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Journal articles on the topic 'Trustworthiness'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 March 2025

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1

Yang, Jun Feng. "Study on the Software Trustworthiness Measurement Algorithm Based on the Grey Relational Analysis." Advanced Materials Research 605-607 (December 2012): 2583–86. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2583.

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Under the premise of the software trustworthiness’s understanding is in the grey level, the Software Trustworthiness Measurement get many people’s attention. Use normal method to complete software trustworthiness measure is very hard. By deeply research on the Grey Relational Analysis (GRA) and Software Trustworthiness Measurement Indicator Systems, this paper brings up the Software Trustworthiness Measurement algorithm based on Grey Relational Analysis (GRA), and gives out instances. Experiments showed that this algorithm has theoretical and practical values.

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2

Jones, Karen. "Trustworthiness." Ethics 123, no. 1 (October 2012): 61–85. http://dx.doi.org/10.1086/667838.

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3

Elgin, Catherine Z. "Trustworthiness." Philosophical Papers 37, no. 3 (November 2008): 371–87. http://dx.doi.org/10.1080/05568640809485227.

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4

Cripe, Larry D. "Trustworthiness." Journal of Clinical Oncology 29, no. 25 (September 1, 2011): 3483–86. http://dx.doi.org/10.1200/jco.2011.35.9463.

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5

Hardin, Russell. "Trustworthiness." Ethics 107, no. 1 (October 1996): 26–42. http://dx.doi.org/10.1086/233695.

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6

Dunn, Craig P. "Managerial Trustworthiness." Proceedings of the International Association for Business and Society 8 (1997): 127–38. http://dx.doi.org/10.5840/iabsproc1997816.

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7

Siebert, Matthew Kent. "Testimonial Trustworthiness." American Catholic Philosophical Quarterly 92, no. 2 (2018): 249–76. http://dx.doi.org/10.5840/acpq2018313149.

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Believing someone is, as Elizabeth Anscombe said, “trusting him for the truth.” Recent accounts of how we trust speakers for the truth have given a central role to speaker trustworthiness but have said little about what speaker trustworthiness is. I argue that it is best to think of speaker trustworthiness as the virtue of truthfulness. I give an account of truthfulness, show how that account solves problems for other accounts of speaker trustworthiness, and then use my account to explain the epistemic benefits of trusting a truthful speaker.

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8

Marsden, Lindsay. "Demonstrate trustworthiness." Children and Young People Now 2019, no. 1 (January 2, 2019): 40. http://dx.doi.org/10.12968/cypn.2019.1.40.

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9

Fang, Yifan, Jefferson Ortega, and David Whitney. "Inferential Trustworthiness Tracking Reveals Fast Context-Based Trustworthiness Perception." Journal of Vision 24, no. 10 (September 15, 2024): 1320. http://dx.doi.org/10.1167/jov.24.10.1320.

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10

Zhang, Jin, Jun-fei Liu, Hai-xing Jiao, Yi Shen, and Shu-yuan Liu. "Industry Software Trustworthiness Criterion Research Based on Business Trustworthiness." Physics Procedia 24 (2012): 851–55. http://dx.doi.org/10.1016/j.phpro.2012.02.128.

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11

Winstead, Mark. "Modeling for Trustworthiness." INSIGHT 25, no. 2 (June 2022): 49–50. http://dx.doi.org/10.1002/inst.12386.

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12

Ashton-James, Claire E., and Michael K. Nicholas. "Appearance of trustworthiness." PAIN 157, no. 8 (August 2016): 1583–85. http://dx.doi.org/10.1097/j.pain.0000000000000595.

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13

LE GRAND, JULIAN, RICHARD WILSON, SUZANNE FRANKS, ANDREW BURNS, MARTIN MOORE, and STEVEN BARNETT. "Trustworthiness: the Commentators." Political Quarterly 79, no. 3 (October 8, 2008): 314–23. http://dx.doi.org/10.1111/j.1467-923x.2008.00934.x.

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14

Ermisch, John, and Diego Gambetta. "Income and Trustworthiness." Sociological Science 3 (2016): 710–29. http://dx.doi.org/10.15195/v3.a30.

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15

BARNARD, DAVID. "Vulnerability and Trustworthiness." Cambridge Quarterly of Healthcare Ethics 25, no. 2 (March 9, 2016): 288–300. http://dx.doi.org/10.1017/s0963180115000596.

