We summarize the potential impact that the European Union's new General Data Protection Regulation will have on the routine use of machine learning algorithms. Slated to take effect as law across the EU in 2018, it will restrict automated individual decision-making (that is, algorithms that make decisions based on userlevel predictors) which "significantly affect" users. The law will also effectively create a "right to explanation," whereby a user can ask for an explanation of an algorithmic decision that was made about them. We argue that while this law will pose large challenges for industry, it highlights opportunities for computer scientists to take the lead in designing algorithms and evaluation frameworks which avoid discrimination and enable explanation.

We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natural disaster response requires an accurate understanding of damaged buildings in an affected region. Current response strategies require in-person damage assessments within 24-48 hours of a disaster. Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life. In collaboration with multiple disaster response agencies, xBD provides pre-and post-event satellite imagery across a variety of disaster events with building polygons, ordinal labels of damage level, and corresponding satellite metadata. Furthermore, the dataset contains bounding boxes and labels for environmental factors such as fire, water, and smoke. xBD is the largest building damage assessment dataset to date, containing 850,736 building annotations across 45,362 km 2 of imagery.

Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. Identifying vessels that evade monitoring systems-known as "dark vessels"-is key to managing and securing the health of marine environments. With the rise of satellite-based synthetic aperture radar (SAR) imaging and modern machine learning (ML), it is now possible to automate detection of dark vessels day or night, under all-weather conditions. SAR images, however, require domainspecific treatment and is not widely accessible to the ML community. Moreover, the objects (vessels) are small and sparse, challenging traditional computer vision approaches. We present the largest labeled dataset for training ML models to detect and characterize vessels from SAR. xView3-SAR consists of nearly 1,000 analysisready SAR images from the Sentinel-1 mission that are, on average, 29,400-by-24,400 pixels each. The images are annotated using a combination of automated and manual analysis. Co-located bathymetry and wind state rasters accompany every SAR image. We provide an overview of the results from the xView3 Computer Vision Challenge, an international competition using xView3-SAR for ship detection and characterization at large scale. We release the data (https://iuu.xview.us/) and code (https://github.com/DIUx-xView) to support ongoing development and evaluation of ML approaches for this important application. l IntroductionRecent advances in remote sensing technology have allowed fishing activity to be tracked across the globe via the Automatic Identification System (AIS) which can broadcast vessels' location [12]. Use of AIS, however, varies by region and fleet; not all vessels are required to carry AIS [29]; some turn their AIS off to engage in illicit activities [23]. This unknown number of non-broadcasting vessels that evade conventional monitoring systems-referred to as "dark" vessels-greatly limits our ability to manage marine resources. Illegal, unreported, and unregulated (IUU) fishing comprises over 20% of all catch around the world [1]. In recent years, the largest IUU fishing offenses were perpetrated by fleets that mostly did not use AIS [23], costing legitimate fishers and governments billions of dollars while also damaging critical ecological systems.Satellite imagery provides an alternative means of sensing dark vessels. Common electro-optical (EO) satellites, however, are limited by cloud coverage and low light conditions. Synthetic aperture radar (SAR) satellites, on the other hand, are able to image in all weather conditions and at nighttime. The European Space Agency (ESA) Sentinel-1 radar satellites cover all coastal waters around the world approximately every six days, offering open access to the full SAR archive. Despite its availability,

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Much has been written about artificial intelligence (AI) perpetuating social inequity and disenfranchising marginalized groups (Barocas in SSRN J, 2016; Goodman in Law and Ethics of AI, 2017; Buolamwini and Gebru in Conference on Fairness, Accountability and Transparency, 2018). It is a sad irony that virtually all of these critiques are exclusively couched in concepts and theories from the Western philosophical tradition (Algorithm Watch in AI ethics guidelines global inventory, 2021; Goffi in Sapiens, 2021). In particular, Buddhist philosophy is, with a few notable exceptions (Hongladarom in A Buddhist Theory of Privacy, Springer, Singapore, 2016; Hongladarom in The Ethics of AI and Robotics A Buddhist Viewpoint, Lexington Book, Maryland, 2020; Hongladarom in MIT Technology Review, 2021; Lin et al. in Robot Ethics: The Ethical and Social Implications fo Robotics, MIT, Cambridge, 2012; Promta and Einar Himma in J Inf Commun Ethics Soc 6(2):172–187, 2008), completely ignored. This inattention to non-Western philosophy perpetuates a pernicious form of intellectual imperialism (Alatas in Southeast Asian J Soc Sci 28(1):23–45, 2000), and deprives the field of vital intellectual resources. The aim of this article is twofold: to introduce Buddhist concepts and arguments to an unfamiliar audience and to demonstrate how those concepts can be fruitfully deployed within the field of AI ethics. In part one, I develop a Buddhist inspired critique of two propositions about privacy: that the scope of privacy is defined by an essential connection between certain types of information and personal identity (i.e., what makes a person who they are), and that privacy is intrinsically valuable as a part of human dignity (Council of the European Union in Position of the Council on General Data Protection Regulation, 2016). The Buddhist doctrine of not self (anattā) rejects the existence of a stable and essential self. According to this view, persons are fictions and questions of personal identity have no ultimate answer. From a Buddhist perspective, the scope and value of privacy are entirely determined by contextual norms—nothing is intrinsically private nor is privacy intrinsically valuable (Nissenbaum in Theor Inq Law 20(1):221–256, 2019). In part two, I show how this shift in perspective reveals a new critique of surveillance capitalism (Zuboff in J Inf Technol 30(1):75–89, 2015). While other ethical analyses of surveillance capitalism focus on its scale and scope of illegitimate data collection, I examine the relationship between targeted advertising and what Buddhism holds to be the three causes of suffering: ignorance, craving and aversion. From a Buddhist perspective, the foremost reason to be wary of surveillance capitalism is not that it depends on systematic violations of our privacy, but that it systematically distorts and perverts the true nature of reality, instilling a fundamentally misguided and corrupting conception of human flourishing. Privacy, it turns out, may be a red herring to the extent that critiques of surveillance capitalism frame surveillance, rather than capitalism, as the primary object of concern. A Buddhist critique, however, reveals that surveillance capitalism is merely the latest symptom of a deeper disease.