More and more, human resources managers rely on data-driven algorithms to help with hiring decisions and to navigate a vast pool of potential job candidates. These software systems can in some cases be so efficient at screening resumes and evaluating personality tests that 72% of resumes are weeded out before a human ever sees them. But there are drawbacks to this level of efficiency. Man-made algorithms are fallible and may inadvertently reinforce discrimination in hiring practices. Any HR manager using such a system needs to be aware of its limitations and have a plan for dealing with them.

If you have recently received a parking ticket, you can use the services of a robot lawyer to help. The robot lawyer asks as series of questions like where the ticket was issued, a description of what happened and within a few minutes, you can have a 500-word letter to send to the city to contest the parking ticket. This bot lawyer has, so far, helped overturn more than 200,000 parking tickets. If you are looking at getting a divorce, wevorce can help. Wevorce's web-based platform allows couples to go through a collaborative divorce -- one in which both partners work together to decide how to split assets and figure out how to coparent.

Automated decision making and the difficulty of ensuring accountability for algorithmic decisions have been in the news. This is a big deal if we are to start addressing some of the serious ethical issues in developing Artificial Intelligence systems that can't easily be made transparent. I'm breaking out of a concentrated book-writing space to offer my voice – and to outline some of the directions I think we should be taking to address the wicked problems of ethics, algorithms and accountability – and hoping also to be standing up to be counted as one of the people opening out discussions in this space, so that it can be more diverse. A few months ago I submitted a response to the UK's Science and Technology Committee consultation on automated decision making. This consultation asked specifically how transparency could be empoyed to allow more scrutiny of algorithmic systems.

THE human face is a remarkable piece of work. The astonishing variety of facial features helps people recognise each other and is crucial to the formation of complex societies. So is the face's ability to send emotional signals, whether through an involuntary blush or the artifice of a false smile. People spend much of their waking lives, in the office and the courtroom as well as the bar and the bedroom, reading faces, for signs of attraction, hostility, trust and deceit. They also spend plenty of time trying to dissimulate.

Researchers from the University of California at Los Angeles have developed a new way to measure air quality. They have developed quite a unique mechanism to measure air quality for cheap -- a mobile microscope connected to a smartphone which will detect the air quality using a machine-learning algorithm. It uses an air sampler along with a holographic microscope of the size of a microchip. The aim of the invention is to provide users the ability to accurately assess dangerous airborne particulate matter and avoid health hazards, which kill 7 million people prematurely annually according to the World Health Organization. Accurate analysis of air quality is very important for improving this air quality and the researchers claim this device might be capable of such analysis.

The Justice Department is investigating whether Uber illegally used software to track drivers for Lyft, its main ride-hailing competitor, to gain an advantage in attracting and recruiting drivers, according to two people familiar with the probe. The FBI and the U.S. attorney's office in New York's Southern District want to know if use of the software, which created fake customer accounts, broke any federal laws, said the people, who didn't want to be identified because they were not authorized to discuss the case publicly. An Uber spokeswoman said Friday it is cooperating in the probe and that use of the software has been discontinued. The U.S. attorney's office would not comment on the case. The investigation adds to mounting legal problems for Uber, including allegations of corporate espionage involving autonomous vehicle technology and at least one other federal investigation into use of software to thwart local government efforts to monitor its operations.

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As the integration of Artificial Intelligence, Machine Learning and Big Data in the workplace is becoming the new norm, concerns are increasing over how the technology could affect employee performance, job security and the adaptation and reliance of technology in the legal industry. Raj Goyle, former Kansas State Representative, Harvard Law graduate and Co-Founder/CEO of LawTech platform Bodhala, sat down with Inside Counsel to discuss why AI, Machine Learning and Big Data are not costing jobs when correctly implemented, how technology eliminating inefficiencies within the workplace, and why is legal one of the last spaces to embrace tech integration.

Should artificial intelligence be regulated? And if so, what should those regulations look like? These are difficult questions to answer for any technology still in development stages – regulations, like those on the food, pharmaceutical, automobile and airline industries, are typically applied after something bad has happened, not in anticipation of a technology becoming dangerous. But AI has been evolving so quickly, and the impact of AI technology has the potential to be so great that many prefer not to wait and learn from mistakes, but to plan ahead and regulate proactively. In the near term, issues concerning job losses, autonomous vehicles, AI- and algorithmic-decision making, and "bots" driving social media require attention by policymakers, just as many new technologies do. In the longer term, though, possible AI impacts span the full spectrum of benefits and risks to humanity – from the possible development of a more utopic society to the potential extinction of human civilization.