Artificial Intelligence and Machine Learning are increasingly being used to automate decision-making in many sectors within international development. Although computer intelligence is continuously improving, it has been shown that improper implementation of these algorithms can lead to strong bias, unfairness, or exclusion of certain groups. This research project helps determine guidelines of ethical use of machine learning in developing countries, developing a framework of use of machine learning with criteria of fairness and appropriate use, discovering partnerships in industry, academia, or government in developing countries, and building capacity through educational materials and datasets shared with the world at the end of the research. Integral to this effort are case studies of several sites abroad and in the US which focus on different aspects of applications of machine learning, from employment, to medicine, education, lending, devices, to name a few. The output of this research includes a framework for appropriate and ethical use of machine learning methods based on the interdisciplinary case studies, data analyses, meta-analyses, and pedagogical materials which can be integrated into future machine learning courses around the world.

Technologically, technology is constantly evolving. Artificial intelligence and machine learning have made our lives easier. These amazing technologies can be used to improve systems and achieve the organization's goals. AI and machine learning not only boost the performance of the system but also address the problems of the business like never before. Problems can be addressed more efficiently and quicker than ever before.

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as faculty members doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as faculty members doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as facultymembers doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.

This week's podcast kicks off with the news that Project Connected Home over IP (CHIP) will also have a commercial element focused on offices, apartments, and public buildings. Then we focus on edge computing with a new way to bring machine learning to the edge and Arm expanding its free IP license program to some of its edge ML chips. We also talk about the new IoT Cybersecurity bill that passed the Senate, a virus prediction score on Airthings devices, and another new Wyze product. We round out the news with LoRaWAN, facial recognition laws, telemedicine, an upgrade to Google Fit, and a new name for Plume's Wi-Fi service. Kevin shares his thoughts on the Eero Pro Wi-Fi system, and a quick impression of the new HomePod mini.