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Semantic Technology - 9th Joint International Conference, JIST 2019, Hangzhou, China, November 25-27, 2019, Revised Selected Papers
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Towards Ethical Risk Assessment of Symbiotic AI Systems with Fuzzy Rules
No full textArtificial Intelligence (AI) based systems are expanding rapidly in all domains of life. They are entering our everyday life and performing tasks on our behalf. AI-based systems such as personal healthcare assistants are increasingly engaging in close symbiotic relationships with humans. Symbiotic AI (SAI) promises improved outcomes in various domains such as healthcare, education, and business. However, as the degree of symbiosis increases, so does the ethical risk. To ensure that these systems behave ethically and do not cause harm of any kind (physical, mental, violation of privacy, etc.), we need to find ways to assess the ethical risk (risk of causing harm), then choose the right action to mitigate that risk. In this work, we propose an approach based on fuzzy logic for ethical risk assessment (ERA) of SAI systems. The approach is illustrated by means of a case study taken from the healthcare domain
Rethinking Bias and Fairness in AI Through the Lens of Gender Studies
No full textThis paper examines the main approaches that have been put forth to contrast the emergence of biases in AI systems, namely causal, counterfactual reasoning, and constructivist methodology. The objective is to demonstrate the necessity of supplementing this technical solution with a more comprehensive social analysis of the genesis of discriminatory practices. To investigate this sphere, we leverage results from the field of Gender Studies. In particular, we apply the theory of gender performativity as theorized by Judith Butler. This illustrates how AI functions within the social fabric, manifesting patriarchal configurations of gender through an analysis of the notorious case of the COMPAS system for predictive justice. This approach enables an expansion of the interpretation of the concept of fairness, thereby reflecting the complex dynamics of gender production. In conclusion, the gender dimension needs to be reconsidered not as an individual feature but as a performative process. Moreover, it enables the identification of pivotal issues that must be addressed during the design, development, testing, and evaluation phases of AI systems
Gender biases in robots for education
Full text linkEducational robotics is increasingly spreading in schools, also with the aim of fostering young women’s interest in STEM disciplines, particularly in programming and Artificial Intelligence. However, it is crucial to design and select robots that resonate emotionally with female students to overcome gender stereotypes that traditionally deter them from computer science disciplines. This study explores the hypothesis that educational robots should be specifically tailored to meet the expectations and interests of female students. An experiment was conducted with 211 participants, equally divided by gender, who were asked to evaluate images of 16 different educational robots using a semantic differential scale. The results reveal differences between males and females in the attitudes and opinions towards educational robots. While both genders generally rated the robots as more masculine than feminine, female participants tended to provide higher overall scores, except for specific robots. Additionally,
robots that were perceived as more feminine were often rated as simpler whereas masculine robots are associated to the words intelligent and creative, reflecting established societal stereotypes. These insights suggest that educational robots should be designed to appeal to both girls and boys, avoiding reinforcing gender stereotypes and ensuring inclusivity in STEM education. Further research is necessary to explore these attitudes and their implications for fostering a more balanced interest in STEM among both genders
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Combining Knowledge Representation and Machine Learning in Forensics
No full textThis invited talk overviews 20 years of work at the intersection between the two AI areas of Knowledge Representation
(KR) and Machine Learning (ML). The distinguishing feature
of this research is the extension of the methodological apparatus of Inductive Logic Programming (ILP) along a couple
of directions towards the realm of Description Logics (DLs).
One aims at learning hybrid rules that tightly integrate DATALOG and DLs, whereas the other aims at learning axioms
in fuzzy DLs. Both have turned out to be alternative suitable
ways to treat spatial knowledge in several applications and
could be successfully applied also in the field of Forensics
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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