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Journal articles on the topic 'AI'

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Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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1

albarracíín, pilar. "Ai, Ai, Ai." Gastronomica 8, no. 4 (2008): 122. http://dx.doi.org/10.1525/gfc.2008.8.4.122.

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2

Euchner, Jim. "Little ai, Big AI—Good AI, Bad AI." Research-Technology Management 62, no. 3 (May 4, 2019): 10–12. http://dx.doi.org/10.1080/08956308.2019.1587280.

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3

Baraheem, Samah S., and Tam V. Nguyen. "AI vs. AI: Can AI Detect AI-Generated Images?" Journal of Imaging 9, no. 10 (September 28, 2023): 199. http://dx.doi.org/10.3390/jimaging9100199.

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The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This might have the ability to improve content and media; however, it also constitutes a threat with regard to legitimacy, authenticity, and security. Moreover, implementing an automated system that is able to detect and recognize GAN-generated images is significant for image synthesis models as an evaluation tool, regardless of the input modality. To this end, we propose a framework for reliably detecting AI-generated images from real ones through Convolutional Neural Networks (CNNs). First, GAN-generated images were collected based on different tasks and different architectures to help with the generalization. Then, transfer learning was applied. Finally, several Class Activation Maps (CAM) were integrated to determine the discriminative regions that guided the classification model in its decision. Our approach achieved 100% on our dataset, i.e., Real or Synthetic Images (RSI), and a superior performance on other datasets and configurations in terms of its accuracy. Hence, it can be used as an evaluation tool in image generation. Our best detector was a pre-trained EfficientNetB4 fine-tuned on our dataset with a batch size of 64 and an initial learning rate of 0.001 for 20 epochs. Adam was used as an optimizer, and learning rate reduction along with data augmentation were incorporated.
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4

Holzinger, Andreas. "Explainable AI (ex-AI)." Informatik-Spektrum 41, no. 2 (April 2018): 138–43. http://dx.doi.org/10.1007/s00287-018-1102-5.

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5

Keller, Jim. "AI-eXplained (AI-X)." IEEE Computational Intelligence Magazine 17, no. 4 (November 1, 2022): 3–4. http://dx.doi.org/10.1109/mci.2022.3201735.

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6

Baungarten-Leon, Emilio Isaac, Susana Ortega-Cisneros, Mohamed Abdelmoneum, Ruth Yadira Vidana Morales, and German Pinedo-Diaz. "The Genesis of AI by AI Integrated Circuit: Where AI Creates AI." Electronics 13, no. 9 (April 28, 2024): 1704. http://dx.doi.org/10.3390/electronics13091704.

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The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis (HLS), on the other hand, converts programming languages to HDL; these methods aim to streamline the engineering process, minimizing human effort and errors. Currently, Electronic Design Automation (EDA) algorithms have been improved with the use of AI, with new advancements in commercial (such as ChatGPT, Bard, among others) Large Language Models (LLM) and open-source tools presenting an opportunity to automate the chip design process. This paper centers on the creation of AI by AI, a Convolutional Neural Network (CNN) IC entirely developed by an LLM (ChatGPT-4), and its manufacturing with the first fabricable open-source Process Design Kit (PDK), SKY130A. The challenges, opportunities, advantages, disadvantages, conversation flow, and workflow involved in CNN IC development are presented in this work, culminating in the manufacturing process of AI by AI using a 130 nm technology, marking a groundbreaking achievement as possibly the world’s first CNN entirely written by AI for its IC manufacturing with a free PDK, being a benchmark for systems that can be generated today with LLMs.
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Kortz, Mason, Jessica Fjeld, Hannah Hilligoss, and Adam Nagy. "Is Lawful AI Ethical AI?" Morals & Machines 2, no. 1 (2022): 60–65. http://dx.doi.org/10.5771/2747-5174-2022-1-60.

