Related papers: Moving Towards Epistemic Autonomy: A Paradigm Shif…

Foundation models excel in stable environments, yet often fail where reliability matters most: medicine, finance, and policy. This Fidelity Paradox is not just a data problem; it is structural. In domains where rules change over time, extra…

Large language models (LLMs) are widely described as artificial intelligence, yet their epistemic profile diverges sharply from human cognition. Here we show that the apparent alignment between human and machine outputs conceals a deeper…

AI researchers and ethicists have long worried about the threat that automation poses to human dignity, autonomy, and to the sense of personal value that is tied to work. Typically, proposed solutions to this problem focus on ways in which…

Divisiveness appears to be increasing in much of the world, leading to concern about political violence and a decreasing capacity to collaboratively address large-scale societal challenges. In this working paper we aim to articulate an…

Human-centered computing research has been increasingly applied to address important challenges in the health domain. Conducting research in cross-disciplinary teams can come with a lot of challenges in integrating knowledge across fields.…

Building on recent interpretivist approaches, we conduct a critical narrative review across journalism studies, human-computer interaction, and FAccT scholarship, conceptualizing editorial authority as the conjunction of decision rights,…

Algorithmic decision-making (ADM) increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by…

We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens arising from the Humanities literature which analyzes how interlocking…

In accessibility research involving human subjects, researchers conventionally anonymize their research participants to protect privacy. However, a lack of intentionality about who to publicly acknowledge for intellectual contributions to…

The increasing integration of artificial intelligence (AI) into medical diagnostics necessitates a critical examination of its ethical and practical implications. While the prioritization of diagnostic accuracy, as advocated by Sabuncu et…

The increasing complexity and unpredictability of many ICT scenarios let us envision that future systems will have to dynamically learn how to act and adapt to face evolving situations with little or no a priori knowledge, both at the level…

Human autonomy and sense of agency are increasingly recognised as critical for user well-being, motivation, and the ethical deployment of robots in human-robot interaction (HRI). Given the rapid development of artificial intelligence, robot…

Performing inference on data obtained through observational studies is becoming extremely relevant due to the widespread availability of data in fields such as healthcare, education, retail, etc. Furthermore, this data is accrued from…

Artificial intelligence (AI) is reshaping higher education, yet current debates often feel tangled, mixing concerns about pedagogy, operations, curriculum, and the future of work without a shared framework. This paper offers a first attempt…

We use an autoethnographic case study of a Latinx high school student from an agricultural community in California to highlight how AI is learned outside classrooms and how her personal background influenced her social-justice oriented…

This paper develops a comprehensive framework for artificial intelligence systems that operate under strict epistemic constraints, moving beyond stochastic language prediction to support structured reasoning, propositional commitment, and…

An epistemic ensemble is composed of knowledge-based agents capable of retrieving and sharing knowledge and beliefs about themselves and their peers. These agents access a global knowledge state and use actions to communicate and cooperate,…

People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of…

People's experiences of discrimination are often shaped by multiple intersecting factors, yet algorithmic fairness research rarely reflects this complexity. While intersectionality offers tools for understanding how forms of oppression…

This paper introduces epistemic graphs as a generalization of the epistemic approach to probabilistic argumentation. In these graphs, an argument can be believed or disbelieved up to a given degree, thus providing a more fine--grained…