Let's take a couple minutes now to examine some of the questions you might have that this Swedish hiring robot poses. Ifeoma Ajunwa of Cornell University has studied the use of artificial intelligence in the hiring process here in the U.S. Welcome. IFEOMA AJUNWA: Thank you very much for having me. CHANG: Is it possible for AI to completely eliminate human bias in the hiring process? AJUNWA: I would say no because you still have to remember that AI isn't fully automated.

There has, for years, been one thing that just about all of the tech industry agrees on: regulation is coming. Recently, they have even realised that it's necessary. But if there is one thing that has split tech behemoths, politicians and the public apart more than perhaps any other issue, it's what that regulation should look like. Now the UK government thinks it has alighted on an answer, offering perhaps the first comprehensive attempt to limit the harm that technology companies are doing to the people – in particular the children – who use them. For the most part, the solution they have chosen focuses on shifting the responsibility for content that appears on the site onto the people who run them.

After little more than a week, Google backtracked on creating its Advanced Technology External Advisory Council, or ATEAC--a committee meant to give the company guidance on how to ethically develop new technologies such as AI. The inclusion of the Heritage Foundation's president, Kay Coles James, on the council caused an outcry over her anti-environmentalist, anti-LGBTQ, and anti-immigrant views, and led nearly 2,500 Google employees to sign a petition for her removal. Instead, the internet giant simply decided to shut down the whole thing. How did things go so wrong? And can Google put them right?

Amazon's digital assistant could soon do more than just turn on your lights or tell you the weather. The e-commerce giant has signaled a major leap into healthcare for Alexa, by rolling out an invite-only program for developers to create their own skills around health and medicine. It would allow users to ask Alexa to book a doctor's appointment, find an urgent care center and check for updates on prescription refills. Amazon's digital assistant could soon do more than just turn on your lights or tell you the weather. Amazon, which launched the program on Thursday, said the skills are all compliant with the federal Health Insurance Portability and Accountability Act, which ensures that personal health care information is protected. The firm told Wired that it has added extra security levels to how it treats the data collected through these skills, beyond the encryption, access controls and secure cloud storage it deploys for other skill data.

Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.

Edge devices/IoT such as smart phones, wearable devices, sensors, and smart homes are increasingly generating massive, diverse, and private data. In response, there is a trend towards moving computation, including the training of machinelearning models, from cloud/datacenters to edge devices [1,24]. Ideally, since trained on massive representative data, the resulting models exhibit improved generalization. In this paper, we consider distributed on-device machine learning. The distributed system is a server-worker architecture.

Two Chinese software developers are trying to harness the power of open source software to improve working conditions for coders. Last weekend, Katt Gu and Suji Yan, published the "Anti-996 License," which requires any company that uses the project's software to comply with local labor laws as well as International Labour Organization standards, including the right for workers to collectively bargain and a ban on forced labor. The license is part of the growing Anti-996 Movement in China, which refers to a common schedule of working from 9 am to 9 pm, six days a week. This grueling schedule is allegedly widespread in the Chinese tech startup industry, according to a story in the South China Morning Post last month. Last week, one or more anonymous activists launched a website called 996.ICU, detailing Chinese labor laws that a 996 schedule may violate, including provisions that generally limit work to 44 hours a week and require overtime pay.

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. AI Magazine also maintains a calendar listing that includes nonaffiliated conferences at www.aaai.org/Magazine/calendar.php. ICAIL-2019 will be held 17-21 June in USA. SoCS-19 will be held July 16-17 2019 (immediately AAAI Fall Symposium Series. IAAI-20 Conference will be held February 9-11, 2020 at the Hilton New York Midtown Hotel in New York, New York USA.

XAI, explainable AI, and the DARPA program to provide that. The European Commission has legislated a demand (Order GDPR 2016/2679) specifying that deployed machine learning systems must explain their decisions. The commission has done this even though no one knows how to provide what they are requiring. What would follow if we and machines are in roughly the same position with respect to the transparency of our ethical decision-making? I want to reintroduce the notion of orthosis into ethical explanation: medically, an orthosis is an externally applied device designed and fitted to the body to aid rehabilitation, and usually contrasted with a prosthesis, which replaces a missing part, like a foot or leg. Here, it will mean an explanatory software agent associated with a human or machine.

Defense and security organizations depend upon science and technology to meet operational needs, predict and counter threats, and meet increasingly complex demands of modern warfare. Artificial intelligence and robotics could provide solutions to a wide range of military gaps and deficiencies. At the same time, the unique and rapidly evolving nature of AI and robotics challenges existing polices, regulations, and values, and introduces complex ethical issues that might impede their development, evaluation, and use by the Canadian Armed Forces (CAF). Early consideration of potential ethical issues raised by military use of emerging AI and robotics technologies in development is critical to their effective implementation. This article presents an ethics assessment framework for emerging AI and robotics technologies. It is designed to help technology developers, policymakers, decision makers, and other stakeholders identify and broadly consider potential ethical issues that might arise with the military use and integration of emerging AI and robotics technologies of interest. We also provide a contextual environment for our framework, as well as an example of how our framework can be applied to a specific technology. Finally, we briefly identify and address several pervasive issues that arose during our research.