The breakthroughs and benefits attributed to big data and, consequently, to machine learning (ML) - or AIsystems [1, 2], have also resulted in making prevalent how these systems are capable of producing unexpected, biased, and in some cases, undesirable output [3, 4, 5]. Seminal work on bias (i.e., prejudice for, or against one person, or group, especially in a way considered to be unfair) in the context of ML systems demonstrates how facial recognition tools and popular search engines can exacerbate demographic disparities, worsening the marginalization of minorities at the individual and group level [6, 7]. Further, biases in news recommenders and social media feeds actively play a role in conditioning and manipulating people's behavior and amplifying individual and public opinion polarization [8, 9]. In this context, the last few years have seen the consolidation of the Trustworthy AI framework, led in large part by regulatory bodies [10], with the objective of guiding commercial AI development to proactively account for ethical, legal, and technical dimensions [11]. Furthermore, this framework is also accompanied by the call to establish standards across the field in order to ensure AI systems are safe, secure and fair upon deployment [11]. In terms of AI bias, many efforts have been concentrated in devising methods that can improve its identification, understanding, measurement, and mitigation [12]. For example, the special publication prepared by the National Institute of Standards and Technology (NIST) proposes a thorough, however not exhaustive, categorization of different types of bias in AI beyond common computational definitions (see Figure 1 for core hierarchy) [13]. In this same direction, some scholars advocate for practices that account for the characteristics of ML pipelines (i.e., datasets, ML algorithms, and user interaction loop) [14] to enable actors concerned with its research, development, regulation, and use, to inspect all the actions performed across the engineering process, with the objective to increase trust placed not only on the development processes, but on the systems themselves [15, 16, 17, 18].

Social networks are vital for information sharing, especially in the health sector for discussing diseases and treatments. These platforms, however, often feature posts as brief texts, posing challenges for Artificial Intelligence (AI) in understanding context. We introduce a novel hybrid approach combining community-maintained knowledge graphs (like Wikidata) with deep learning to enhance the categorization of social media posts. This method uses advanced entity recognizers and linkers (like Falcon 2.0) to connect short post entities to knowledge graphs. Knowledge graph embeddings (KGEs) and contextualized word embeddings (like BERT) are then employed to create rich, context-based representations of these posts. Our focus is on the health domain, particularly in identifying posts related to eating disorders (e.g., anorexia, bulimia) to aid healthcare providers in early diagnosis. We tested our approach on a dataset of 2,000 tweets about eating disorders, finding that merging word embeddings with knowledge graph information enhances the predictive models' reliability. This methodology aims to assist health experts in spotting patterns indicative of mental disorders, thereby improving early detection and accurate diagnosis for personalized medicine.

Bias evaluation benchmarks and dataset and model documentation have emerged as central processes for assessing the biases and harms of artificial intelligence (AI) systems. However, these auditing processes have been criticized for their failure to integrate the knowledge of marginalized communities and consider the power dynamics between auditors and the communities. Consequently, modes of bias evaluation have been proposed that engage impacted communities in identifying and assessing the harms of AI systems (e.g., bias bounties). Even so, asking what marginalized communities want from such auditing processes has been neglected. In this paper, we ask queer communities for their positions on, and desires from, auditing processes. To this end, we organized a participatory workshop to critique and redesign bias bounties from queer perspectives. We found that when given space, the scope of feedback from workshop participants goes far beyond what bias bounties afford, with participants questioning the ownership, incentives, and efficacy of bounties. We conclude by advocating for community ownership of bounties and complementing bounties with participatory processes (e.g., co-creation).