Paper: Integrating Artificial Intelligence into Weapon Systems

The integration of Artificial Intelligence (AI) into weapon systems is one of the most consequential tactical and strategic decisions in the history of warfare. Current AI development is a remarkable combination of accelerating capability, hidden decision mechanisms, and decreasing costs. Implementation of these systems is in its infancy and exists on a spectrum from resilient and flexible to simplistic and brittle. Resilient systems should be able to effectively handle the complexities of a high-dimensional battlespace. Simplistic AI implementations could be manipulated by an adversarial AI that identifies and exploits their weaknesses. In this paper, we present a framework for understanding the development of dynamic AI/ML systems that interactively and continuously adapt to their user’s needs. We explore the implications of increasingly capable AI in the kill chain and how this will lead inevitably to a fully automated, always on system, barring regulation by treaty. We examine the potential of total integration of cyber and physical security and how this likelihood must inform the development of AI-enabled systems with respect to the ‘fog of war’, human morals, and ethics.


Article: Free Will in an Algorithmic World

Tai, a senior at the University of Pennsylvania, wakes up at the perfect time every morning – well-rested, but not late for classes or appointments. Today that meant rising at 7:18 a.m. He did not set his alarm for that time. Rather, it was chosen for him. His phone’s sleep-tracker app had been following his sleep patterns over the past few months, monitoring his REM cycles and periods of lighter rest. Using this information, it set a smart alarm that wakes him during a light stage of sleep, while also trying to maintain some level of consistency over time. The theory is that this schedule will prime Tai for greater energy and concentration throughout the day. Tai needs to be sharp. He’s at a turning point in his life, about to step away from the relatively safe world of college – of information-gathering, homework, and exams – into the ‘real’ world of practical problem solving: finding a job, choosing a place to live, and negotiating the wonderful but complicated details of a romantic relationship that’s getting more serious by the day.


Article: The Ethics of People Analytics and AI in the Workplace: Four Dimensions of Trust

AI and People Analytics have taken off. As I’ve written about in the past, the workplace has become a highly instrumented place. Companies use surveys and feedback tools to get our opinions, new tools monitor emails and our network of communications (ONA), we capture data on travel, location, and mobility, and organizations now have data on our wellbeing, fitness, and health. And added to this is a new stream of data which includes video (every video conference can be recorded and more than 40% of job interviews are recorded), audio (tools that record meetings can sense mood), and image recognition that recognizes faces wherever we are.


Article: Ethical Storyboarding for Machine Learning

Machine learning is gradient-descending its way into more and more places, and with its arrival comes both increasing demand for skilled ML practitioners and increasingly disruptive challenges to the basic assumptions we hold about our society. The truth is, ML is fast becoming too deeply integrated into our world to keep engineering and ethical concerns separate from each other, and to survive the wave it’s creating we’re going to need a workforce that is acutely aware of how its work ripples through the surrounding world. Where better to start increasing this awareness than the classroom?


Article: AI and the ‘Useless’ Class

Human robots will take your job before AI. The human robot is you, and you will help AI steal your job tomorrow. Will you become ‘useless’?


Article: The Myth of the Impartial Machine

Wide-ranging applications of data science bring utopian proposals of a world free from bias, but in reality, machine learning models reproduce the inequalities that shape the data they’re fed. Can programmers free their models from prejudice?


Article: Simulating the impact of digital and AI on jobs and economy

The aim of the NumJobs project is to provide to firms and organizations an innovative tool to simulate the impact of digital and AI technologies on economies and societies. Digitalization, automation, robotization, rise of artificial intelligence (AI), as many movements and factors which constitute the digital revolution, often founded on innovations of rupture.The bigger issue (or fear depending on point of view) here concerning these digital innovations is that of the impact on employment. Are they sources of job creation, or will they involve destruction – possibly massive – of employment ? If there is job creation, is it through new types of occupation ? And which actual professions would be threatened of extinction? Moreover, beyond the question of creation or destruction, how this digital revolution will modify the nature of work, the actual tasks, the work conditions, the place of work in the life of the individuals and the society ?Thus, the impact of digitalization about employment not only concerns the economy but also the whole society. Innovations lead destructions of existing employment and creations of new jobs. Among them, disruptive innovations create new sectors, and could trigger waves of innovations that are potentially engines of wealth creation by diffusion of purchasing power to the other sectors of the economy. They have however destroying effects on certain types of jobs.Previous industrial revolutions have not induced a net negative effect on the number of jobs. Productivity and new products have raised incomes and demand for products. The key issue we aim to tackle is whether the digital technology has similar effects or may involve negative effects on the number of jobs and their structure.


Article: People + AI Guidebook – Designing human-centered AI products

This Guidebook will help you build human-centered AI products. It’ll enable you to avoid common mistakes, design excellent experiences, and focus on people as you build AI-driven applications. It was written for user experience (UX) professionals and product managers as a way to help create a human-centered approach to AI on their product teams. However, this Guidebook should be useful to anyone in any role wanting to build AI products in a more human-centered way.
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