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Abstract:Although recent literature on professionalism in healthcare abounds in recommended character traits, attitudes, or behaviors, with a few exceptions, the recommendations are untethered to any serious consideration of the contours and ethical demands of the healing relationship. This article offers an approach based on the professional’s commitment to trustworthiness in response to the vulnerability of those seeking professional help. Because our willingness and ability to trust health professionals or healthcare institutions are affected by our personality, culture, race, age, prior experiences with illness and healthcare, and socioeconomic and political circumstances—“the social determinants of trust”—the attitudes and behaviors that actually do gain trust are patient and context specific. Therefore, in addition to the commitment to cultivating attitudes and behaviors that embody trustworthiness, professionalism also includes the commitment to actually gaining a patient’s or family’s trust by learning, through individualized dialogue, which conditions would win their justified trust, given their particular history and social situation.

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16

Lehrer, Keith. "Rationality and Trustworthiness." ProtoSociology 8 (1996): 183–96. http://dx.doi.org/10.5840/protosociology19968/911.

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17

Zimmermann, Ulf. "Trust and Trustworthiness." Public Integrity 5, no. 3 (July 2003): 274–77. http://dx.doi.org/10.1080/15580989.2003.11770952.

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18

Bohnet, Iris, and Rachel Croson. "Trust and trustworthiness." Journal of Economic Behavior & Organization 55, no. 4 (December 2004): 443–45. http://dx.doi.org/10.1016/j.jebo.2003.11.002.

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19

Wright, Stephen. "Trust and Trustworthiness." Philosophia 38, no. 3 (July 31, 2009): 615–27. http://dx.doi.org/10.1007/s11406-009-9218-0.

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20

Bisconti, Piercosma, Letizia Aquilino, Antonella Marchetti, and Daniele Nardi. "A Formal Account of Trustworthiness: Connecting Intrinsic and Perceived Trustworthiness." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 131–40. http://dx.doi.org/10.1609/aies.v7i1.31624.

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This paper proposes a formal account of AI trustworthiness, connecting both intrinsic and perceived trustworthiness in an operational schematization. We argue that trustworthiness extends beyond the inherent capabilities of an AI system to include significant influences from observers' perceptions, such as perceived transparency, agency locus, and human oversight. While the concept of perceived trustworthiness is discussed in the literature, few attempts have been made to connect it with the intrinsic trustworthiness of AI systems. Our analysis introduces a novel schematization to quantify trustworthiness by assessing the discrepancies between expected and observed behaviors and how these affect perceived uncertainty and trust. The paper provides a formalization for measuring trustworthiness, taking into account both perceived and intrinsic characteristics. By detailing the factors that influence trust, this study aims to foster more ethical and widely accepted AI technologies, ensuring they meet both functional and ethical criteria.

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21

Combs, David J. Y., and Peggy S. Keller. "Politicians and Trustworthiness: Acting Contrary to Self-Interest Enhances Trustworthiness." Basic and Applied Social Psychology 32, no. 4 (November 18, 2010): 328–39. http://dx.doi.org/10.1080/01973533.2010.519246.

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22

Hongwei Tao, Hongwei Tao, Yixiang Chen Hongwei Tao, Han Liu Yixiang Chen, Hengyang Wu Han Liu, and Yinghui Hu Hengyang Wu. "Attributes Oriented Software Trustworthiness Measure Based on Axiomatic Approaches." 網際網路技術學刊 23, no. 3 (May 2022): 583–92. http://dx.doi.org/10.53106/160792642022052303016.

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<p>Software trustworthiness measurement can provide evidence for increasing the trustworthiness of the design and implementation of software. We once studied the application of axiomatic approaches in software trustworthiness measurement, presented seven desirable properties of software trustworthiness measure based on attributes, and established three measures. However, none of the three measures can fully satisfy these seven properties. In this paper, we propose an improved software trustworthiness measure based on a partition which satisfies all of the seven properties. Meanwhile, we conduct an empirical validation of this measure by a real case. Comparative study shows that this measure is better than the three measures we once built.</p> <p> </p>

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23

Wilson, John Paul, and Nicholas O. Rule. "Advances in Understanding the Detectability of Trustworthiness From the Face: Toward a Taxonomy of a Multifaceted Construct." Current Directions in Psychological Science 26, no. 4 (August 2017): 396–400. http://dx.doi.org/10.1177/0963721416686211.

