AI has the potential to improve human lives and a company's bottom line, but it can also accelerate inequality and eliminate jobs during the worst U.S. recession since the Great Depression. This dual promise and peril led members of the House Budget Committee to hold a hearing today to discuss the impact of AI on economic recovery, the future of work, and the federal budget. Expert witnesses recommended approaches that ranged from giving people lifelong upskilling accounts to creating regional investment districts and portable benefits. Daron Acemoglu warned the committee about the dangers of excessive automation. The MIT professor and economist recently found that every robot replaces 3.3 human jobs in the U.S. In a working paper published by the National Bureau of Economic Research, Acemoglu detailed how excessive automation looks for ways to replace workers with machines or algorithms but produces few new jobs.

Universal Serial Bus (USB) Flash Drives are nowadays one of the most convenient and diffused means to transfer files, especially when no Internet connection is available. However, USB flash drives are also one of the most common attack vectors used to gain unauthorized access to host devices. For instance, it is possible to replace a USB drive so that when the USB key is connected, it would install passwords stealing tools, root-kit software, and other disrupting malware. In such a way, an attacker can steal sensitive information via the USB-connected devices, as well as inject any kind of malicious software into the host. To thwart the above-cited raising threats, we propose MAGNETO, an efficient, non-interactive, and privacy-preserving framework to verify the authenticity of a USB flash drive, rooted in the analysis of its unintentional magnetic emissions. We show that the magnetic emissions radiated during boot operations on a specific host are unique for each device, and sufficient to uniquely fingerprint both the brand and the model of the USB flash drive, or the specific USB device, depending on the used equipment. Our investigation on 59 different USB flash drives---belonging to 17 brands, including the top brands purchased on Amazon in mid-2019---, reveals a minimum classification accuracy of 98.2% in the identification of both brand and model, accompanied by a negligible time and computational overhead. MAGNETO can also identify the specific USB Flash drive, with a minimum classification accuracy of 91.2%. Overall, MAGNETO proves that unintentional magnetic emissions can be considered as a viable and reliable means to fingerprint read-only USB flash drives. Finally, future research directions in this domain are also discussed.

Applying automated reasoning tools for decision support and analysis in law has the potential to make court decisions more transparent and objective. Since there is often uncertainty about the accuracy and relevance of evidence, non-classical reasoning approaches are required. Here, we investigate probabilistic epistemic argumentation as a tool for automated reasoning about legal cases. We introduce a general scheme to model legal cases as probabilistic epistemic argumentation problems, explain how evidence can be modeled and sketch how explanations for legal decisions can be generated automatically. Our framework is easily interpretable, can deal with cyclic structures and imprecise probabilities and guarantees polynomial-time probabilistic reasoning in the worst-case.

After three years speaking about, writing about and training in AI Ethics, organizations I speak with report that many of the students come back with a good understanding of the elements and remedies for ethical issues. But they continue to work as before. Part of the problem is the use of the term "ethics." It's too ineffable for most people to grasp. Some groups have tried "trustworthy."

In light of the Covid-19 pandemic, MIT has been charged with reimagining its campus, classes, and programs, including the Dr. Martin Luther King, Jr. (MLK) Visiting Professors and Scholars Program (VPSP). Founded in 1990, MLK VPSP honors the life and legacy of Martin Luther King, Jr. by increasing the presence of and recognizing the contributions of scholars from underrepresented groups at MIT. MLK Visiting Professors and Scholars enhance their scholarship through intellectual engagement with the MIT community and enrich the cultural, academic, and professional experience of students. But what does a virtual year mean for a visiting scholar? Even with the challenge of remote learning and limited in-person contact, MLK VPSP faculty hosts have articulated innovative ways to engage with the MIT community. Moya Bailey, for instance, will be a content contributor for the Program in Women's and Gender Studies' website and social media accounts.

Pressure on US lawmakers to create federal regulations on facial recognition has been mounting. IBM, Amazon, and Microsoft stopped selling the technology to US police, and called on Congress to regulate its use. Amidst international protests against racism and police misconduct, news broke that Detroit police had wrongfully arrested a Black man based on a faulty facial recognition match. In response, House Democrats proposed a bill last week that would ban police from using facial recognition. Against that backdrop, industry groups have quietly lobbied to soften regulations and avoid an outright ban.

This article is part of the Policing and Technology Project, a collaboration between Future Tense and the Tech, Law, & Security Program at American University Washington College of Law that examines the relationship between law enforcement, police reform, and technology. On Sept. 18 at noon Eastern, Future Tense will host "Power, Policing, and Tech," an online event about the role of technology in law enforcement reform. Public scrutiny around data-driven technologies in the criminal justice system has been on a steady rise over the past few years, but with the recent widespread Black Lives Matter mobilization, it has reached a crescendo. Alongside a broader reckoning with the harms of the criminal justice system, technologies like facial recognition and predictive policing have been called out as racist systems that need to be dismantled. After being an early adopter of predictive policing, the Santa Cruz, California, became the first city in the United States to ban its use.

COCIR welcomes the inception impact assessment by the European Commission on ethical and legal requirements for Artificial Intelligence (AI) and the opportunity to provide feedback. Continuing our engagement in this area, and following the earlier consultation on the AI White Paper, COCIR is pleased to share its experience and expertise on the use of AI within healthcare. COCIR and its members have recently published a comprehensive in-depth analysis of Artificial Intelligence in Medical Device Legislation. The document provides a thorough analysis of the legal requirements applicable to AI-based medical devices. Based on this analysis COCIR sees no need for novel regulatory frameworks for AI-based medical devices, because the requirements of the EU Medical Device Regulation4 (MDR) in combination with provisions of the General Data Protection Regulation (GDPR) are adequate to ensure excellence and trust in AI in line with European values.

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed.

Legal argumentation is a vital cornerstone of justice, underpinning an adversarial form of law, and extensive research has attempted to augment or undertake legal argumentation via the use of computer-based automation including Artificial Intelligence (AI). AI advances in Natural Language Processing (NLP) and Machine Learning (ML) have especially furthered the capabilities of leveraging AI for aiding legal professionals, doing so in ways that are modeled here as CARE, namely Crafting, Assessing, Refining, and Engaging in legal argumentation. In addition to AI-enabled legal argumentation serving to augment human-based lawyering, an aspirational goal of this multi-disciplinary field consists of ultimately achieving autonomously effected human-equivalent legal argumentation. As such, an innovative meta-approach is proposed to apply the Levels of Autonomy (LoA) of AI Legal Reasoning (AILR) to the maturation of AI and Legal Argumentation (AILA), proffering a new means of gauging progress in this ever-evolving and rigorously sought domain.