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Attempts to impose moral constraints on autonomous, artificial decision-making systems range from “human in the loop” requirements to specialized languages for machine-readable moral rules. Regardless of the approach, though, such proposals all face the challenge that moral standards are not universal. It is tempting to use lawfulness as a proxy for morality; unlike moral rules, laws are usually explicitly defined and recorded – and they are usually at least roughly compatible with local moral norms. However, lawfulness is a highly abstracted and, thus, imperfect substitute for morality, and it should be relied on only with appropriate caution. In this paper, we argue that law-abiding AI systems are a more achievable goal than moral ones. At the same time, we argue that it’s important to understand the multiple layers of abstraction, legal and algorithmic, that underlie even the simplest AI-enabled decisions. The ultimate output of such a system may be far removed from the original intention and may not comport with the moral principles to which it was meant to adhere. Therefore, caution is required lest we develop AI systems that are technically law-abiding but still enable amoral or immoral conduct.
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La Rosa, Michele, and Enrica Morlicchio. "Ai collaboratori e ai lettori." SOCIOLOGIA DEL LAVORO, no. 153 (March 2019): 7. http://dx.doi.org/10.3280/sl2019-153001.

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9

BIRMINGHAM, WILLIAM P. "The AI in AI EDAM." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 1 (January 2007): 5–6. http://dx.doi.org/10.1017/s0890060407070035.

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A natural question is why AI in design? Although the design applications written about in the journal vary widely, the common thread is that researchers use AI techniques to implement their ideas. The use of AI techniques for design applications, at least when AI EDAM was started, was partially a reaction against the predominant design methods based on some form of optimization. Knowledge-based techniques, particularly rule-based systems of various sorts, were very popular. One of the draws of these methods, I believe, was their ability to represent knowledge that is hard or awkward to represent in traditional optimization frameworks. This mirrors my experience: at the time, I was working in configuration with components that had a large number compatibility and resource constraints. Although many constraints could be represented in mixed integer linear programming systems, it was not easy to conceptualize, write, and most importantly, maintain the constraints in those systems.
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10

笹嶋, 宗彦. "AI戦略（AI Strategy）." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 35, no. 2 (May 15, 2023): 25. http://dx.doi.org/10.3156/jsoft.35.2_25_1.

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11

Manning, Ryan Vincent. "Tech Limited: AI is AI." Architectural Design 94, no. 3 (May 2024): 94–101. http://dx.doi.org/10.1002/ad.3060.

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AbstractWhat might a conversation between a highly trained generator that calculates the probability of the next word in a sentence, and the jumbled mess of organic neurons inside the head of a human architect look like? Through such a fictitious dialogue, architectural designer and educator Ryan Vincent Manning explores issues of human inquisitiveness, uniqueness and agency, human‐ machine interfaces, machine intelligence, AI latent spaces and the assimilation of design originality into free‐access, ubiquitous machine code that is adding to huge potential datasets.
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12

van Wynsberghe, Aimee. "Sustainable AI: AI for sustainability and the sustainability of AI." AI and Ethics 1, no. 3 (February 26, 2021): 213–18. http://dx.doi.org/10.1007/s43681-021-00043-6.

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AbstractWhile there is a growing effort towards AI for Sustainability (e.g. towards the sustainable development goals) it is time to move beyond that and to address the sustainability of developing and using AI systems. In this paper I propose a definition of Sustainable AI; Sustainable AI is a movement to foster change in the entire lifecycle of AI products (i.e. idea generation, training, re-tuning, implementation, governance) towards greater ecological integrity and social justice. As such, Sustainable AI is focused on more than AI applications; rather, it addresses the whole sociotechnical system of AI. I have suggested here that Sustainable AI is not about how to sustain the development of AI per say but it is about how to develop AI that is compatible with sustaining environmental resources for current and future generations; economic models for societies; and societal values that are fundamental to a given society. I have articulated that the phrase Sustainable AI be understood as having two branches; AI for sustainability and sustainability of AI (e.g. reduction of carbon emissions and computing power). I propose that Sustainable AI take sustainable development at the core of its definition with three accompanying tensions between AI innovation and equitable resource distribution; inter and intra-generational justice; and, between environment, society, and economy. This paper is not meant to engage with each of the three pillars of sustainability (i.e. social, economic, environment), and as such the pillars of sustainable AI. Rather, this paper is meant to inspire the reader, the policy maker, the AI ethicist, the AI developer to connect with the environment—to remember that there are environmental costs to AI. Further, to direct funding towards sustainable methods of AI.
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13

Janssen, T. "SP-0541 AI^2: When the AI checks the AI." Radiotherapy and Oncology 182 (May 2023): S427. http://dx.doi.org/10.1016/s0167-8140(23)67400-6.