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Researchers have recently shown increasing interest in assessments of trustworthiness, devoting much attention to whether trustworthiness can be detected from a person’s facial appearance. This question has been investigated along diverse behavioral dimensions, using a wide variety of targets, and with great inconsistency in results. Here, we call for greater precision in defining trustworthiness. We review various subdomains of trustworthiness perception and argue that developing a more highly specified taxonomy of trustworthiness will allow for better predictions about when trustworthiness can be judged on the basis of appearance, for more precision in estimating how accurate people are in making such judgments, and for more accurate information regarding the specific cues relevant to inferring trustworthiness in each domain.

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24

Gabrieli, Giulio, Sarah Ng, and Gianluca Esposito. "Hacking Trust: The Presence of Faces on Automated Teller Machines (ATMs) Affects Trustworthiness." Behavioral Sciences 11, no. 6 (June 21, 2021): 91. http://dx.doi.org/10.3390/bs11060091.

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Trustworthiness is a core concept that drives individuals’ interaction with others, as well with objects and digital interfaces. The perceived trustworthiness of strangers from the evaluation of their faces has been widely studies in social psychology; however, little is known about the possibility of transferring trustworthiness from human faces to other individuals, objects or interfaces. In this study, we explore how the perceived trustworthiness of automated teller machines (ATMs) is influenced by the presence of faces on the machines, and how the trustworthiness of the faces themselves is transferred to the machine. In our study, participants (N = 57) rated the trustworthiness of ATMs on which faces of different age, gender, and ethnicity are placed. Subsequently, the trustworthiness of the ATMs is compared to the trustworthiness ratings of faces presented on their own. Results of our works support the idea that faces’ trustworthiness can be transferred to objects on which faces are presented. Moreover, the trustworthiness of ATMs seems to be influenced by the age of presented faces, with ATMs on which children faces are presented are trusted more than the same machines when adults’ or elders’ faces are presented, but not by the ethnicity (Asian or Caucasian) or gender (male or female) of presented faces.

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25

Tao, Hongwei, and Jie Zhao. "Source Codes Oriented Software Trustworthiness Measure Based on Validation." Mathematical Problems in Engineering 2018 (November 19, 2018): 1–10. http://dx.doi.org/10.1155/2018/6982821.

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Validation is critical to the success of software trustworthiness measurement. A large number of software trustworthiness measures are proposed; however, most of them are not validated from a theory perspective. Therefore, they lack theoretical foundation and will induce unnecessary cost and useless calculation. In this paper, we bring measurement theory into software trustworthiness measurement, construct a source codes oriented software trustworthiness measure based on extensive structure in the measurement theory, and validate the developed measure by use of axiomatic approaches. Compared with some software trustworthiness measures that are already presented, this measure can evaluate software trustworthiness better from a theory perspective.

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26

Liubyva, Tetiana, and Ol'ha Maksymenko. "A theoretico-methodological understanding of the concept of trustworthiness in sociology and adjacent branches of knowledge." Sociology: Theory, Methods, Marketing, no. 2 (June 2023): 83–103. http://dx.doi.org/10.15407/sociology2023.02.083.

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The paper focuses on the understanding of the trustworthiness concept in various realms of academic knowledge, as well as on possible intersections of these interpretations with the understanding of trustworthiness in sociology. Despite quite a frequent usage of the word “trustworthiness” in daily life and a completely “transparent” etymology of this word (“trustworthy”, that is worthy of trust), there is not an established definition of the term “trustworthiness” in academic literature. For example, V. Shlapentokh defines trustworthiness as a property which characterises the degree of adequate reflection of particular features of the social phenomena and processes under study, whereas V. Volovych regards trustworthiness as a degree of correspondence between the gained knowledge and reality. According to Y. Elez, trustworthiness is the way the truth exists “for us”, the way that reflects the correspondence between the truth and its provenness by a cognizing subject. There may be certain difficulties in translating this term. In English, “trustworthiness” literally means “the quality of being worthy of trust”, but we can also come across the word “credibility”, which can be interpreted as “believability” or “cogency”. Besides, there are a number of other concepts such as “truthfulness”, “verifiability”, “consistency”, “certainty”, etc., which are somewhat related to trustworthiness and have similar meanings. In empirical sociological research, trustworthiness mostly relates to validity and reliability. Y. Lincoln and E. Guba developed a set of criteria for assessing the trustworthiness of qualitative research, which are comparable to validity and reliability in quantitative studies. As for factors influencing the trustworthiness of acquired sociological information, they include the following: a) trust between an interviewer and a respondent (A. Irvine, P. Drew and R. Sainsbury); b) the researcher’s awareness of social, cultural and institutional aspects of the issues in question (V. Lub). The given paper does not pretend to provide an exhaustive coverage of the concept of trustworthiness; however, it can give impetus to the discussion on this topic. For instance, designing statistical indicators that allow assessing quantitatively the trustworthiness of sociological research results can be proposed as the first issue to address.

