In the field of IP law, however, AI raises new questions and challenges. A research project of the legal departments of the Max Planck Institute for Innovation and Competition led by Professor Reto M. Hilty and Professor Josef Drexl is investigating these issues. The Research Group "Regulation of the Digital Economy" examines whether the existing IP system can fulfil its fundamental functions in the context of AI. Since a sound understanding of technology is indispensable for this task, the members of the group researched technical literature, conducted interviews with practitioners and organized a workshop with international AI researchers. The result is the present paper "Technical Aspects of Artificial Intelligence: An Understanding from an Intellectual Property Law Perspective".

Recent concern about harms of information technologies motivate consideration of regulatory action to forestall or constrain certain developments in the field of artificial intelligence (AI). However, definitional ambiguity hampers the possibility of conversation about this urgent topic of public concern. Legal and regulatory interventions require agreed-upon definitions, but consensus around a definition of AI has been elusive, especially in policy conversations. With an eye towards practical working definitions and a broader understanding of positions on these issues, we survey experts and review published policy documents to examine researcher and policy-maker conceptions of AI. We find that while AI researchers favor definitions of AI that emphasize technical functionality, policy-makers instead use definitions that compare systems to human thinking and behavior. We point out that definitions adhering closely to the functionality of AI systems are more inclusive of technologies in use today, whereas definitions that emphasize human-like capabilities are most applicable to hypothetical future technologies. As a result of this gap, ethical and regulatory efforts may overemphasize concern about future technologies at the expense of pressing issues with existing deployed technologies.

Though used extensively, the concept and process of machine learning (ML) personalization have generally received little attention from academics, practitioners, and the general public. We describe the ML approach as relying on the metaphor of the person as a feature vector and contrast this with humanistic views of the person. In light of the recent calls by the IEEE to consider the effects of ML on human well-being, we ask whether ML personalization can be reconciled with these humanistic views of the person, which highlight the importance of moral and social identity. As human behavior increasingly becomes digitized, analyzed, and predicted, to what extent do our subsequent decisions about what to choose, buy, or do, made both by us and others, reflect who we are as persons? This paper first explicates the term personalization by considering ML personalization and highlights its relation to humanistic conceptions of the person, then proposes several dimensions for evaluating the degree of personalization of ML personalized scores. By doing so, we hope to contribute to current debate on the issues of algorithmic bias, transparency, and fairness in machine learning.

The rise of populist politicians across the rich world has led to a profound rethinking of the way developed economies work. In particular, the impact of automation on the labor market, and the disappearance of routine manufacturing jobs, has been blamed for the electoral successes of leaders, such as US President Donald Trump and Italy's Matteo Salvini. Yet, there are profound differences in what determines the economic winners and losers on the two sides of the Atlantic. In the US, the main factor deciding whether a worker can prosper in the age of robots appears to be education. Conversely, in the European Union, it seems to be whether staff have strong protection in their employment contracts--as many older industrial workers do here. It would be foolish for any government to dissuade companies from investing in machines that are more productive.

COIMBATORE: The nation's constitution is what prevails and has to prevail and it was the duty of every citizen to see that the constitutional values, norms and nuances remain sacrosanct, said former Chief Justice of India, Dipak Misra on Saturday. He was speaking at the inaugural session of the fourth edition of the Indian Cyber Congress held at a private college in the city. "Question arises that who is sovereign amongst the three wings of the state -- executive, legislative or judiciary. On occasions there you will find there are certain utterances that there is'parliamentary sovereignty'. But the Supreme Court time and again has reiterated that there is'constitutional sovereignty', meaning thereby it's the Constitution that prevails and that has to prevail. It's the duty of every citizen to see that the constitutional values, norms and nuances are remain sacrosanct," said Misra.

For years, the Denver public school system worked with Video Insight, a Houston-based video management software company that centralized the storage of video footage used across its campuses. So when Panasonic acquired Video Insight, school officials simply transferred the job of updating and expanding their security system to the Japanese electronics giant. That meant new digital HD cameras and access to more powerful analytics software, including Panasonic's facial recognition, a tool the public school system's safety department is now exploring. Denver, where some activists are pushing for a ban on government use of facial recognition, is not alone. Mass shootings have put school administrators across the country on edge, and they're understandably looking at anything that might prevent another tragedy. Safety concerns have led some schools to consider artificial intelligence-enabled tools, including facial recognition software; AI that can scan video feeds for signs of brandished weapons; even analytics tools that warn when there's been suspicious movement in a usually-empty hallway.

Although numerous ethics courses are available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on software development or data analysis. Technical students often consider these courses unimportant and a distraction from the "real" material. To develop instructional materials and methodologies that are thoughtful and engaging, we must strive for balance: between texts and coding, between critique and solution, and between cutting-edge research and practical applicability. Finding such balance is particularly difficult in the nascent field of responsible data science (RDS), where we are only starting to understand how to interface between the intrinsically different methodologies of engineering and social sciences. In this paper we recount a recent experience in developing and teaching an RDS course to graduate and advanced undergraduate students in data science. We then dive into an area that is critically important to RDS -- transparency and interpretability of machine-assisted decision-making, and tie this area to the needs of emerging RDS curricula. Recounting our own experience, and leveraging literature on pedagogical methods in data science and beyond, we propose the notion of an "object-to-interpret-with". We link this notion to "nutritional labels" -- a family of interpretability tools that are gaining popularity in RDS research and practice. With this work we aim to contribute to the nascent area of RDS education, and to inspire others in the community to come together to develop a deeper theoretical understanding of the pedagogical needs of RDS, and contribute concrete educational materials and methodologies that others can use. All course materials are publicly available at https://dataresponsibly.github.io/courses.

A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. In spite of its fundamental nature however, data collection remains an overlooked part of the machine learning (ML) pipeline. In this paper, we argue that a new specialization should be formed within ML that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics & privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural ML. By showing data collection practices from another field, we encourage ML research to be more cognizant and systematic in data collection and draw from interdisciplinary expertise.

Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and entailing the significant cost of retraining. We propose a simple alternative: the Plug and Play Language Model (PPLM) for controllable language generation, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM. In the canonical scenario we present, the attribute models are simple classifiers consisting of a user-specified bag of words or a single learned layer with 100,000 times fewer parameters than the LM. Sampling entails a forward and backward pass in which gradients from the attribute model push the LM's hidden activations and thus guide the generation. Model samples demonstrate control over a range of topics and sentiment styles, and extensive automated and human annotated evaluations show attribute alignment and fluency. PPLMs are flexible in that any combination of differentiable attribute models may be used to steer text generation, which will allow for diverse and creative applications beyond the examples given in this paper.

Anything you do online can come back to bite you. It's been a decade full of lessons: who to trust, when to speak out and how to stream big events online after you've broken up with your cable company. In 2010, the first iPhone was only three years old. Uber and Lyft didn't exist, and neither did Google Assistant and Siri, Instagram or streaming video. We've come a long way since then, but the next 10 years won't be easy.