibly reinforcing stereotypes which see the victim as partly responsible, too. As a contribution to raise awareness on perspective-based writing, and to facilitate access to alternative perspectives, we introduce the novel task of automatically rewriting GBV descriptions as a means to alter the perceived level of blame on the perpetrator. We present a quasi-parallel dataset of sentences with low and high perceived responsibility levels for the perpetrator, and experiment with unsupervised (mBART-based), zero-shot and few-shot (GPT3-based) methods for rewriting sentences. We evaluate our models using a questionnaire study and a suite of automatic metrics.