Sep 2026· AI and Ethics· Vol 6· 0 citations· 33 references
TL;DR
This critical integrative review synthesizes research on automation bias, algorithm appreciation and aversion, large-language-model sycophancy, persuasive dialogue, anthropomorphic interaction, explainable AI, and human autonomy to reposition productive disagreement as a positive safety and autonomy property of human–AI systems.
It is argued that once moral trustworthiness is set aside, the central normative question shifts from whether AI systems can be trusted to how their epistemic influence on human decision-making ought to be justified and governed.
John Dorsch, Maximilian Moll, Ophélia Deroy· Philosophy & Technology· 0 citations
Large language models and AI-mediated search systems have become the primary intermediaries for institutional
information across educational, legal, civic, and professional life. Current models of AI ethics focus primarily on the
accuracy, bias, and fairness of AI systems. This focus is essential but fails to recognize...
Jonathan D. Nelson· International Journal of Inn...· 0 citations
As conversational AI systems increasingly operate in sensitive domains, the central challenge shifts from usability to trust calibration, ensuring that users rely on systems neither too much nor too little. Systems that provide advice or interpretations risk encouraging inappropriate reliance, particularly when users p...
R. Hassan, Nahla Aboromi, Naomi Unkelos-Shpigel· 0 citations
The article proposes a reframing of alignment from an ethical-pedagogical project to an institutional one: instead of presuming or attempting to cultivate artificial moral agents on the basis of behavioral proxies, governance should focus on designing legal, technical, and organizational structures that constrain and r...
It is argued that intelligence should be assessed not by output quality or efficiency alone, but by its impact on intellectual character, arguing that intelligence should be assessed not by output quality or efficiency alone, but by its impact on intellectual character.
A mechanistic account according to which AI-driven engagement architectures reconfigure the cues by which beliefs are evaluated, producing unwarranted confidence in one’s own judgments and undermining the conditions for deliberative compromise is developed.
John Dorsch, Mark Coeckelbergh, Tillmann Vierkant· Synthese· 1 citation
In Compton Lecture, the distinguished legal scholar warns of potential “loss of the Enlightenment” while suggesting ways innovations can meet our civic and cultural needs.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
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