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14

Bokdash, Saed. "AI-Imam AI- Shaikh M. Aabid AI- Sendi AI-Ansari and his Scientific and.Feqhia Efforts." Journal of King Abdulaziz University-Educational Sciences 16, no. 1 (2003): 95–191. http://dx.doi.org/10.4197/edu.16-1.6.

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15

Miller, David P., R. James Firby, Paul A. Fishwick, and Jeff Rothenberg. "AI." ACM Transactions on Modeling and Computer Simulation 2, no. 4 (October 1992): 269–84. http://dx.doi.org/10.1145/149516.149519.

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16

Clancey, William J. "AI." ACM Computing Surveys 27, no. 3 (September 1995): 320–22. http://dx.doi.org/10.1145/212094.212110.

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17

Wu, Xianchao. "When Creative AI Meets Conversational AI." Journal of Natural Language Processing 28, no. 3 (2021): 881–87. http://dx.doi.org/10.5715/jnlp.28.881.

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18

Moehle, Matthew R., Roopa Nandi, and Hardik Shah. "AI Practitioner November 2013 - AI Resources." AI Practitioner 15, no. 3 (November 1, 2013): 71–75. http://dx.doi.org/10.12781/978-1-907549-17-5-10.

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19

Vaux, Janet. "AI is dead; long live AI." Expert Systems 11, no. 1 (February 1994): 49. http://dx.doi.org/10.1111/j.1468-0394.1994.tb00317.x.

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20

Johnson, Deborah G., and Mario Verdicchio. "Ethical AI is Not about AI." Communications of the ACM 66, no. 2 (January 20, 2023): 32–34. http://dx.doi.org/10.1145/3576932.

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21

Hollands, Fiona, and Cynthia Breazeal. "Establishing AI Literacy before Adopting AI." Science Teacher 91, no. 2 (March 3, 2024): 35–42. http://dx.doi.org/10.1080/00368555.2024.2308316.

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22

Goel, Ashok K., and David A. Joyner. "Using AI to Teach AI: Lessons from an Online AI Class." AI Magazine 38, no. 2 (July 1, 2017): 48–59. http://dx.doi.org/10.1609/aimag.v38i2.2732.

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In fall 2014, we launched a foundational course in artificial intelligence (CS7637: Knowledge-Based AI) as part of the Georgia Institute of Technology's Online Master of Science in Computer Science program. We incorporated principles and practices from the cognitive and learning sciences into the development of the online AI course. We also integrated AI techniques into the instruction of the course, including embedding 100 highly focused intelligent tutoring agents in the video lessons. By now, more than 2000 students have taken the course. Evaluations have indicated that OMSCS students enjoy the course compared to traditional courses, and more importantly, that online students have matched residential students' performance on the same assessments. In this article, we present the design, delivery, and evaluation of the course, focusing on the use of AI for teaching AI. We also discuss lessons we learned for scaling the teaching and learning of AI.
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23

Miyamoto, Michiko. "Measuring AI Governance, AI Adoption and AI Strategy of Japanese Companies." International Journal of Membrane Science and Technology 10, no. 1 (October 11, 2023): 649–57. http://dx.doi.org/10.15379/ijmst.v10i1.2627.

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Purpose: This study aims to measure the level of AI governance and AI adoption among Japanese companies. Theoretical Framework: The research investigates the extent to which Japanese companies have implemented AI governance frameworks and the degree of AI adoption in their operations. The study also explores the relationship between AI governance, AI adoption, and AI strategy, providing insights into the factors that influence successful AI implementation. Design / Methodology / Approach: a survey questionnaire was administered to a representative sample of Japanese companies across various industries. The questionnaire included items that assessed the presence and effectiveness of AI governance practices within the organizations. Findings: a positive correlation was observed between AI governance and AI adoption. Companies with well-established AI governance frameworks tended to have higher levels of AI adoption, suggesting that effective governance practices play a crucial role in facilitating successful AI implementation. These findings provide valuable insights into the current state of AI governance and AI adoption among Japanese companies. Conclusion: The results can assist organizations in benchmarking their AI initiatives against industry standards and identifying areas for improvement. Policymakers and regulators can also utilize these findings to develop guidelines and frameworks that promote responsible and effective AI implementation.
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24

Yoo, Sunghee. "Ethical Issues Posed by ‘Generative-AI’ (G-AI) - Response strategies for ‘Good AI Society’." Journal of the Korean Bioethics Association 24, no. 1 (June 30, 2023): 1–29. http://dx.doi.org/10.37305/jkba.2023.06.24.1.1.

