Juan Duran

Epistemic transparency is often proposed as a solution to algorithmic opacity, wherein revealing the inner logic of an algorithm provides reasons or supporting evidence for the justification of its outputs. I argue that transparency is defective in belief formation and thus inadequate as an epistemology of algorithms. Two objections are developed: transparency regress and bootstrapping, both rooted in transparency’s status as a time-sliced, “outsourced” inferentialist epistemology. While weaker forms—such as contextual transparency—are briefly considered, the central argument remains that transparency fails to provide justification and should be abandoned as an epistemology of algorithms.

Human-AI complementarity is the claim that a human supported by an AI system can outperform either alone in a decision-making process. Since its introduction in the human-AI interaction literature, it has gained traction by generalizing the reliance paradigm and by offering a more practical alternative to the contested construct of 'trust in AI.' Yet complementarity faces key theoretical challenges: it lacks precise theoretical anchoring, it is formalized just as a post hoc indicator of relative predictive accuracy, it remains silent about other desiderata of human-AI interactions and it abstracts away from the magnitude-cost profile of its performance gain. As a result, complementarity is difficult to obtain in empirical settings. In this work, we leverage epistemology to address these challenges by reframing complementarity within the discourse on justificatory AI. Drawing on computational reliabilism, we argue that historical instances of complementarity function as evidence that a given human-AI interaction is a reliable epistemic process for a given predictive task. Together with other reliability indicators assessing the alignment of the human-AI team with the epistemic standards and socio-technical practices, complementarity contributes to the degree of reliability of human-AI teams when generating predictions. This supports the practical reasoning of those affected by these outputs -- patients, managers, regulators, and others. In summary, our approach suggests that the role and value of complementarity lies not in providing a relative measure of predictive accuracy, but in helping calibrate decision-making to the reliability of AI-supported processes that increasingly shape everyday life.

Some varieties that are extensions of relational algebras with two constants that play the role of projections are studied. The classes have as a subvariety the abstract fork algebra equivalent variety involving projections. They are obtained by weakening some laws valid in AFA. Some applications of the varieties in the literature and in the specification of abstract data types are exhibited. For each of the classes obtained, an answer is given to the question: 'Is the relational reduct of the class representable?'. For the subvarieties formed with the models that have a representable relational reduct, a representation theorem is proved. For them the finitization problem is studied. Next the varieties presented are compared by means of the inclusion order. For each class the problem of characterizing finite models is considered. Simple models in the varieties are studied. Finally the existence of equivalent classes with a binary operation like fork is studied.