But there is no doubt that the pandemic has hastened the adoption of emerging digital technologies, ushered in a new era of remote and flexible working arrangements, increased organisations' reliance on digital infrastructure and exposed our tech-related strengths and weaknesses alike. Leaving 2020 in the rear-view mirror, we count down our top 10 predictions for 2021 and beyond in the domain of Digital Law in Australia. Despite an existing principles-based framework for the protection of privacy under the Privacy Act, in recent years the Federal Government has preferred to introduce parallel privacy requirements, such as the 13 Privacy Safeguards under the Consumer Data Right legislation and the privacy aspects of the upcoming Data Availability and Transparency Act for Government agencies. These nascent regimes are similar enough to the existing privacy regime to encourage complacency and different enough to give any compliance function a headache. Overlapping and often sectorial regulation adds to the increasing complexity of privacy law in Australia.

It's been two weeks since Google fired Timnit Gebru, a decision that still seems incomprehensible. Gebru is one of the most highly regarded AI ethics researchers in the world, a pioneer whose work has highlighted the ways tech fails marginalized communities when it comes to facial recognition and more recently large language models. Of course, this incident didn't happen in a vacuum. Case in point: Gebru was fired the same day the National Labor Review Board (NLRB) filed a complaint against Google for illegally spying on employees and the retaliatory firing of employees interested in unionizing. Gebru's dismissal also calls into question issues of corporate influence in research, demonstrates the shortcomings of self-regulation, and highlights the poor treatment of Black people and women in tech in a year when Black Lives Matter sparked the largest protest movement in U.S. history. In an interview with VentureBeat last week, Gebru called the way she was fired disrespectful and described a companywide memo sent by CEO Sundar Pichai as "dehumanizing." To delve further into possible outcomes following Google's AI ethics meltdown, VentureBeat spoke with five experts in the field about Gebru's dismissal and the issues it raises.

Hate speech detection research has predominantly focused on purely content-based methods, without exploiting any additional context. We briefly critique pros and cons of this task formulation. We then investigate profiling users by their past utterances as an informative prior to better predict whether new utterances constitute hate speech. To evaluate this, we augment three Twitter hate speech datasets with additional timeline data, then embed this additional context into a strong baseline model. Promising results suggest merit for further investigation, though analysis is complicated by differences in annotation schemes and processes, as well as Twitter API limitations and data sharing policies.

Explaining the predictions of AI models is paramount in safety-critical applications, such as in legal or medical domains. One form of explanation for a prediction is an extractive rationale, i.e., a subset of features of an instance that lead the model to give its prediction on the instance. Previous works on generating extractive rationales usually employ a two-phase model: a selector that selects the most important features (i.e., the rationale) followed by a predictor that makes the prediction based exclusively on the selected features. One disadvantage of these works is that the main signal for learning to select features comes from the comparison of the answers given by the predictor and the ground-truth answers. In this work, we propose to squeeze more information from the predictor via an information calibration method. More precisely, we train two models jointly: one is a typical neural model that solves the task at hand in an accurate but black-box manner, and the other is a selector-predictor model that additionally produces a rationale for its prediction. The first model is used as a guide to the second model. We use an adversarial-based technique to calibrate the information extracted by the two models such that the difference between them is an indicator of the missed or over-selected features. In addition, for natural language tasks, we propose to use a language-model-based regularizer to encourage the extraction of fluent rationales. Experimental results on a sentiment analysis task as well as on three tasks from the legal domain show the effectiveness of our approach to rationale extraction.

Artificial intelligence (AI) has been a go-to technology for many people throughout the COVID-19 pandemic. AI has helped businesses solve many issues during this highly disruptive time, from helping to improve the customer experience and detecting fraud to automating work processes. That is helpful, but businesses also need to be aware of the risks of using AI. AI is fed by data that are designed to protect personal privacy, but that is not always what happens. This reality has been highlighted by a number of reported data breaches, some of which have targeted large businesses, like Twitter and Magellan Health.

Artificial Intelligence Technology Solutions, Inc. (OTCPK:AITX) is thrilled to announce that it has restructured the vast majority of its debt, which has saved the company a tremendous amount of dilution. Specifically, AITX has restructured over 85% of its convertible debentures into non-convertible notes and warrants. The remaining note holders have not made any conversions in over two years and the company does not anticipate any further conversions prior to restructuring the remaining balance of the debentures. "This announcement is the result of the extraordinary efforts and commitment by the entire RAD team in creating and building a company that we believe will be the breakthrough leader in an emerging multi-billion dollar industry," said Steve Reinharz, President and CEO of RAD and controlling shareholder of AITX. "I thank all of our clients, investors, supporters and fans for their incredible support throughout this journey. Moving forward, you will see additional announcements regarding additional restructuring and additional financing. We are now in a much stronger position to fulfill our mission."

A EU Fundamental Rights Agency (FRA) report on Monday warned about the risks of artificial intelligence in predictive policing or targeted advertising,as the bloc prepares new rules. "AI is not infallible, it is made by people - and humans can make mistakes. That is why people need to be aware when AI is used, how it works and how to challenge automated decisions," FRA director Michael O'Flaherty said. We celebrate 20 years of independent, expert news on Europe. Become an expert on Europe yourself.

Branches of Co-op in the south of England have been using real-time facial recognition cameras to scan shoppers entering stores. In total 18 shops from the Southern Co-op franchise have been using the technology in an effort to reduce shoplifting and abuse against staff. As a result of the trials, other regional Co-op franchises are now believed to be trialling facial recognition systems. Use of facial recognition by police forces has been controversial with the Court of Appeal ruling parts of its use to be unlawful earlier this year. But its use has been creeping into the private sector, but the true scale of its use remains unknown.

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Problems of cooperation--in which agents seek ways to jointly improve their welfare--are ubiquitous and important. They can be found at scales ranging from our daily routines--such as driving on highways, scheduling meetings, and working collaboratively--to our global challenges--such as peace, commerce, and pandemic preparedness. Arguably, the success of the human species is rooted in our ability to cooperate. Since machines powered by artificial intelligence are playing an ever greater role in our lives, it will be important to equip them with the capabilities necessary to cooperate and to foster cooperation. We see an opportunity for the field of artificial intelligence to explicitly focus effort on this class of problems, which we term Cooperative AI. The objective of this research would be to study the many aspects of the problems of cooperation and to innovate in AI to contribute to solving these problems. Central goals include building machine agents with the capabilities needed for cooperation, building tools to foster cooperation in populations of (machine and/or human) agents, and otherwise conducting AI research for insight relevant to problems of cooperation. This research integrates ongoing work on multi-agent systems, game theory and social choice, human-machine interaction and alignment, natural-language processing, and the construction of social tools and platforms. However, Cooperative AI is not the union of these existing areas, but rather an independent bet about the productivity of specific kinds of conversations that involve these and other areas. We see opportunity to more explicitly focus on the problem of cooperation, to construct unified theory and vocabulary, and to build bridges with adjacent communities working on cooperation, including in the natural, social, and behavioural sciences.