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25

Pakdemirli, Ahu, and Asim Leblebici. "AI in Medicine versus AI in Prehospital." Medicine Science | International Medical Journal 9, no. 2 (2020): 293. http://dx.doi.org/10.5455/medscience.2020.09.9186.

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26

Dodge, Jonathan, Roli Khanna, Jed Irvine, Kin-ho Lam, Theresa Mai, Zhengxian Lin, Nicholas Kiddle, et al. "After-Action Review for AI (AAR/AI)." ACM Transactions on Interactive Intelligent Systems 11, no. 3-4 (December 31, 2021): 1–35. http://dx.doi.org/10.1145/3453173.

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Explainable AI is growing in importance as AI pervades modern society, but few have studied how explainable AI can directly support people trying to assess an AI agent. Without a rigorous process, people may approach assessment in ad hoc ways—leading to the possibility of wide variations in assessment of the same agent due only to variations in their processes. AAR, or After-Action Review, is a method some military organizations use to assess human agents, and it has been validated in many domains. Drawing upon this strategy, we derived an After-Action Review for AI (AAR/AI), to organize ways people assess reinforcement learning agents in a sequential decision-making environment. We then investigated what AAR/AI brought to human assessors in two qualitative studies. The first investigated AAR/AI to gather formative information, and the second built upon the results, and also varied the type of explanation (model-free vs. model-based) used in the AAR/AI process. Among the results were the following: (1) participants reporting that AAR/AI helped to organize their thoughts and think logically about the agent, (2) AAR/AI encouraged participants to reason about the agent from a wide range of perspectives , and (3) participants were able to leverage AAR/AI with the model-based explanations to falsify the agent’s predictions.
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Pogue, David. "I'll Have My AI Call Your AI." Scientific American 319, no. 2 (July 17, 2018): 26. http://dx.doi.org/10.1038/scientificamerican0818-26.

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28

Baj, Fabio, Paolo Cattaneo, and Mike Rosner. "Practicing AI with the portable AI lab." ACM SIGPLAN Lisp Pointers VI, no. 3 (July 1993): 44–53. http://dx.doi.org/10.1145/174169.174179.

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29

Zachary, G. Pascal. "Let's shape AI before AI shapes us." IEEE Spectrum 52, no. 7 (July 2015): 8. http://dx.doi.org/10.1109/mspec.2015.7131675.

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Bauer, Grace. "The Collected Poems of Ai by Ai." Prairie Schooner 88, no. 1 (2014): 169–76. http://dx.doi.org/10.1353/psg.2014.0004.

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31

Prado, Miguel De, Jing Su, Rabia Saeed, Lorenzo Keller, Noelia Vallez, Andrew Anderson, David Gregg, et al. "Bonseyes AI Pipeline—Bringing AI to You." ACM Transactions on Internet of Things 1, no. 4 (October 5, 2020): 1–25. http://dx.doi.org/10.1145/3403572.

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32

Bosch, Jan, Helena Holmstrom Olsson, Bjorn Brinne, and Ivica Crnkovic. "AI Engineering: Realizing the Potential of AI." IEEE Software 39, no. 6 (November 2022): 23–27. http://dx.doi.org/10.1109/ms.2022.3199621.

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33

Smolensky, P. "Connectionist AI, symbolic AI, and the brain." Artificial Intelligence Review 1, no. 2 (1987): 95–109. http://dx.doi.org/10.1007/bf00130011.

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34

Harfouche, Antoine, Bernard Quinio, and Francesca Bugiotti. "Human-Centric AI to Mitigate AI Biases." Journal of Global Information Management 31, no. 5 (October 9, 2023): 1–23. http://dx.doi.org/10.4018/jgim.331755.

