The last decade has seen a vast increase both in the diversity of applications to which machine learning is applied, and to the import of those applications. Machine learning is no longer just the engine behind ad placements and spam filters; it is now used to filter loan applicants, deploy police officers, and inform bail and parole decisions, among other things. The result has been a major concern for the potential for data-driven methods to introduce and perpetuate discriminatory practices, and to otherwise be unfair. And this concern has not been without reason: a steady stream of empirical findings has shown that data-driven methods can unintentionally both encode existing human biases and introduce new ones.7,9,11,60 At the same time, the last two years have seen an unprecedented explosion in interest from the academic community in studying fairness and machine learning. "Fairness and transparency" transformed from a niche topic with a trickle of papers produced every year (at least since the work of Pedresh56 to a major subfield of machine learning, complete with a dedicated archival conference--ACM FAT*). But despite the volume and velocity of published work, our understanding of the fundamental questions related to fairness and machine learning remain in its infancy.

Women are underrepresented in the fields of science, technology, engineering, and mathematics (STEM) in most countries, including Germany and the U.S.29,32 This was demonstrated in several surveys investigating the proportion of women in the STEM fields for specific populations. Some of these studies, for example, investigated the number of enrolled students10,30 or the percentage of female professors at universities. Other studies analyzed the disparities in research funding.23 Nearly all these surveys selected a particular population of women in consideration of their university degree or their nationality.11,34 Like many other studies investigating the gender gap and its reasons in science, these surveys are usually based on data records from several kinds of registrations or enrollments, for example, the enrollment as student or doctoral student, the registration of finished doctoral theses or the membership as professor in a certain country.1,14,16,28 However, researchers at the postdoctoral level or industrial researchers are often not registered and unfortunately drop out of the surveys.

Four articles, published across the March through May issues of Communications, highlight how people are the unique source of the adaptive capacity essential to incident response in modern Internet-facing software systems. While it's reasonable for software engineering and operations communities to focus on the intricacies of technology, there is not much attention given to the intricacies of how people do their work. Ultimately, it is human performance that makes modern business-critical systems robust and resilient. As business-critical software systems become more successful, they necessarily increase in complexity. Ironically, this complexity makes these systems inherently messy so that surprising incidents are part and parcel of the capability to provide services at larger scales and speeds.13

We live in an age of amazing new visual art created with artificial intelligence (AI) technology. The recent wave began with neural stylization apps and the trippy, evocative DeepDream. Many fine artists now work with neural network algorithms, creating high-profile works appearing in major venues.1 Together with these new developments comes the hype: technologists who claim that their algorithms are artists and journalists who suggest that computers are creating art on their very own. This column explains why today's technologies do not create art; they are tools for artists. This is not a fringe viewpoint; it reflects mainstream understanding of both art and computer science.

With the increased prevalence of U.S. states including computer science as a required subject in K-8 education (and as an elective in 9-12), in the next decade, nearly every child in the U.S. will be taking CS classes. The rapid integration of CS into the current education system has challenged states, districts, and teacher preparation programs to revamp their current efforts considerably. As this is a relatively new innovation and challenge, it provides us with a unique opportunity to consider our agenda: What is the goal of CS education? In the K--12 context, CS is often synonymous with coding--in fact, to many educators, CS is only coding. We suggest the goal of CS K--12 education should be for K--12 students to understand CS beyond simply learning to code.

Samuel Greengard is an author and journalist based in West Linn, OR, USA.

The Charisma Machine chronicles the life and legacy of the One Laptop Per Child project and explains why--despite its failures--the same utopian visions that inspired OLPC still motivate other projects trying to use technology to "disrupt" education and development. Announced in 2005 by MIT Media Lab cofounder Nicholas Negroponte, One Laptop Per Child promised to transform the lives of children across the Global South with a small, sturdy, and cheap laptop computer, powered by a hand crank. In reality, the project fell short in many ways, starting with the hand crank, which never materialized. Yet the project remained charismatic to many who were enchanted by its claims of access to educational opportunities previously out of reach. Behind its promises, OLPC, like many technology projects that make similarly grand claims, had a fundamentally flawed vision of who the computer was made for and what role technology should play in learning.

A friend's birthday barbecue is coming up in a few days and we decide to surprise our friend with a new grill. Online, a manufacturer's website allows us to customize the grill. Unknown to us, the design space consists of billions of grills and we create a one-of-a-kind design that has never been produced before. Designing, procuring, producing, and delivering a unique product in a short time, at an affordable price, with minimal human intervention, requires complex interactions across software-hardware, systems, and time scales. This process, known as lot size one production, is only possible through the use of autonomous production systems. Manufacturing is a cornerstone of a country's innovation pipeline and many companies are reshoring to keep manufacturing and R&D as close as possible. While most people think manufacturing is low-tech and boring, I must disagree!

The ACM constitution provides that our Association holds a general election in the even-numbered years for the positions of President, Vice President, Secretary/Treasurer, and Members-at-Large. Biographical information and statements of the candidates appear on the following pages (candidates' names appear in random order). In addition to the election of ACM's officers--President, Vice President, Secretary/Treasurer--five Members-at-Large will be elected to serve on ACM Council. Please refer to the instructions posted at https://www.esc-vote.com/acm. To access the secure voting site, you will need to enter your email address (the email address associated with your ACM member record) and your unique PIN provided by Election Services Co. Please return your ballot in the enclosed envelope, which must be signed by you on the outside in the space provided. The signed ballot envelope may be inserted into a separate envelope for mailing if you prefer this method. All ballots must be received by no later than 16:00 UTC on 22 May 2020. Validation by the Tellers Committee will take place at 14:00 UTC on 26 May 2020. Elizabeth Churchill is a Director of User Experience at Google. Her field of study is Human Computer Interaction (HCI) and User Experience (UX), with a current focus on the design of effective designer and developer tools. Churchill has built research groups and led research in a number of well-known companies, including as Director of Human Computer Interaction at eBay Research Labs in San Jose, CA, as a Principal Research Scientist and Research Manager at Yahoo! in Santa Clara, CA, and as a Senior Scientist at the Palo Alto Research Center (PARC) and FXPAL, Fuji Xerox's Research lab in Silicon Valley. Working across a number of research areas, she has over 100 peer reviewed top-tier journal and conference publications in theoretical and applied psychology, cognitive science, human-computer interaction, mobile and ubiquitous computing, computer-mediated communication, and social media, more than 50 patents granted or pending, and 7 academic books. Her team produces research that impacts a large number of Google's products (by shaping Google's Flutter and Material Design), influencing the work of hundreds of thousands of designers and developers globally, and thus affecting the user experience of millions of end-users. She continues to guest lecture at universities and to mentor early stage career professionals and students.

As I am writing these lines, in mid-March 2020, COVID-19, the disease caused by the coronavirus virus, is spreading around the world. From a local epidemic that broke out in China in late 2019, the disease has turned into a raging pandemic the likes of which the world has not seen since the 1918 Spanish Flu Pandemic. Thousands have already died, and the ultimate death toll may be in the millions. Attempting to mitigate the pandemic, individuals are curtailing travel, entertainment, and more, as well as exercising "social distancing," thus causing an economic slowdown. Businesses hoard cash and cut spending in order to survive a slowdown of uncertain duration.