Four investment areas for ethical AI: Transdisciplinary opportunities to close the publication-to-practice gap

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110401
Author(s):  
Jana Schaich Borg
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Positive Impact ◽  
Real Life ◽  
Symbiotic Relationship ◽  
Social Good ◽  
Data Problem ◽  
Practice Gap ◽  
The Creation ◽  
Transdisciplinary Collaboration

Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence are therefore a Big Data problem, at least to some degree. Efforts to address this problem have been dominated by the documentation of Ethical Artificial Intelligence principles and the creation of technical tools that address specific aspects of those principles. However, there is mounting evidence that Ethical Artificial Intelligence principles and technical tools have little impact on the Artificial Intelligence that is created in practice, sometimes in very public ways. The goal of this commentary is to highlight four interconnected areas society can invest in to close this Ethical Artificial Intelligence publication-to-practice gap, maximizing the positive impact Artificial Intelligence and Big Data have on society. For Ethical Artificial Intelligence to become a reality, I argue that these areas need to be addressed holistically in a way that acknowledges their interdependencies. Progress will require iteration, compromise, and transdisciplinary collaboration, but the result of our investments will be the realization of Artificial Intelligence's and Big Data's tremendous potential for social good, in practice rather than in just our hopes and aspirations.

scholarly journals The Impact of Big Data Processing Framework for Artificial Intelligence within Corporate Marketing Communication

2018 ◽  
Vol 7 (4.34) ◽  
pp. 384
Author(s):  
Muhamad Fazil Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Positive Impact ◽  
Marketing Communication ◽  
Short Term ◽  
The Impact ◽  
Processing Framework

This research examines what impact the Big Data Processing Framework (BDPF) has on Artificial Intelligence (AI) applications within Corporate Marketing Communication (CMC), and thereby the research question stated is: What is the potential impact of the BDPF on AI applications within the CMC tactical and managerial functions? To fulfill the purpose of this research, a qualitative research strategy was applied, including semi-structured interviews with experts within the different fields of examination: management, AI technology and CMC. The findings were analyzed through performing a thematic analysis, where coding was conducted in two steps. AI has many useful applications within CMC, which currently mainly are of the basic form of AI, so-called rule-based systems. However, the more complicated communication systems are used in some areas. Based on these findings, the impact of the BDPF on AI applications is assessed by examining different characteristics of the processing frameworks. The BDPF initially imposes both an administrative and compliance burden on organizations within this industry, and is particularly severe when machine learning is used. These burdens foremost stem from the general restriction of processing personal data and the data erasure requirement. However, in the long term, these burdens instead contribute to a positive impact on machine learning. The timeframe until enforcement contributes to a somewhat negative impact in the short term, which is also true for the uncertainty around interpretations of the BDPF requirements. Yet, the BDPF provides flexibility in how to become compliant, which is favorable for AI applications. Finally, BDPF compliance can increase company value, and thereby incentivize investments into AI models of higher transparency. The impact of the BDPF is quite insignificant for the basic forms of AI applications, which are currently most common within CMC. However, for the more complicated applications that are used, the BDPF is found to have a more severe negative impact in the short term, while it instead has a positive impact in the long term.   

Can Artificial Intelligence and Big Data Improve Gamified Healthcare Services and Devices?

2022 ◽  
pp. 205-220
Author(s):  
María A. Pérez-Juárez ◽  
Javier M. Aguiar-Pérez ◽  
Javier Del-Pozo-Velázquez ◽  
Miguel Alonso-Felipe ◽  
Saúl Rozada-Raneros ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Essential Role ◽  
Healthy Lifestyle ◽  
Positive Impact ◽  
Well Being ◽  
Healthcare Services ◽  
Personalized Care ◽  
Ehealth Services ◽  
Mental Well Being

Systems that aim to maintain and improve the health of citizens are steadily gaining importance. Digital transformation is having a positive impact on healthcare. Gamification motivates individuals to maintain and improve their physical and mental well-being. In the era of artificial intelligence and big data, healthcare is not only digital, but also predictive, proactive, and preventive. Big data and artificial intelligence techniques are called to play an essential role in gamified eHealth services and devices allowing to offer personalized care. This chapter aims to explore the possibilities of artificial intelligence and big data techniques to support and improve gamified eHealth services and devices, including wearable technology, which are essential for digital natives but also increasingly important for digital immigrants. These services and devices can play an important role in the prevention and diagnosis of diseases, in the treatment of illnesses, and in the promotion of healthy lifestyle habits.