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The global health crisis represents an unprecedented opportunity for the development of artificial intelligence (AI) solutions. This article aims to tackle part of the biases in artificial intelligence by implementing a human-centric AI to help decision-makers in organizations. It relies on the results of two design science research (DSR) projects: SCHOPPER and VRAILEXIA. These two design projects operationalize the human-centric AI approach with two complementary stages: 1) the first installs a human-in-loop informed design process, and 2) the second implements a usage architecture that aggregates AI and humans. The proposed framework offers many advantages such as permitting to integrate of human knowledge into the design and training of the AI, providing humans with an understandable explanation of their predictions, and driving the advent of augmented intelligence that can turn algorithms into a powerful counterweight to human decision-making errors and humans as a counterweight to AI biases.
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35

Schneier, Bruce. "Trustworthy AI Means Public AI [Last Word]." IEEE Security & Privacy 21, no. 6 (November 2023): 95–96. http://dx.doi.org/10.1109/msec.2023.3301262.

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36

Yoon, Dae-yeob. "Competition of AI Weaponization and AI RMA." Korean Journal of International Relations 64, no. 1 (March 31, 2024): 333–69. http://dx.doi.org/10.14731/kjir.2024.03.64.1.333.

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37

Müller, Manuel E. B., and Matthias C. Laupichler. "Medical students learning about AI – with AI?" Medical Education 57, no. 11 (September 15, 2023): 1156. http://dx.doi.org/10.1111/medu.15211.

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38

Verma, Umika. "A journey from AI to Gen-AI." Spectrum of Emerging Sciences 4, no. 1 (February 1, 2024): 74–78. http://dx.doi.org/10.55878/ses2024-4-1-14.

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The history of artificial intelligence (AI), from its conception to the creation of general AI (Gen-AI), is a fascinating story of human inventiveness, technical growth, and philosophical research. This article examines the historical milestones, major inventions, and transformational concepts that have influenced AI's trajectory. Beginning with early symbolic AI and rule-based systems, it investigates the shift to machine learning, highlighting discoveries in neural networks and deep learning that transformed disciplines such as computer vision and natural language processing. The introduction of generative models, such as GANs and VAEs, resulted in a considerable increase in AI capabilities, paving the path for Gen-AI. Unlike narrow AI, Gen-AI strives to imitate human-like intelligence and adaptability across a wide range of jobs, bringing serious ethical and philosophical concerns. This essay also looks at the current state of Gen-AI, its problems, and possible applications in healthcare, education, finance, and other areas. It finishes by picturing a future in which human-machine collaboration and ethical AI development are prioritized, highlighting the need of continual learning and responsible innovation.
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Park, SunJu. "AI education perception of pre-service teachers according to AI learning experience, Interest in AI education, and Major." Journal of The Korean Association of Information Education 25, no. 1 (February 28, 2021): 103–11. http://dx.doi.org/10.14352/jkaie.2021.25.1.103.

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TAKANO, Toshiaki, Takumi ICHIMURA, Masayoshi KANOH, and Makoto KOSHINO. "Game AI." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 25, no. 4 (2013): 111–18. http://dx.doi.org/10.3156/jsoft.25.4_111.

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Spezio, Michael. "AI empires." Science 372, no. 6539 (April 15, 2021): 246. http://dx.doi.org/10.1126/science.abh2250.

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Monroe, Don. "Deceiving AI." Communications of the ACM 64, no. 6 (June 2021): 15–16. http://dx.doi.org/10.1145/3460218.

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Richins, Daniel, Dharmisha Doshi, Matthew Blackmore, Aswathy Thulaseedharan Nair, Neha Pathapati, Ankit Patel, Brainard Daguman, et al. "AI Tax." ACM Transactions on Computer Systems 37, no. 1-4 (June 2021): 1–32. http://dx.doi.org/10.1145/3440689.

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Artificial intelligence and machine learning are experiencing widespread adoption in industry and academia. This has been driven by rapid advances in the applications and accuracy of AI through increasingly complex algorithms and models; this, in turn, has spurred research into specialized hardware AI accelerators. Given the rapid pace of advances, it is easy to forget that they are often developed and evaluated in a vacuum without considering the full application environment. This article emphasizes the need for a holistic, end-to-end analysis of artificial intelligence (AI) workloads and reveals the “AI tax.” We deploy and characterize Face Recognition in an edge data center. The application is an AI-centric edge video analytics application built using popular open source infrastructure and machine learning (ML) tools. Despite using state-of-the-art AI and ML algorithms, the application relies heavily on pre- and post-processing code. As AI-centric applications benefit from the acceleration promised by accelerators, we find they impose stresses on the hardware and software infrastructure: storage and network bandwidth become major bottlenecks with increasing AI acceleration. By specializing for AI applications, we show that a purpose-built edge data center can be designed for the stresses of accelerated AI at 15% lower TCO than one derived from homogeneous servers and infrastructure.
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Negoita, Constantin. "Revisiting AI." Human Systems Management 8, no. 1 (1989): 81. http://dx.doi.org/10.3233/hsm-1989-8110.

