BibSonomy

Fuzzy Loss functions for GANs, Learning Analytics, Next Generation AI and Sustainability, Deep Learning for Melodic Frameworks Speakers: Prof. Priti S. Sajja, Sardar Patel University, India Prof. Elvira Popescu, University of Craiova, Romania Dr. Celestine Iwendi, University of Bolton, UK Dr. Vishnu S. Pendyala, San Jose State University, USA Date: Tuesday, July 12, 2022

«This book is organized into four parts. The first part considers the fundamental principles of the Matplotlib library. This includes reviewing the different parts that constitute a figure, the different coordinate systems, the available scales and projections, and we’ll also introduce a few concepts related to typography and colors. The second part is dedicated to the actual design of a figure. After introducing some simple rules for generating better figures, we’ll then go on to explain the Matplotlib defaults and styling system before diving on into figure layout organization. We’ll then explore the different types of plot available and see how a figure can be ornamented with different elements. The third part is dedicated to more advanced concepts, namely 3D figures, optimization & animation. The fourth and final part is a collection of showcases.»

«Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS. In previous work, we proposed that ethics-based auditing (EBA)—that is, a structured process by which ADMS are assessed for consistency with relevant principles or norms—can (a) help organisations verify claims about their ADMS and (b) provide decision-subjects with justifications for the outputs produced by ADMS. In this article, we outline the conditions under which EBA procedures can be feasible and effective in practice. First, we argue that EBA is best understood as a ‘soft’ yet ‘formal’ governance mechanism. This implies that the main responsibility of auditors should be to spark ethical deliberation at key intervention points throughout the software development process and ensure that there is sufficient documentation to respond to potential inquiries. Second, we frame AMDS as parts of larger sociotechnical systems to demonstrate that to be feasible and effective, EBA procedures must link to intervention points that span all levels of organisational governance and all phases of the software lifecycle. The main function of EBA should, therefore, be to inform, formalise, assess, and interlink existing governance structures. Finally, we discuss the policy implications of our findings. To support the emergence of feasible and effective EBA procedures, policymakers and regulators could provide standardised reporting formats, facilitate knowledge exchange, provide guidance on how to resolve normative tensions, and create an independent body to oversee EBA of ADMS.»

As data scientists and business leaders, we need to think about the ethical and privacy considerations of machine learning and artificial intelligence. FICO's Scott Zoldi shares recommendations around responsible AI, ethics, and privacy during this important conversation.

This paper provides a succinct overview of this emerging theory of overparameterized ML (henceforth abbreviated as TOPML) that explains these recent findings through a statistical signal processing perspective. We emphasize the unique aspects that define the TOPML research area as a subfield of modern ML theory and outline interesting open questions that remain.

Presentation by Cristina Alonso at EDUCON 2021, Special Session Education in the new Normal (eMadrid: EdNN), 22nd April 2021.

Join NYU-LEARN and Drs. Paul Prinsloo and Ravi Shroff in an engaging conversation on how we can productively navigate the intersection of learning analytics and equity and consider the role of educational data science as a tool for social justice.

AI has transformed a host of fields including engineering, medicine, finance, and now it is innovating education.

Through 3 key case studies, Rachel Thomas covers how people can be harmed by machine learning gone wrong, why we as machine learning practitioners should care, and what tech ethics are.

Using deep learning to understand the level of attention and engagement of students. Ensuring privacy, having real-time feedback on the delivery of coursework will help lecturers/presenters, make improvements vs. waiting once or twice a year for this information.