The use of artificial intelligence in our everyday lives is on the increase, and it now covers many fields of activity. Something as seemingly banal as avoiding a traffic jam through the use of a smart navigation system, or receiving targeted offers from a trusted retailer is the result of big data analysis that AI systems may use. While these particular examples have obvious benefits, the ethical and legal implications of the data science behind them often go unnoticed by the public at large. Artificial intelligence, and in particular its subfields of machine learning and deep learning, may only be neutral in appearance, if at all. Underneath the surface, it can become extremely personal.

AI is here and it's here to stay - in retail and finance, those recommendations you get on Amazon. While still fairly unsophisticated, the direction of travel suggests rapid and wide-ranging applications in the near future that will touch on every aspect of our lives. But AI is not without its issues – from data and privacy, law, equality. Instead, its applications can shape and reshape the world around us. My reason for this one is to share 5 fascinating one-off podcasts that highlight some of the complex issues in thinking through AI and its applications. Alongside the problems come a whole host of opportunities.

One of the most significant developments in cyber espionage in recent years has been the number of groups adopting "living off the land" tactics. That's our shorthand for the use of operating system features or legitimate network administration tools to compromise victims' networks. The purpose of living off the land is twofold. By using such features and tools, attackers are hoping to blend in on the victim's network and hide their activity in a sea of legitimate processes. Secondly, even if malicious activity involving these tools is detected, it can make it harder to attribute attacks.

Published by the US Patent and Trademark Office, a patent application filed by Microsoft surfaced this week related to a new method of cheat detection in games. Microsoft believes it can leverage technology outside of a game to detect cheating, since current platforms like Xbox Live aren't capable of doing so in their current forms. To this end, Microsoft is working with artificial intelligence. As stated in the patent, platforms that host games aren't necessarily equipped to handle cheating, and they can even reward cheaters through automatic systems, such as achievements. Through artificial intelligence, player activity can be tracked externally, which would allow for more accurate analysis and detection of abnormal behavior.

The use of artificial intelligence in our everyday lives is on the increase, and it now covers many fields of activity. Something as seemingly banal as avoiding a traffic jam through the use of a smart navigation system, or receiving targeted offers from a trusted retailer is the result of big data analysis that AI systems may use. While these particular examples have obvious benefits, the ethical and legal implications of the data science behind them often go unnoticed by the public at large. Artificial intelligence, and in particular its subfields of machine learning and deep learning, may only be neutral in appearance, if at all. Underneath the surface, it can become extremely personal.

We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises when historical decision making blinds us to the true outcome for certain instances. Examples of this are common in many applications, ranging from predicting recidivism using pre-trial release data to diagnosing patients. In this paper we discuss why selective labels often cannot be effectively tackled by standard methods for adjusting for sample selection bias, even if there are no unobservables. We propose a data augmentation approach that can be used to either leverage expert consistency to mitigate the partial blindness that results from selective labels, or to empirically validate whether learning under such framework may lead to unreliable models prone to systemic discrimination.

Domestic Violence (DV) is considered as big social issue and there exists a strong relationship between DV and health impacts of the public. Existing research studies have focused on social media to track and analyse real world events like emerging trends, natural disasters, user sentiment analysis, political opinions, and health care. However there is less attention given on social welfare issues like DV and its impact on public health. Recently, the victims of DV turned to social media platforms to express their feelings in the form of posts and seek the social and emotional support, for sympathetic encouragement, to show compassion and empathy among public. But, it is difficult to mine the actionable knowledge from large conversational datasets from social media due to the characteristics of high dimensions, short, noisy, huge volume, high velocity, and so on. Hence, this paper will propose a novel framework to model and discover the various themes related to DV from the public domain. The proposed framework would possibly provide unprecedentedly valuable information to the public health researchers, national family health organizations, government and public with data enrichment and consolidation to improve the social welfare of the community. Thus provides actionable knowledge by monitoring and analysing continuous and rich user generated content.

In this extended interview from Employment Law This Week (Episode 122: Week of June 25, 2018), Michelle Capezza, a Member of the Firm at Epstein Becker Green, explains how recent legal developments have prepared employers for their future workforce, which will include artificial intelligence technologies working alongside human employees. She also discusses the strategies employers should start to consider as artificial intelligence is incorporated into the workplace. Tune in each week for developments that may affect your business. Click here to subscribe by email - select the checkbox next to Employment Law This Week. Please contact [email protected] and mention whether you were at home or working within a corporate network.

The use of artificial intelligence in our everyday lives is on the increase, and it now covers many fields of activity. Something as seemingly banal as avoiding a traffic jam through the use of a smart navigation system, or receiving targeted offers from a trusted retailer is the result of big data analysis that AI systems may use

Ibrahim Diallo was allegedly fired by a machine. Recent news reports relayed the escalating frustration he felt as his security pass stopped working, his computer system login was disabled, and finally he was frogmarched from the building by security personnel. His managers were unable to offer an explanation, and powerless to overrule the system. Some might think this was a taste of things to come as artificial intelligence is given more power over our lives. Personally, I drew the opposite conclusion. Diallo was sacked because a previous manager hadn't renewed his contract on the new computer system and various automated systems then clicked into action.