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Barnes, Nick, Peter Baumgartner, Tiberio Caetano, Hugh Durrant-Whyte, Gerwin Klein, Penelope Sanderson, Abdul Sattar, et al. "AI@NICTA." AI Magazine 33, no. 3 (September 20, 2012): 115. http://dx.doi.org/10.1609/aimag.v33i3.2430.

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NICTA is Australia's Information and Communications Technology (ICT) Centre of Excellence. It is the largest organization in Australia dedicated to ICT research. While it has close links with local universities, it is in fact an independent but not-for-profit company in the business of doing research, commercializing that research and training PhD students to do that research. Much of the work taking place at NICTA involves various topics in artificial intelligence. In this article, we survey some of the AI work being undertaken at NICTA.
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Sofge, Don, William Lawless, and Ranjeev Mittu. "AI Bookie." AI Magazine 40, no. 3 (September 30, 2019): 79–84. http://dx.doi.org/10.1609/aimag.v40i3.5196.

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The AI Bookie column documents highlights from AI Bets, an online forum for the creation of adjudicatable predictions and bets about the future of AI. While it is easy to make a prediction about the future, this forum was created to help researchers craft predictions whose accuracy can be clearly and unambiguously judged when they come due. The bets will be documented on line, and regularly in this publication in The AI Bookie. We encourage bets that are rigorously and scientifically argued. We discourage bets that are too general to be evaluated, or too specific to an institution or individual. The goal is not to continue to feed the media frenzy and pundit predictions about AI, but rather to curate and promote bets whose outcomes will provide useful feedback to the scientific community. Place your bets! Please go to ai.sciencebets.org
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47

El-Deeb, Ahmed. "AI Adoption." ACM SIGSOFT Software Engineering Notes 47, no. 4 (September 27, 2022): 16–17. http://dx.doi.org/10.1145/3561846.3561851.

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While Artificial Intelligence (AI) has been an industry buzzword the past 15+ years, AI as a subject is not something new. The term AI has been coined by John McCarthy in 1956 and Neural Networks has been a popular subject well in the 1980s. It's just that AI has undergone a long journey of invention and entrepreneurial phase; and seem to still not fully over it. The question now why the industry is not crossing the chasm to the mass production phase? Why most companies are not relying on AI product to reduce their churn and increase their efficiency? In this paper, I will survey the major factors that play critical role in the slow AI adoption across the software industry.
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48

Sparkes, Matthew. "AI copyright." New Scientist 256, no. 3407 (October 2022): 17. http://dx.doi.org/10.1016/s0262-4079(22)01807-3.

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Larson, Pär. "Ai Santiago!" Verba: Anuario Galego de Filoloxía 45 (September 19, 2018): 361. http://dx.doi.org/10.15304/verba.45.4463.

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50

Schmidt Nordmo, Tor-Arne, Ove Kvalsvik, Svein Ove Kvalsund, Birte Hansen, and Michael A. Riegler. "Fish AI." Nordic Machine Intelligence 2, no. 2 (June 2, 2022): 1–3. http://dx.doi.org/10.5617/nmi.9657.

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Sustainable Commercial Fishing is the second challenge at the Nordic AI Meet following the successful MedAI, which had a focus on medical image segmentation and transparency in machine learning (ML)-based systems. FishAI focuses on a new domain, namely, commercial fishing and how to make it more sustainable with the help of machine learning. A range of public available datasets is used to tackle three specific tasks. The first one is to predict fishing coordinates to optimize catching of specific fish, the second one is to create a report that can be used by experienced fishermen, and the third task is to make a sustainable fishing plan that provides a route for a week. The second and third task require to some extend explainable and interpretable models that can provide explanations. A development dataset is provided and all methods will be tested on a concealed test dataset and assessed by an expert jury. artificial intelligence; machine learning; segmentation; transparency; medicine
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