In 2019, Forrester predicted that there will be three high profile AI-related PR snafus in 2020. It's only August and we've already seen plenty of examples of AI going wrong -- the ACLU sued facial recognition provider Clearview for violating a well-known Illinois state biometric law in the US, the UK's Home Office was forced to abandon its visa processing algorithm which was deemed to be racist, and researchers recently found that automated speech recognition systems from Amazon, Apple, IBM, Google, and Microsoft perform much worse for black speakers than white ones. AI will continue to err. And it will continue to surface thorny legal and accountability questions, namely -- who is to blame when AI goes wrong? I am not a lawyer, but my father spent his career as a litigator so I posed this question to him when I kicked off this research. His response: "That's easy -- a lawyer would say, 'Sue everybody!'"

Organisations looking to acquire or collaborate with Artificial Intelligence (AI) companies, or acquiring AI technologies, are having to address a host of specific risks in their due diligence procedures. AI, in various forms, has long been pervasive in certain industries. However, it is currently advancing at breakneck pace thanks to the growing sophistication of mathematical models and algorithms, the massive abundance of readily available data and exponential growth in computational power. It remains a frontier technology – an area of huge potential, but also complexity. In its early form, the notion of AI was the quest to make computers do what humans can do.

A game developer can file for a patent on an original gaming idea, a legal process that has been used to strangle video game clones in the past. But getting a patent is a long and arduous process that can fall apart if there's "prior art" predating the idea (or if the mechanic could be considered legally "obvious").

With the introduction of new privacy regulations, machine unlearning is becoming an emerging research problem due to an increasing need for regulatory compliance required for machine learning (ML) applications. Modern privacy regulations grant citizens the right to be forgotten by products, services and companies. This necessitates deletion of data not only from storage archives but also from ML model. The right to be forgotten requests come in the form of removal of a certain set or class of data from the already trained ML model. Practical considerations preclude retraining of the model from scratch minus the deleted data. The few existing studies use the whole training data, or a subset of training data, or some metadata stored during training to update the model weights for unlearning. However, strict regulatory compliance requires time-bound deletion of data. Thus, in many cases, no data related to the training process or training samples may be accessible even for the unlearning purpose. We therefore ask the question: is it possible to achieve unlearning with zero training samples? In this paper, we introduce the novel problem of zero-shot machine unlearning that caters for the extreme but practical scenario where zero original data samples are available for use. We then propose two novel solutions for zero-shot machine unlearning based on (a) error minimizing-maximizing noise and (b) gated knowledge transfer. We also introduce a new evaluation metric, Anamnesis Index (AIN) to effectively measure the quality of the unlearning method. The experiments show promising results for unlearning in deep learning models on benchmark vision data-sets. The source code will be made publicly available.

We introduce Active Predictive Coding Networks (APCNs), a new class of neural networks that solve a major problem posed by Hinton and others in the fields of artificial intelligence and brain modeling: how can neural networks learn intrinsic reference frames for objects and parse visual scenes into part-whole hierarchies by dynamically allocating nodes in a parse tree? APCNs address this problem by using a novel combination of ideas: (1) hypernetworks are used for dynamically generating recurrent neural networks that predict parts and their locations within intrinsic reference frames conditioned on higher object-level embedding vectors, and (2) reinforcement learning is used in conjunction with backpropagation for end-to-end learning of model parameters. The APCN architecture lends itself naturally to multi-level hierarchical learning and is closely related to predictive coding models of cortical function. Using the MNIST, Fashion-MNIST and Omniglot datasets, we demonstrate that APCNs can (a) learn to parse images into part-whole hierarchies, (b) learn compositional representations, and (c) transfer their knowledge to unseen classes of objects. With their ability to dynamically generate parse trees with part locations for objects, APCNs offer a new framework for explainable AI that leverages advances in deep learning while retaining interpretability and compositionality.

A typical information extraction pipeline consists of token- or span-level classification models coupled with a series of pre- and post-processing scripts. In a production pipeline, requirements often change, with classes being added and removed, which leads to nontrivial modifications to the source code and the possible introduction of bugs. In this work, we evaluate sequence-to-sequence models as an alternative to token-level classification methods for information extraction of legal and registration documents. We finetune models that jointly extract the information and generate the output already in a structured format. Post-processing steps are learned during training, thus eliminating the need for rule-based methods and simplifying the pipeline. Furthermore, we propose a novel method to align the output with the input text, thus facilitating system inspection and auditing. Our experiments on four real-world datasets show that the proposed method is an alternative to classical pipelines.

The uprising of Artificial Intelligence machines (hereinafter referred as "AI") is a popular and intriguing subject for many science fiction works. The advancement of AI machines and their progression with respect to playing a significant role in our lives has increased exponentially in the past few years. The future possibilities of this technology has stirred a hornets' nest of innumerable possibilities. As we witness AI machines overlapping with Intellectual Property Rights (IPR), it gives rise to many questions concerning legal discipline. When the earliest substantial work in the field of Artificial Intelligence was concluded in the mid-20th century by the British logician and computer pioneer, Alan Mathison Turing, nobody could have imagined that there will be an attempt towards an assimilation of technical solutions created by an AI machines into the scope of patent law.

Over the past several years, a slew of different methods to measure the fairness of a machine learning model have been proposed. However, despite the growing number of publications and implementations, there is still a critical lack of literature that explains the interplay of fair machine learning with the social sciences of philosophy, sociology, and law. We hope to remedy this issue by accumulating and expounding upon the thoughts and discussions of fair machine learning produced by both social and formal (specifically machine learning and statistics) sciences in this field guide. Specifically, in addition to giving the mathematical and algorithmic backgrounds of several popular statistical and causal-based fair machine learning methods, we explain the underlying philosophical and legal thoughts that support them. Further, we explore several criticisms of the current approaches to fair machine learning from sociological and philosophical viewpoints. It is our hope that this field guide will help fair machine learning practitioners better understand how their algorithms align with important humanistic values (such as fairness) and how we can, as a field, design methods and metrics to better serve oppressed and marginalized populaces.

Several solutions for specifying normative artefacts (norms, contracts, policies) in a computational processable way have been presented in the literature. Legal core ontologies have been proposed to systematize concepts and relationships relevant to normative reasoning. However, no solution amongst those has achieved general acceptance, and no common ground (representational, computational) has been identified enabling us to easily compare them. Yet, all these efforts share the same motivation of representing normative directives, therefore it is plausible that there may be a representational model encompassing all of them. This presentation will introduce DPCL, a domain-specific language (DSL) for specifying higher-level policies (including norms, contracts, etc.), centred on Hohfeld's framework of fundamental legal concepts. DPCL has to be seen primarily as a "template", i.e. as an informational model for architectural reference, rather than a fully-fledged formal language; it aims to make explicit the general requirements that should be expected in a language for norm specification. In this respect, it goes rather in the direction of legal core ontologies, but differently from those, our proposal aims to keep the character of a DSL, rather than a set of axioms in a logical framework: it is meant to be cross-compiled to underlying languages/tools adequate to the type of target application. We provide here an overview of some of the language features.

Welcome to Pushing Buttons, the Guardian's gaming newsletter. If you'd like to receive it in your inbox every week, just pop your email in below – and check your inbox (and spam) for the confirmation email. If you've followed gaming news over the past couple of years, it has been impossible to avoid the many appalling stories about workplace harassment and discrimination that have emerged as part of a long-overdue reckoning in the games industry. As a woman who's worked in the games media for over 15 years, I can only say that I have been grimly unsurprised by the revelations. The consequences that women face for speaking out on these issues has meant that until recently, few were willing to do so publicly.