Transparency and reproducibility in artificial intelligence

Abstract

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

Towards intellectual freedom in an AI Ethics Global Community

“…AI must be seen societally and legally as a profession with fiduciary duties to the public, much like how the field of medicine has a strong history of professional ethical norms [ 7 ]. Problems of systemic racism and other culturally deeply encoded discriminatory mechanisms multiply the severity of such conflicts of interest insignificant, perhaps non-linear and even counter-intuitive ways [ 22 , 24 , 36 , 38 , 39 , 44 ]. AI Ethics research becomes a site where both problems intersect.…”

“…These efforts will significantly aid in ensuring a robust AI ethics research community, but we cannot ignore the intersection of AI ethics with issues of systemic racism and other culturally encoded discriminatory mechanisms. It is well known that racial minorities, women, LGBTQ + individuals, and other groups are underrepresented in AI research and other key technical roles [ 24 ]. This means that often AI ethics researchers raising concerns about these populations are raising them to people who are not directly affected by the issue and may, in fact, have other personal or corporate stakes in the project.…”

On the Role of Artificial Intelligence in Medical Imaging of COVID-19

“…AI literature focus mainly on diagnosis with less coverage on other tasks with higher clinical relevance, such as severity assessment and monitoring. With few exceptions, the authors of the best publications did not release their clinical datasets, raising concerns about reproducibility and transparency of their studies as was recently exemplified for Nature study on breast cancer screening 59,60 and a COVID-19 mortality prediction study 61 that was found irreproducible by three independent research groups from different countries 62,63,64 . Given the global, unprecedented public health challenge caused by COVID-19, we strongly encourage medical researchers to follow the trends toward open-source development in other fields and expedite a transformation toward a common practice of publishing data and code.…”

Welcome to the first issue of Applied AI Letters

“…We are facing a global reproducibility crisis in science and research. Artificial intelligence is no different, in part due to the complexity and ever increasing scale of training and testing data, however, we still have many lessons to learn 2 …”