Artificial intelligence (AI) and machine learning (ML) systems increasingly purport to deliver knowledge about people and the world. Unfortunately, they also seem to frequently present results that repeat or magnify biased treatment of racial and other vulnerable minorities. This paper proposes that at least some of the problems with AI’s treatment of minorities can be captured by the concept of epistemic injustice. To substantiate this claim, I argue that (1) pretrial detention and physiognomic AI systems commit testimonial injustice because their (...) target variables reflect inaccurate and unjust proxies for what they claim to measure; (2) classification systems, such as facial recognition, commit hermeneutic injustice because their classification taxonomies, almost no matter how they are derived, reflect and perpetuate racial and other stereotypes; and (3) epistemic injustice better explains what is going wrong in these types of situations than does the more common focus on procedural (un)fairness. (shrink)

Scientific research is growingly increasingly reliant on "microwork" or "crowdsourcing" provided by digital platforms to collect new data. Digital platforms connect clients and workers, charging a fee for an algorithmically managed workflow based on Terms of Service agreements. Although these platforms offer a way to make a living or complement other sources of income, microworkers lack fundamental labor rights and basic safe working conditions, especially in the Global South. We ask how researchers and research institutions address the ethical issues involved (...) in considering microworkers as "human participants." We argue that current scientific research fails to treat microworkers in the same way as in-person human participants, producing de facto a double morality: one applied to people with rights acknowledged by states and international bodies (e.g., the Helsinki Declaration), the other to guest workers of digital autocracies who have almost no rights at all. We illustrate our argument by drawing on 57 interviews conducted with microworkers in Spanish-speaking countries. (shrink)

Assistive systems based on Artificial Intelligence (AI) are bound to reshape decision-making in all areas of society. One of the most intricate challenges arising from their implementation in high-stakes environments such as medicine concerns their frequently unsatisfying levels of explainability, especially in the guise of the so-called black-box problem: highly successful models based on deep learning seem to be inherently opaque, resisting comprehensive explanations. This may explain why some scholars claim that research should focus on rendering AI systems understandable, rather (...) than explainable. Yet, there is a grave lack of agreement concerning these terms in much of the literature on AI. We argue that the seminal distinction made by the philosopher and physician Karl Jaspers between different types of explaining and understanding in psychopathology can be used to promote greater conceptual clarity in the context of Machine Learning (ML). Following Jaspers, we claim that explaining and understanding constitute multi-faceted epistemic approaches that should not be seen as mutually exclusive, but rather as complementary ones as in and of themselves they are necessarily limited. Drawing on the famous example of Watson for Oncology we highlight how Jaspers’ methodology translates to the case of medical AI. Classical considerations from the philosophy of psychiatry can therefore inform a debate at the centre of current AI ethics, which in turn may be crucial for a successful implementation of ethically and legally sound AI in medicine. (shrink)

In the field of applied computer research, the problem of the reinforcement of existing inequalities through the processing of “big data” in neural networks is typically addressed via concepts of representation and fairness. These approaches, however, tend to overlook the limits of the liberal antidiscrimination discourse, which are well established in critical theory. In this paper, I address these limits and propose a different framework for understanding technologically amplified oppression departing from the notion of “mute compulsion” (Marx), a specifically modern (...) form of power based on the social abstraction of wage labor in market exchange. I begin with a reconstruction of the process of technological abstraction in automated pattern recognition, after which I summarize the conceptional shortcomings of representational fairness discourse. In contrast, I demonstrate how the social reproduction of exploitation is conceptualized in critical theory as being mediated through a process of social abstraction, defined as a social practice of reification that manifest in fetishization. To avoid economistic shortcomings, I contextualize the argument of social abstraction against the backdrop of social reproduction theory, which takes the juncture of various historical forms of oppression into account. I conclude by elaborating how technological abstraction corresponds to the problem of mute compulsion by replicating processes of social abstraction. (shrink)

Large language models (LLMs) structure the linguistic landscape by reflecting certain beliefs and assumptions. In this paper, we address the risk of people unthinkingly adopting and being determined by the values or worldviews embedded in LLMs. We provide a Nietzschean critique of LLMs and, based on the concept of will to power, consider LLMs as will-to-power organisations. This allows us to conceptualise the interaction between self and LLMs as power struggles, which we understand as negotiation. Currently, the invisibility and incomprehensibility (...) of LLMs make it difficult, if not impossible, to engage in such negotiations. This bears the danger that LLMs make reality increasingly homogeneous by recycling beliefs and creating feedback loops that ultimately freeze power struggles and thus consolidate the status quo. In view of this, LLMs constrain self-formation. Based on our critique, we provide some recommendations on how to develop interactions with LLMs that enable negotiations that allow for different ways of being. (shrink)

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace, making it difficult for novel researchers and practitioners to have a bird’s-eye view picture of the field. In particular, many policy initiatives, standards, and best practices in fair-AI have been proposed for setting principles, procedures, and knowledge bases to guide and operationalize the management of bias and fairness. The first objective of this paper is to concisely survey the state-of-the-art of fair-AI methods and resources, (...) and the main policies on bias in AI, with the aim of providing such a bird’s-eye guidance for both researchers and practitioners. The second objective of the paper is to contribute to the policy advice and best practices state-of-the-art by leveraging from the results of the NoBIAS research project. We present and discuss a few relevant topics organized around the NoBIAS architecture, which is made up of a Legal Layer, focusing on the European Union context, and a Bias Management Layer, focusing on understanding, mitigating, and accounting for bias. (shrink)