This article provides an organization of various kinds of biases that can occur in the AI pipeline starting from dataset creation and problem formulation to data analysis and evaluation. It highlights the challenges associated with the design of bias-mitigation strategies, and it outlines some best practices suggested by researchers. Finally, a set of guidelines is presented that could aid ML developers in identifying potential sources of bias, as well as avoiding the introduction of unwanted biases. The work is meant to serve as an educational resource for ML developers in handling and addressing issues related to bias in AI systems.

In this time of pandemic, the world has turned to Internet-based, real-time communication (RTC) as never before. The number of RTC products has, over the past decade, exploded in large part because of cheaper high-speed network access and more powerful devices, but also because of an open, royalty-free platform called WebRTC. In fact, over the past year, there has been a 100-fold increase of video minutes received via the WebRTC stack in the anonymous population that has opted into Google Chrome's statistics. WebRTC can be found in most Internet meeting services, social networks, live-streaming experiences, and even cloud-based gaming products. An open source implementation and tutorials for this platform can be found at https://webrtc.org.

The high expectations of AI have triggered worldwide interest and concern, generating 400 policy documents on responsible AI. Intense discussions over the ethical issues lay a helpful foundation, preparing researchers, managers, policy makers, and educators for constructive discussions that will lead to clear recommendations for building the reliable, safe, and trustworthy systems6 that will be commercial success. This Viewpoint focuses on four themes that lead to 15 recommendations for moving forward. The four themes combine AI thinking with human-centered User Experience Design (UXD). Ethical discussions are a vital foundation, but raising the edifice of responsible AI requires design decisions to guide software engineering teams, business managers, industry leaders, and government policymakers.

The commonly held belief that programming is inherently hard lacks sufficient evidence. Stating this belief can send influential messages that can have serious unintended consequences including inequitable practices.4 Further, this position is most often based on incomplete knowledge of the world's learners. More studies need to include a greater diversity of all kinds including but not limited to ability, ethnicity, geographic region, gender identity, native language, race, and socioeconomic background. Language is a powerful tool.

In late 2018, thousands of workers walked out of Google offices around the globe to protest the company's handling of sexual harassment accusations against prominent executives. The same year, hundreds of Salesforce employees signed a letter to CEO Marc Benioff protesting the fact the company sold products to U.S. Customs and Border Protection. Also in the headlines was an effort by some Microsoft employees to protest the company's bid for work on the U.S. Department of Defense's Joint Enterprise Defense Infrastructure (JEDI) project. In a letter to Microsoft CEO Satya Nadella, the employees wrote, "many Microsoft employees don't believe that what we build should be used for waging war." Tech employee activism is nothing new, but the momentum generated by the 2018 wave of protests was.

Carter shook her head and stared at the combined camera and microphone that surveyed the corridor. Lipcott's words seemed to float in front of her eyes. What she did next could determine not just Lipcott's future, but her own. She walked on to the corner, to a dead spot between cameras, took a deep breath, and mouthed, "Don't ask questions.

Computers have been able to quickly process 2D images for some time. Your cell phone can snap digital photographs and manipulate them in a number of ways. Much more difficult, however, is processing an image in three dimensions, and doing it in a timely manner. The mathematics are more complex, and crunching those numbers, even on a supercomputer, takes time. That's the challenge a group of scientists from the U.S. Department of Energy's (DOE) Argonne National Laboratory is working to overcome.

Emilia Kirk is the Global Head of Growth at Seedtag, responsible for Client, Marketing and Product Growth. Visit Seedtag's site to hear more There are certain developments in the world of business that cannot be ignored. They revolutionize the way you work and become absolutely essential in the ways you operate. However, some developments, such as contextual AI, work quietly in the background and are sometimes seen as nice-to-have rather than business-critical. In my experience, this is a flawed assumption. I believe contextual AI needs to be seen as part of the absolute necessities to drive success.

One of the challenges with the pursuit of AI is the mismatch between the science fiction concept of artificial intelligence and the real-world, practical applications of AI. In movies and science fiction novels, AI systems are portrayed as super-intelligent machines that have cognitive capabilities equal to or greater than that of humans. However, the reality is that much of what organizations are implementing today for artificial intelligence are narrow applications of AI. This is in clear contrast to artificial general intelligence (AGI). The limit of our current AI abilities lets organizations implement specific cognitive abilities in narrow domains, such as image recognition, conversational systems, predictive analytics as well as pattern and anomaly detection.

There is no question that the enterprises that successfully leverage AI's potential will be the ones to get ahead. While businesses are more and more certain about the goals they want to use AI for, how to execute and deploy it successfully remains a difficult question. In today's world, the dynamics and efficiency of operations are crucial factors, and data is the key to flourishing in the rapidly changing business reality. Data provides insights from various areas of business activity. Solutions based on artificial intelligence or machine learning not only support the analysis of huge amounts of data, but also provide a new approach to optimizing and automating core processes.