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27

Tao, Hongwei, Lianyou Fu, Yixiang Chen, Lin Han, and Xiao Wang. "Improved Allocation and Reallocation Approaches for Software Trustworthiness Based on Mathematical Programming." Symmetry 14, no. 3 (March 21, 2022): 628. http://dx.doi.org/10.3390/sym14030628.

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Software trustworthiness allocation and reallocation are the symmetry of software trustworthiness measure. They can provide the optimization scheme for trustworthiness development and improvement, according to the requirements. The existing allocation and reallocation models do not consider the absolute majority of software trustworthiness classification; therefore, they cannot be very accurate in the allocation and reallocation of software trustworthiness. In this paper, improved allocation and reallocation models are constructed, which can resolve the above problem, and their polynomial solving algorithms are designed. At the same time, a demonstration application of the improved models and algorithms is given, and the trustworthiness enhancement specification of spacecraft software, based on factory reports, is established, including trustworthiness development specification and trustworthiness improvement specification. This enhancement specification provides a scientific and reasonable theory and method for the delivery acceptance of spacecraft software, from qualitative to quantitative grading acceptance, and furnishes a standard guarantee for the trustworthy development and improvement of such software.

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28

Wang, Shuo, Virginia Falvello, Jenny Porter, Christopher P. Said, and Alexander Todorov. "Behavioral and Neural Adaptation in Approach Behavior." Journal of Cognitive Neuroscience 30, no. 6 (June 2018): 885–97. http://dx.doi.org/10.1162/jocn_a_01243.

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People often make approachability decisions based on perceived facial trustworthiness. However, it remains unclear how people learn trustworthiness from a population of faces and whether this learning influences their approachability decisions. Here we investigated the neural underpinning of approach behavior and tested two important hypotheses: whether the amygdala adapts to different trustworthiness ranges and whether the amygdala is modulated by task instructions and evaluative goals. We showed that participants adapted to the stimulus range of perceived trustworthiness when making approach decisions and that these decisions were further modulated by the social context. The right amygdala showed both linear response and quadratic response to trustworthiness level, as observed in prior studies. Notably, the amygdala's response to trustworthiness was not modulated by stimulus range or social context, a possible neural dynamic adaptation. Together, our data have revealed a robust behavioral adaptation to different trustworthiness ranges as well as a neural substrate underlying approach behavior based on perceived facial trustworthiness.

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29

Mittal, Akhil. "Trustworthiness of Big Data." International Journal of Computer Applications 80, no. 9 (October 18, 2013): 35–40. http://dx.doi.org/10.5120/13892-1835.

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30

Ivanova, Anna. "The Trustworthiness of Science." Dialogue and Universalism 27, no. 3 (2017): 213–20. http://dx.doi.org/10.5840/du201727356.

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31

Neumann, Peter G. "Toward total-system trustworthiness." Communications of the ACM 65, no. 6 (June 2022): 32–35. http://dx.doi.org/10.1145/3532631.

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32

Fettweis, Gerhard P., and Holger Buche. "On 6G and trustworthiness." Communications of the ACM 65, no. 4 (April 2022): 48–49. http://dx.doi.org/10.1145/3512996.

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33

Hand, David J. "Trustworthiness of statistical inference." Journal of the Royal Statistical Society: Series A (Statistics in Society) 185, no. 1 (October 12, 2021): 329–47. http://dx.doi.org/10.1111/rssa.12752.

