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3Growing research in sign language recognition, generation, and translation AI has been accompanied by calls for ethical development of such technologies. While these works are crucial to helping individual researchers do better, there is a notable lack of discussion of systemic biases or analysis of rhetoric that shape the research questions and methods in the field, especially as it remains dominated by hearing non-signing researchers. Therefore, we conduct a systematic review of 101 recent papers in sign language AI. Our analysis identifies significant biases in the current state of sign language AI research, including an overfocus on addressing perceived communication barriers, a lack of use of representative datasets, use of annotations lacking linguistic foundations, and development of methods that build on flawed models. We take the position that the field lacks meaningful input from Deaf stakeholders, and is instead driven by what decisions are the most convenient or perceived as important to hearing researchers. We end with a call to action: the field must make space for Deaf researchers to lead the conversation in sign language AI.
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In this paper, we report on the initial results of an explorative study that aims to investigate the occurrence of cognitive biases when designers use generative AI in the ideation phase of a creative design process. When observing current AI models utilised as creative design tools, potential negative impacts on creativity can be identified, namely deepening already existing cognitive biases but also introducing new ones that might not have been present before. Within our study, we analysed the emergence of several cognitive biases and the possible appearance of a negative synergy when designers use generative AI tools in a creative ideation process. Additionally, we identified a new potential bias that emerges from interacting with AI tools, namely prompt bias.
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A challenge in digital content marketing is to create meaningful messages on meaningful moments. To do so, brands frequently align social media messages with topical moments, also known as Real-time Marketing (RTM). While RTM aims to make meaningful connections, the creative development is subject to time pressure due to its real-time nature, which could have a negative effect on originality and craftsmanship, two other creativity dimensions besides meaningfulness which drive consumer responses. We address this tension by examining the creative crafting of RTM on Instagram and its consequences. Based on a content analysis of 516 Instagram messages, we indeed found a meaningfulness bias for RTM, such that meaningfulness comes at the expense of originality and craftsmanship. However, the findings from the content analysis, as well as an additional experiment (N = 245), showed that only craftsmanship and originality, and not meaningfulness, positively induced consumer responses. Implications are discussed.
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In dit artikel worden de belangrijkste oorzaken beschreven van problemen bij het bepalen van en het omgaan met risico’s in het veiligheidsdomein: beperkte gecijferdheid en structurele denkfouten (biases). Aan de hand van een procesmodel wordt inzichtelijk gemaakt waar in het analyse- en besluitvormingsproces de schoen wringt. Het model brengt daarbij bestaande modellen van risicomanagement terug tot de twee fasen die voor het analyseren van problema-tische risicobeslissingen cruciaal zijn: de fase van risicoanalyse en die van risicobeslissing. Waar in de subdomeinen fysieke en sociale veiligheid doorgaans geheel eigen analysemodellen en -methoden gangbaar zijn, biedt het hier gepresenteerde procesmodel een generiek toepasbare benadering van problemen met risicobesluitvorming in het domein van de integrale veiligheid als geheel. Het overzicht van knelpunten is opgesteld aan de hand van de onderzoeksliteratuur en wordt geïllustreerd met behulp van praktijkvoorbeelden. Aan het eind van het artikel wordt stilgestaan bij de vraag of er voor de gesignaleerde problemen een remedie kan worden ont-wikkeld.
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Studenten onderwijzen in kritisch denken wordt nationaal en internationaal gezien als een van de centrale doelen van het hoger onderwijs. Docenten spelen een cruciale rol in het realiseren van dit doel. Desondanks is er weinig bekend over hoe zij hierin ondersteund kunnen worden. Het hoofddoel van het promotieonderzoek van de eerste auteur van dit artikel was om een eerste stap te zetten in het beantwoorden van de vraag hoe we docenten kunnen toerusten met de kennis en vaardigheden benodigd voor het onderwijzen van een essentieel aspect van kritisch denken: het vermogen om biases in redeneren en besluitvorming te vermijden. In dit artikel bespreken we aan de hand van een aantal hoofdbevindingen waarom het belangrijk is om docenten ondersteuning te bieden in hun onderwijs in kritisch denken en welke vormen van ondersteuning kunnen helpen.
