Examinando por Autor "Liberal, Karlos G."

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    Assessing emotion and sensitivity of AI artwork
    (Frontiers Media S.A., 2022-04-05) Agudo Díaz, Ujué; Arrese, Miren; Liberal, Karlos G. ; Matute, Helena
    Artificial Intelligence (AI) is currently present in areas that were, until recently, reserved for humans, such as, for instance, art. However, to the best of our knowledge, there is not much empirical evidence on how people perceive the skills of AI in these domains. In Experiment 1, participants were exposed to AI-generated audiovisual artwork and were asked to evaluate it. We told half of the participants that the artist was a human and we confessed to the other half that it was an AI. Although all of them were exposed to the same artwork, the results showed that people attributed lower sensitivity, lower ability to evoke their emotions, and lower quality to the artwork when they thought the artist was AI as compared to when they believed the artist was human. Experiment 2 reproduced these results and extended them to a slightly different setting, a different piece of (exclusively auditory) artwork, and added some additional measures. The results show that the evaluation of art seems to be modulated, at least in part, by prior stereotypes and biases about the creative skills of AI. The data and materials for these experiments are freely available at the Open Science Framework: https://osf.io/3r7xg/. Experiment 2 was preregistered at AsPredicted: https://aspredicted.org/fh2u2.pdf.
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    Ítem
    The impact of AI errors in a human-in-the-loop process
    (Springer Science and Business Media Deutschland GmbH, 2024-01-07) Agudo Díaz, Ujué; Liberal, Karlos G.; Arrese, Miren; Matute, Helena
    Automated decision-making is becoming increasingly common in the public sector. As a result, political institutions recommend the presence of humans in these decision-making processes as a safeguard against potentially erroneous or biased algorithmic decisions. However, the scientific literature on human-in-the-loop performance is not conclusive about the benefits and risks of such human presence, nor does it clarify which aspects of this human–computer interaction may influence the final decision. In two experiments, we simulate an automated decision-making process in which participants judge multiple defendants in relation to various crimes, and we manipulate the time in which participants receive support from a supposed automated system with Artificial Intelligence (before or after they make their judgments). Our results show that human judgment is affected when participants receive incorrect algorithmic support, particularly when they receive it before providing their own judgment, resulting in reduced accuracy. The data and materials for these experiments are freely available at the Open Science Framework: https://osf.io/b6p4z/ Experiment 2 was preregistered