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34

O’Neill, Onora. "Linking Trust to Trustworthiness." International Journal of Philosophical Studies 26, no. 2 (March 15, 2018): 293–300. http://dx.doi.org/10.1080/09672559.2018.1454637.

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35

Todorov, Alexander. "Evaluating Faces on Trustworthiness." Annals of the New York Academy of Sciences 1124, no. 1 (March 2008): 208–24. http://dx.doi.org/10.1196/annals.1440.012.

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36

Bellucci, Gabriele, and Soyoung Q. Park. "Honesty biases trustworthiness impressions." Journal of Experimental Psychology: General 149, no. 8 (August 2020): 1567–86. http://dx.doi.org/10.1037/xge0000730.

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37

Belot, Michèle, V. Bhaskar, and Jeroen van de Ven. "Can Observers Predict Trustworthiness?" Review of Economics and Statistics 94, no. 1 (February 2012): 246–59. http://dx.doi.org/10.1162/rest_a_00146.

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38

Cox, Landon P. "Usefulness Is Not Trustworthiness." IEEE Internet Computing 15, no. 3 (May 2011): 79–80. http://dx.doi.org/10.1109/mic.2011.69.

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39

Weibel, Antoinette. "Formal Control and Trustworthiness." Group & Organization Management 32, no. 4 (August 2007): 500–517. http://dx.doi.org/10.1177/1059601106293961.

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40

Merrie, Laureon A., Jaimie Arona Krems, Daniel Sznycer, and Nina N. Rodriguez. "Trustworthiness: an adaptationist account." Evolution and Human Behavior 46, no. 1 (January 2025): 106648. https://doi.org/10.1016/j.evolhumbehav.2024.106648.

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41

SEATON, JEAN. "Trustworthiness: Ancient and Modern." Political Quarterly 79, no. 3 (October 8, 2008): 300–302. http://dx.doi.org/10.1111/j.1467-923x.2008.00932.x.

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42

Hausman, Daniel M. "Trustworthiness and self-interest." Journal of Banking & Finance 26, no. 9 (September 2002): 1767–83. http://dx.doi.org/10.1016/s0378-4266(02)00190-5.

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43

Srivastava, Biplav, Francesca Rossi, Sheema Usmani, and Mariana Bernagozzi. "Personalized Chatbot Trustworthiness Ratings." IEEE Transactions on Technology and Society 1, no. 4 (December 2020): 184–92. http://dx.doi.org/10.1109/tts.2020.3023919.

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44

Fitzmaurice, Garrett. "The trustworthiness of surveys." Nutrition 20, no. 10 (October 2004): 949. http://dx.doi.org/10.1016/j.nut.2004.06.018.

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45

Simpson, Thomas W. "Trustworthiness and Moral Character." Ethical Theory and Moral Practice 16, no. 3 (June 30, 2012): 543–57. http://dx.doi.org/10.1007/s10677-012-9373-4.

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46

Junfeng, Tian, Xiao Bing, Wang Zixian, and Zhang Yuzhu. "Trustworthiness technologies of DDSS." Wuhan University Journal of Natural Sciences 11, no. 6 (November 2006): 1853–56. http://dx.doi.org/10.1007/bf02831891.

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47

Ashraf, Nava, Iris Bohnet, and Nikita Piankov. "Decomposing trust and trustworthiness." Experimental Economics 9, no. 3 (September 2006): 193–208. http://dx.doi.org/10.1007/s10683-006-9122-4.

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48

Levi, Margaret, and Laura Stoker. "Political Trust and Trustworthiness." Annual Review of Political Science 3, no. 1 (June 2000): 475–507. http://dx.doi.org/10.1146/annurev.polisci.3.1.475.

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49

Hu, Donghui, Xiaotian Zhang, Yuqi Fan, Zhong-Qiu Zhao, Lina Wang, Xintao Wu, and Xindong Wu. "On digital image trustworthiness." Applied Soft Computing 48 (November 2016): 240–53. http://dx.doi.org/10.1016/j.asoc.2016.07.010.

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50

Haddad, Amy. "Trustworthiness and Professional Ethics." Journal of the American Pharmaceutical Association (1996) 42, no. 4 (July 2002): 540–41. http://dx.doi.org/10.1331/108658002763029481.

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