In December, the University of Texas at Austin's computer science department announced that it would stop using a machine-learning system to evaluate applicants for its Ph.D. program due to concerns that encoded bias may exacerbate existing inequities in the program and in the field in general. This move toward more inclusive admissions practices is a rare (and welcome) exception to a worrying trend in education: Colleges, standardized test providers, consulting companies, and other educational service providers are increasingly adopting predatory, discriminatory, and outright exclusionary student data practices. Student data has long been used as a college recruiting and admissions tool. In 1972, College Board, the company that owns the PSAT, the SAT, and the AP Exams, created its Student Search Service and began licensing student names and data profiles to colleges (hence the college catalogs that fill the mail boxes of high school students who have taken the exams). Today, College Board licenses millions of student data profiles every year for 47 cents per examinee.
Among the things I have not missed since entering middle age is the sensation of being an absolute beginner. It has been decades since I've sat in a classroom in a gathering cloud of incomprehension (Algebra 2, tenth grade) or sincerely tried, lesson after lesson, to acquire a skill that was clearly not destined to play a large role in my life (modern dance, twelfth grade). Learning to ride a bicycle in my early thirties was an exception--a little mortifying when my husband had to run alongside the bike, as you would with a child--but ultimately rewarding. Less so was the time when a group of Japanese schoolchildren tried to teach me origami at a public event where I was the guest of honor--I'll never forget their sombre puzzlement as my clumsy fingers mutilated yet another paper crane. Like Tom Vanderbilt, a journalist and the author of "Beginners: The Joy and Transformative Power of Lifelong Learning" (Knopf), I learn new facts all the time but new skills seldom.
Karoly Bozan (email@example.com) is an assistant professor in the Palumbo-Donahue School of Business at Duquesne University, Pittsburgh, PA, USA. Kalle Lyytinen is a Distinguished University Professor and Iris S. Wolstein Professor of Management Design at Case Western Reserve University, Cleveland, OH, USA. Gregory M. Rose is an associate professor in the Carson College of Business at Washington State University, Pullman, WA, USA.
The Non-Programmers' Tutorial For Python 3 is a tutorial designed to be an introduction to the Python programming language. This guide is for someone with no programming experience. "The Coder's Apprentice" aims at teaching Python 3 to students and teenagers who are completely new to programming. Contrary to many of the other books that teach Python programming, this book assumes no previous knowledge of programming on the part of the students, and contains numerous exercises that allow students to train their programming skills. The book aims at striking the balance between a tutorial and reference book. Includes some fun exercises at the end! "A Byte of Python" is a free book on programming using the Python language. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you.
An HU Analytics Ph.D. student's research paper, recently named "Best Overall Paper" at the Tackling Climate Change with Machine Learning workshop at the NeurIPS2020 Conference, has been featured on Forbes.com. In the article, "A.I. needs to get real--and other takeaways from this year's NeurIPS," author Jeremy Kahn notes that (HU Analytics Ph.D. student) Lyra Wang and her collaborators have teamed "to create a machine learning system to automatically predict areas of oil and natural gas drilling sites that are likely to leak methane, the heat trapping gas that is 84 times more potent than carbon dioxide and a major contributor to global warming." Wang's paper, titled "A Machine Learning Approach to Methane Emissions Mitigation in the Oil and Gas Industry," in a section of the article titled, "An Eye of AI Research." To view the article, visit this link. Accredited by the Middle States Commission on Higher Education, Harrisburg University is a private non-profit university offering bachelor and graduate degree programs in science, technology, and math fields to a diverse student body.
Research shows that even undergraduate students lack these basic skills. Some are not accurate at judging the reliability of information sources or managing the quantity of material available. This is a particularly critical skill in the time of AI where fake news is so easy to produce and spread. Some do not have the skills to collaborate or work in teams, especially not through digital technologies. How can we then expect them to work with different types of AI?
In this paper, we report experimental results on assessing the impact of COVID-19 on college students by processing free-form texts generated by them. By free-form texts, we mean textual entries posted by college students (enrolled in a four year US college) via an app specifically designed to assess and improve their mental health. Using a dataset comprising of more than 9000 textual entries from 1451 students collected over four months (split between pre and post COVID-19), and established NLP techniques, a) we assess how topics of most interest to student change between pre and post COVID-19, and b) we assess the sentiments that students exhibit in each topic between pre and post COVID-19. Our analysis reveals that topics like Education became noticeably less important to students post COVID-19, while Health became much more trending. We also found that across all topics, negative sentiment among students post COVID-19 was much higher compared to pre-COVID-19. We expect our study to have an impact on policy-makers in higher education across several spectra, including college administrators, teachers, parents, and mental health counselors.
Books dedicated to Digital Transformation are on the rise in 2020. For that reason, we present a selection of the best Digital Transformation books recently written by talented authors. Every business that began before the Internet now faces the same challenge: How to transform to compete in a digital economy? Globally recognized digital expert David L. Rogers argues that digital transformation is not about updating your technology but about upgrading your strategic thinking. Based on Rogers's decade of research and teaching at Columbia Business School, and his consulting for businesses around the world, The Digital Transformation Playbook shows how pre-digital-era companies can reinvigorate their game plans and capture the new opportunities of the digital world. Rogers shows why traditional businesses need to rethink their underlying assumptions in five domains of strategy―customers, competition, data, innovation, and value. He reveals how to harness customer networks, platforms, big data, rapid experimentation, and disruptive business models―and how to integrate these into your existing business and organization. Rogers illustrates every strategy in this playbook with real-world case studies, from Google to GE, from Airbnb to the New York Times.
Leading researchers discussed which requirements AI algorithms must meet to fight bias in healthcare during the'Artificial Intelligence and Implications for Health Equity: Will AI Improve Equity or Increase Disparities?' session which was held on 1 December. The speakers were: Ziad Obermeyer, associate professor of health policy and management at the Berkeley School of Public Health, CA; Luke Oakden-Rayner, director of medical imaging research at the Royal Adelaide Hospital, Australia; Constance Lehman, professor of radiology at Harvard Medical School, director of breast imaging, and co-director of the Avon Comprehensive Breast Evaluation Center at Massachusetts General Hospital; and Regina Barzilay, professor in the department of electrical engineering and computer science and member of the Computer Science and AI Lab at the Massachusetts Institute of Technology. The discussion was moderated by Judy Wawira Gichoya, assistant professor in the Department of Radiology at Emory University School of Medicine, Atlanta. Artificial intelligence (AI) may unintentionally intensify inequities that already exist in modern healthcare and understanding those biases may help defeat them. Social determinants partly cause poor healthcare outcomes and it is crucial to raise awareness about inequity in access to healthcare, as Prof Sam Shah, founder and director of Faculty of Future Health in London, explained in a keynote during the HIMSS & Health 2.0 European Digital event.
Amid declining sales and evidence that smoking causes lung cancer, in the 1950s tobacco companies undertook PR campaigns to reinvent themselves as socially responsible and to shape public opinions. They also started funding research into the relationship between health and tobacco. Now, Big Tech companies like Amazon, Facebook, and Google are following the same playbook to fund AI ethics research in academia, according to a recently published paper by University of Toronto Center for Ethics PhD student Mohamed Abdalla and Harvard Medical School student Moustafa Abdalla. The coauthors conclude that effective solutions to the problem will need to come from institutional or governmental policy changes. The Abdalla brothers argue Big Tech companies aren't just involved with, but are leading, ethics discussions in academic settings.