Melodies Manipulated: Intellectual Property & The Music Industry

Marilyn Mosby, Founder and Managing Partner of Mahogany Elite Consulting, opened the IPLJ Symposium with her Keynote Address which focused on the cultural, political, and social context surrounding the use of rap lyrics as evidence in criminal prosecutions. The opening panel, “Do You Get Déjà Vu?,” comprised of Gary Adelman, Partner, Adelman Matz PC; Linna Chen, Senior Legal Counsel, Litigation & Copyright, Spotify; and Ilene Farkas, Partner, Pryor Cashman, and was moderated by Sarah Matz, Partner, Adelman Matz PC, and Adjunct Professor at Fordham University School of Law. The panel discussed recent copyright cases, specifically Williams v. Gaye (the “Blurred Lines Case”) and Griffin v. Sheeran and deliberated about what these decisions mean for artists involved in copyright infringement suits. The second panel, Robotic Rhapsody, explored the effects of AI-generated music on the music industry and its implications for music copyright law. The panel included Paul Fakler, Partner, Mayer Brown; Alex Mitchell, Co-Founder and CEO of Boomy Music (a Generative AI Music platform); and Marc Ostrow, Senior Counsel, Romano Law. The panel was moderated by Fordham Law Visiting Professor Shlomit Yanisky-Ravid. The last panel of the day, Rhyme & Punishment, circled back to the use of rap lyrics as evidence in criminal trials and the blatantly racist nature of this practice. Panelists included Erik Nielson, University of Richmond Liberal Arts Professor and Department Chair and Co-Author of the award-winning book Rap on Trial: Race, Lyrics, and Guilt in America; Amber Baylor, Columbia Law School Professor and Criminal Defense Clinic Director; Emerson Sykes, Senior Staff Attorney, American Civil Liberties Union (“ACLU”); and Kenan Kurt, Chief of Staff and Counsel for New York State Senator Brad Hoylman-Sigal. The panel was moderated by Fordham Law Professor Bennett Capers

A qualitative comparison of how older breast cancer survivors process treatment information regarding endocrine therapy.

BACKGROUND:It remains unclear how information about aromatase inhibitors (AI) impacts women's decision-making about persistence with endocrine therapy. PURPOSE:To describe and compare how women treated for primary early stage breast cancer either persisting or not persisting with an AI received, interpreted, and acted upon AI-related information. DESIGN:Thematic analysis was used to sort and compare the data into the most salient themes. PARTICIPANTS:Women (N = 54; 27 persisting, 27 not persisting with an AI) aged 65-93 years took part in qualitative interviews. RESULTS:Women in both subgroups described information similarly in terms of its value, volume, type, and source. Aspects of AI-related information that either differed between the subgroups or were misunderstood by one or both subgroups included: (1) knowledge of AI or tamoxifen prior to cancer diagnosis, (2) use of online resources, (3) misconceptions about estrogen, hormone replacement therapies and AI-related symptoms, and (4) risk perception and the meaning and use of recurrence statistics such as Oncotype DX. CONCLUSIONS:Persisters and nonpersisters were similar in their desire for more information about potential side effects and symptom management at AI prescription and subsequent appointments. Differences included how information was obtained and interpreted. Interactive discussion questions are shared that can incorporate these findings into clinical settings

Sensemaking Practices in the Everyday Work of AI/ML Software Engineering

This paper considers sensemaking as it relates to everyday software engineering (SE) work practices and draws on a multi-year ethnographic study of SE projects at a large, global technology company building digital services infused with artificial intelligence (AI) and machine learning (ML) capabilities. Our findings highlight the breadth of sensemaking practices in AI/ML projects, noting developers' efforts to make sense of AI/ML environments (e.g., algorithms/methods and libraries), of AI/ML model ecosystems (e.g., pre-trained models and "upstream"models), and of business-AI relations (e.g., how the AI/ML service relates to the domain context and business problem at hand). This paper builds on recent scholarship drawing attention to the integral role of sensemaking in everyday SE practices by empirically investigating how and in what ways AI/ML projects present software teams with emergent sensemaking requirements and opportunities

Wittgenstein and Communication Technology : A conversation between Richard Harper and Constantine Sandis

Special Issue: PROCEEDINGS OF THE BRITISH WITTGENSTEIN SOCIETY 10TH ANNIVERSARY CONFERENCE: WITTGENSTEIN IN THE 21ST CENTURY © 2018 John Wiley & Sons LtdThis paper documents a conversation between a philosopher and a human computer interaction researcher whose research has been enormously influenced by Wittgenstein. In particular, the in vivo use of categories in the design of communications and AI technologies are discussed, and how this meaning needs to evolve to allow creative design to flourish. The paper will be of interest to anyone concerned with philosophical tools in everyday action.Non peer reviewe

Comparison between the two definitions of AI

Two different definitions of the Artificial Intelligence concept have been proposed in papers [1] and [2]. The first definition is informal. It says that any program that is cleverer than a human being, is acknowledged as Artificial Intelligence. The second definition is formal because it avoids reference to the concept of human being. The readers of papers [1] and [2] might be left with the impression that both definitions are equivalent and the definition in [2] is simply a formal version of that in [1]. This paper will compare both definitions of Artificial Intelligence and, hopefully, will bring a better understanding of the concept.Comment: added four new section