Ex Machina in the Garden

2016 ◽  
Vol 69 (4) ◽  
pp. 23-34 ◽  
Author(s):  
Brian R. Jacobson
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Technological Change ◽  
Digital Age ◽  
Science Technology ◽  
History Of Art ◽  
History Of ◽  
The Creation ◽  
Technological Means

Alex Garland's Ex Machina (2015) explores both long-standing discourses about artificial intelligence and more recent concerns about automation, surveillance, and big data. It does so by associating AI creation not solely with science, technology, and religion but also with the history of art and, more reflexively, with film itself. In this way, the film becomes an allegory for its own production, a story about representation and the creation of artificial film worlds by new technological means. This reflexivity underscores cinema's important role in popular discourses about technological change, a role it has long served as a “technocritical art.” AI films like Ex Machina suggest that this role is changing as film enters not just the digital age but also what W.J.T. Mitchell terms the age of biocybernetics.

scholarly journals Does Project-Based Learning (PBL) Promote Student Learning? A Performance Evaluation

2020 ◽  
Vol 10 (11) ◽  
pp. 330 ◽  
Author(s):  
Bertha Ngereja ◽  
Bassam Hussein ◽  
Bjørn Andersen
Keyword(s):  
Performance Evaluation ◽  
Project Management ◽  
Student Learning ◽  
Positive Impact ◽  
Educational Institution ◽  
Real Life ◽  
Project Based Learning ◽  
Measurement Scales ◽  
The Creation ◽  
A Performance

The purpose of the study on which this paper is based was to conduct a performance evaluation of student learning for an introductory course in project management in a higher educational institution in Norway. This was done by utilizing performance measurement philosophy to evaluate perceived student learning after a project-based assignment was applied as an instructional tool. The evaluation was conducted at the end of the semester to determine whether it facilitated learning effectiveness by providing an authentic learning experience. Relevant learning criteria were identified from existing literature and were measured by means of a questionnaire survey. Ten measurement scales were established using a 5-point Likert scale. The survey was then rolled out for the same subject for two consecutive semesters for just over 100 project management students. The results indicated that the incorporation of project-based assignments has a positive impact on student learning, motivation, and performance both in the short and long term. The study finally revealed that the incorporation of project-based assignments enables the creation of real-life experiences, which further stimulates the creation and development of real-life competencies.

Trading faces: Complete AI face doubles avoid the uncanny valley

2020 ◽  
Author(s):  
Christopher Welker ◽  
David France ◽  
Alice Henty ◽  
Thalia Wheatley
Keyword(s):  
Artificial Intelligence ◽  
Sexual Orientation ◽  
Uncanny Valley ◽  
Torture Survivors ◽  
The Uncanny ◽  
The Creation ◽  
Full Face ◽  
The Impact

Advances in artificial intelligence (AI) enable the creation of videos in which a person appears to say or do things they did not. The impact of these so-called “deepfakes” hinges on their perceived realness. Here we tested different versions of deepfake faces for Welcome to Chechnya, a documentary that used face swaps to protect the privacy of Chechen torture survivors who were persecuted because of their sexual orientation. AI face swaps that replace an entire face with another were perceived as more human-like and less unsettling compared to partial face swaps that left the survivors’ original eyes unaltered. The full-face swap was deemed the least unsettling even in comparison to the original (unaltered) face. When rendered in full, AI face swaps can appear human and avoid aversive responses in the viewer associated with the uncanny valley.

A Study on the Development of Appointment Algorithm of CT With Big Data using AI (Artificial Intelligence)

2018 ◽  
Vol 20 (2) ◽  
pp. 1-5
Author(s):  
Sang-ho Jeon ◽  
Sung-yeul Yang ◽  
In-beom Shin ◽  
Dae-mok Son ◽  
Tae-han Kwon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data

Enhancing statistical literacy skills through real life activities enriched with gamification elements: An experimental study

2021 ◽  
pp. 204275302098701
Author(s):  
Ünal Çakıroğlu ◽  
Mustafa Güler
Keyword(s):  
Experimental Study ◽  
Experimental Design ◽  
Positive Impact ◽  
Real Life ◽  
Literacy Skills ◽  
Statistical Literacy ◽  
Instructional Process ◽  
Post Test ◽  
Quasi Experimental ◽  
Teaching Statistics

This study attempts to determine whether gamification can be used as a pedagogical technique to overcome the challenges in teaching statistics. A post-test quasi-experimental design was carried out in gamified and non-gamified groups in order to reveal the effect of gamification elements in cultivating students’ statistical literacy skills. Students in gamified group were also interviewed to understand the function of gamification process. The results suggest that; although gamifying the instructional process had a positive impact on developing students’ statistical literacy in medium and high score students; surprisingly the influence of the gamification to the low- achieved scores were not positive. The positive impact was discussed in accordance with the gradual structure of statistical literacy and suggestions for successful gamification applications due to the context were included.

scholarly journals Mapping value sensitive design onto AI for social good principles

AI and Ethics ◽  
2021 ◽  
Author(s):  
Steven Umbrello ◽  
Ibo van de Poel
Keyword(s):  
Artificial Intelligence ◽  
Contact Tracing ◽  
Use Case ◽  
Value Sensitive Design ◽  
Specific Design ◽  
Established Method ◽  
Social Good ◽  
Do No Harm ◽  
Design Requirements ◽  
Whole Life Cycle

AbstractValue sensitive design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: (1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; (2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good, and (3) extending the VSD process to encompass the whole life cycle of an AI technology to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app.

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