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There is a need for effective methods to teach critical thinking (CT). One instructional method that seems promising is comparing correct and erroneous worked examples (i.e., contrasting examples). The aim of the present study, therefore, was to investigate the effect of contrasting examples on learning and transfer of CT-skills, focusing on avoiding biased reasoning. Students (N = 170) received instructions on CT and avoiding biases in reasoning tasks, followed by: (1) contrasting examples, (2) correct examples, (3) erroneous examples, or (4) practice problems. Performance was measured on a pretest, immediate posttest, 3-week delayed posttest, and 9-month delayed posttest. Our results revealed that participants’ reasoning task performance improved from pretest to immediate posttest, and even further after a delay (i.e., they learned to avoid biased reasoning). Surprisingly, there were no differences in learning gains or transfer performance between the four conditions. Our findings raise questions about the preconditions of contrasting examples effects. Moreover, how transfer of CT-skills can be fostered remains an important issue for future research.
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Critical thinking is considered to be an important competence for students and graduates of higher education. Yet, it is largely unclear which teaching methods are most effective in supporting the acquisition of critical thinking skills, especially regarding one important aspect of critical thinking: avoiding biased reasoning. The present study examined whether creating desirable difficulties in instruction by prompting students to generate explanations of a problem-solution to themselves (i.e., self-explaining) is effective for fostering learning and transfer of unbiased reasoning. Seventy-nine first-year students of a Dutch Applied University of Sciences were first instructed on two categories of “heuristics and biases” tasks (syllogism and base-rate or Wason and conjunction). Thereafter, they practiced these either with (self-explaining condition) or without (no self-explaining condition) self-explanation prompts that asked them to motivate their answers. Performance was measured on a pretest, immediate posttest, and delayed (2 weeks later) posttest on all four task categories, to examine effects on learning (performance on practiced tasks) and transfer (performance on non-practiced tasks). Participants' learning and transfer performance improved to a comparable degree from pretest to immediate posttest in both conditions, and this higher level of performance was retained on the delayed posttest. Surprisingly, self-explanation prompts had a negative effect on posttest performance on practiced tasks when those were Wason and conjunction tasks, and self-explaining had no effect on transfer performance. These findings suggest that the benefits of explicit instruction and practice on learning and transfer of unbiased reasoning cannot be enhanced by increasing the difficulty of the practice tasks through self-explaining.
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It is essential that higher education students are trained to become critically-thinking professionals. Critical thinking (CT) is crucial for succeeding in future careers and, moreover, is an important life skill (Davies, 2013; Facione, 1990; Halpern, 2014; Van Gelder, 2005). More specifically, students should be able to avoid biases in their reasoning and decision-making, even in situations that have not been encountered before, because especially in (professional) situations, reasoning biases can have serious consequences. However, it would be unfeasible to train students on each and every type of reasoning bias they will ever encounter. The challenge for educational practitioners, therefore, is to design instruction/practice so that students acquire the necessary resources to enhance CT in such a way that it would also transfer across tasks and contexts. The overarching purpose of the research presented in this dissertation was to acquire more knowledge on how higher education students’ learning and transfer of CT-skills can be fostered, focusing specifically on one important aspect of CT: the ability to avoid bias in reasoning.
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The study evaluated two speech recognition systems, Wav2vec2 and Whisper, for potential biases for Dutch speakers.Results obtained by evaluating on the JASMIN corpus revealed biases against non-native speakers, children, and the elderly,with (slightly) better performance for women. The study emphasizes the need for ASR systems to handle variations in speakingin order to reach equal performance among all users.
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Traditional IMU based PDR systems suffer from rapidly growing drift effects due to the inherent bias of the inertial sensor. Many existing solutions to mitigate this problem use aiding sensors or information as heuristics or map data. We propose a new optimization framework to solve the PDR estimation problem where the sensors biases are explicitly included as state variables and therefore be used to correct for bias effects in the PDR. By using a smoothing approach and exploiting the rigid structure of a MIMU array one can solve for the slowly varying sensor biases. This paper presents the method and gives an exemplary result of a walking trial. Good agreements in the position and orientation with an optical reference system were found. Moreover, accelerometer and gyroscope biases could be estimated accordingly. Further research includes the performance of more experiments under various conditions such that a more quantitative evaluation can be obtained. In addition, an exploration of a (pseudo) realtime filter version would be valuable such that the system can be applied online.
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