Governments are hastening to tap its potential. For most businesses too, the application of AI has moved from being a "nice-to-have" to a "must". In all of its forms and variations, AI and machine learning has started to deliver real value to organisations; automating manual processes, improving customer service and boosting enterprise-wide efficiency. Financial institutions are leading the way, as AI and machine learning technology has come to play an integral role in a range of operations, from portfolio management to fraud prevention. It is also transforming the customer experience.

Google and Alphabet CEO Sundar Pichai takes his sweet time getting to the point in a new Financial Times editorial. But when he gets there, he leaves little room for interpretation: "...there is no question in my mind that artificial intelligence needs to be regulated. It is too important not to." After laying out his relationship with technology and offering a few examples where innovation has had unintended negative consequences, Pichai makes the case that while AI is powerful and useful, we must balance its "potential harms... with social opportunities." Of course, this call for "balance" leaves some questions about how tight the regulation is that Pichai is talking about.

Google CEO Sundar Pichai has explained why the world's governments need to impose regulations on the use of artificial intelligence (AI) beyond principles published by a company. Pichai outlined his thoughts on AI regulation in the Financial Times today, reflecting on Google's own AI principles, which it published in mid-2018 following an outcry from employees over its work on the Pentagon's Project Maven. The project applied Google-developed object recognition AI to drone surveillance technology. Google vowed in its AI principles not to create AI that would harm people, but Pichai noted that "principles that remain on paper are meaningless" without action, pointing to the tools Google has developed and open-sourced to test AI for "fairness". But he also admits that with every major innovation comes potential negative side effects.

Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available supply, historical transport data typically represents a biased, or censored, version of the true underlying demand pattern. Without explicitly accounting for this inherent distinction, predictive models of demand would necessarily represent a biased version of true demand, thus less effectively predicting the needs of service users. To counter this problem, we propose a general method for censorship-aware demand modeling, for which we devise a censored likelihood function. We apply this method to the task of shared mobility demand prediction by incorporating the censored likelihood within a Gaussian Process model, which can flexibly approximate arbitrary functional forms. Experiments on artificial and real-world datasets show how taking into account the limiting effect of supply on demand is essential in the process of obtaining an unbiased predictive model of user demand behavior.

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, towards a better understanding of which mechanisms should be used in different scenarios. The paper then describes the most commonly used fairness-related datasets in this field. Finally, the paper ends by reviewing several emerging research sub-fields of algorithmic fairness.

Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. Firstly, it introduces a taxonomy to analyze the field of machine ethics from an ethical, implementational, and technical perspective. Secondly, an exhaustive selection and description of relevant works is presented. Thirdly, applying the new taxonomy to the selected works, dominant research patterns and lessons for the field are identified, and future directions for research are suggested.

One way of carving up the broad "AI ethics and society" research space that has emerged in recent years is to distinguish between "near-term" and "long-term" research. While such ways of breaking down the research space can be useful, we put forward several concerns about the near/long-term distinction gaining too much prominence in how research questions and priorities are framed. We highlight some ambiguities and inconsistencies in how the distinction is used, and argue that while there are differing priorities within this broad research community, these differences are not well-captured by the near/long-term distinction. We unpack the near/long-term distinction into four different dimensions, and propose some ways that researchers can communicate more clearly about their work and priorities using these dimensions. We suggest that moving towards a more nuanced conversation about research priorities can help establish new opportunities for collaboration, aid the development of more consistent and coherent research agendas, and enable identification of previously neglected research areas.

The head of Google and parent company Alphabet has called for artificial intelligence (AI) to be regulated. Writing in the Financial Times, Sundar Pichai said it was "too important not to" impose regulation but argued for "a sensible approach". He said that individual areas of AI development, like self-driving cars and health tech, required tailored rules. Last week it was revealed that the European Commission is considering a five-year ban on facial recognition. Earlier this month, the White House published its own proposed regulatory principles and urged Europe to "avoid heavy-handed innovation-killing models".

In August of 2018, the Federal Minister of Justice approved the Drager Drug Test 5000 as the Approved Drug Screening Equipment (ADSE) for all Canadian police services. The device itself is costly ($6,000 per device, and $60 per swab) and has to be used under ideal conditions for proper analysis, according to experts. The device tests for commonly used drugs in oral fluids including THC, which is the major psychoactive component in cannabis. Although the device may excel at identifying presence of THC, it does not address the issue of impairment specially when studies do not support a strong correlation between THC levels and impairment. Currently, there's an urgent demand for a device to assist Canadian police officers in their drug impairment investigations which is where PredictMedix is likely to fill an unmet need.

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