Companies know that employee turnover is expensive and disruptive. And they know that retaining their best and brightest employees helps them not only save money but also preserve competitive advantages and protect intellectual capital. Most retention efforts, however, rely on two retrospective tools. First, exit interviews are conducted to better understand why people chose to leave, though by this point, it is usually too late to keep them. Second, annual employee surveys are used to assess engagement.

Email messages have been an important mode of communication, not only for work, but also for social interactions and marketing. When messages have time sensitive information, it becomes relevant for the sender to know what is the expected time within which the email will be read by the recipient. In this paper we use a survival analysis framework to predict the time to open an email once it has been received. We use the Cox Proportional Hazards (CoxPH) model that offers a way to combine various features that might affect the event of opening an email. As an extension, we also apply a mixture model (MM) approach to CoxPH that distinguishes between recipients, based on a latent state of how prone to opening the messages each individual is. We compare our approach with standard classification and regression models. While the classification model provides predictions on the likelihood of an email being opened, the regression model provides prediction of the real-valued time to open. The use of survival analysis based methods allows us to jointly model both the open event as well as the time-to-open. We experimented on a large real-world dataset of marketing emails sent in a 3-month time duration. The mixture model achieves the best accuracy on our data where a high proportion of email messages go unopened.

Machine learning and artificial intelligence (AI) systems are rapidly being adopted across the economy and society. These AI algorithms, many of which process fast-growing datasets, are increasingly used to deliver personalised, interactive, 'smart' goods and services that affect everything from how banks provide advice to how chairs and buildings are designed. There is no doubt that AI has a huge potential to facilitate and enhance a large number of human activities and that it will provide new and exciting insights into human behaviour and cognition. The further development of AI will boost the rise of new and innovative enterprises, will result in promising new services and products in – for instance – transportation, health care, education and the home environment. They may transform, and even disrupt, the way public and private organisations currently work and the way our everyday social interactions take place.

Barking off orders to Alexa and Siri without so much as a please or thank you likely isn't going to become a habit you carry over into the rest of your life. This is because adults have already formed their behaviours for interacting with others -- and, in their current form, we don't see smart assistants as people. Researchers came to this conclusion after talking with over 200 people and seeing how they interacted with digital assistants like Alexa, Google Assistant and Siri. However, children may be more susceptible to forming impolite habits from talking to smart assistants -- partly because they are more likely to personify them. Adults may begin to be more influenced by their interactions with smart machines as their designs more more human-like or relatable, however, the researchers added.

The UK's privacy watchdog has opened an investigation into the use of facial recognition cameras in a busy part of central London. The information commissioner, Elizabeth Denham, announced she would look into the technology being used in Granary Square, close to King's Cross station. Two days ago the mayor of London, Sadiq Khan, wrote to the development's owner demanding to know whether the company believed its use of facial recognition software in its CCTV systems was legal. The Information Commissioner's Office (ICO) said it was "deeply concerned about the growing use of facial recognition technology in public spaces" and was seeking detailed information about how it is used. "Scanning people's faces as they lawfully go about their daily lives in order to identify them is a potential threat to privacy that should concern us all," Denham said.

We are increasingly turning to machine learning systems to support human decisions. While decision makers may be subject to many forms of prejudice and bias, the promise and hope is that machines would be able to make more equitable decisions. Unfortunately, whether because they are fitted on already biased data or otherwise, there are concerns that some of these data driven recommendation systems treat members of different classes differently, perpetrating biases, providing different degrees of utilities, and inducing disparities. The examples that have emerged are quite varied: 1. Criminal justice: courts in the United States use COMP AS--a commercially available algorithm to assess a criminal defendant's likelihood of becoming a recidivist--to help them decide who should receive parole, based on records collected through the criminal justice system. In 2016 ProPublica analyzed COMP AS and "found that black defendants were far more likely than white defendants to be incorrectly judged to be at a higher risk of recidivism, while white defendants were more likely than black defendants to be incorrectly flagged as low risk" [1].

The Artificial Intelligence (AI) sector is growing rapidly and is estimated that it will add £630bn to the UK economy alone by 2035. Following the World Intellectual Property Organization (WIPO) report in early 2019, a new report from the UK Intellectual Property Office (UKIPO) now identifies the growth in terms of published AI patent applications. This insight provides an overview of the UKIPO findings and considerations for technology businesses in this space. AI is the use of technology to perform tasks that would usually require some intelligence, if done by humans. A patent is a registered intellectual property right, which seeks to create a monopoly over the exploitation of an invention.

There is growing pressure for more details about the use of facial recognition in London's King's Cross to be disclosed after a watchdog described the deployment as "alarming". Developer Argent has confirmed it uses the technology to "ensure public safety" but did not reveal any details. It raises the issue of how private land used by the public is monitored. The UK's biometrics commissioner said the government needed to update the laws surrounding the technology. Argent is responsible for a 67-acre site close to King's Cross station.

I have commented before that the topic of AI Safety should be equally as much about ensuring the field of artificial intelligence is working for important goals such as climate change or reducing inequality. In this regard I find the UNDP strategy of interest. UNDP works to eradicate poverty and reduce inequalities through the sustainable development of nations. This mission is being carried out in more than 170 countries and territories. Quite recently the UNDP launched its digital strategy for 2019–2021.

There is a lively debate all over the world regarding AI's perceived "black box" problem. Most profoundly, if a machine can be taught to learn itself, how does it explain its conclusions? This issue comes up most frequently in the context of how to address possible algorithmic bias. One way to address this issue is to mandate a right to a human decision per the General Data Protection Regulation's (GDPR) Article 22. Here in the United States, Senators Wyden and Booker propose in the Algorithmic Accountability Act that companies be compelled to conduct impact assessments. Auditability, explainability, transparency and replicability (reproducibility) are often suggested as means of avoiding bias.