Check out the machine learning sessions at the Strata Data Conference in London, May 21-24, 2018. Hurry--best price ends February 23. The problem of fairness comes up in any discussion of data ethics. We've seen analyses of products like COMPASS, we've seen the maps that show where Amazon first offered same-day delivery, and we've seen how job listings shown to women are skewed toward lower-paying jobs. We also know that "fair" is a difficult concept for any number of reasons, not the least of which is the data used to train machine learning models.

There are a lot of frightened lawyers out there, scared that artificial intelligence will gobble up their jobs. Some lawyers are right to be scared: the ones who don't do enough thinking while they make their living. Think of all times when you're on the phone with a customer service person and are getting an answer that makes no sense to you (but seems perfectly fine to him). That rep who can only explain his company's policy with, "That's what the computer is saying," is like the lawyer whose job is doomed. For a bright employment future, you want to be the lawyer who looks at the answers AI produces, not just the one who asks the computer questions.

He predicted that machines would eventually displace people in the workplace and that this would ultimately put the human race at the mercy of technology. This was written on a typewriter at a time when the internet was in its infancy, desktop computers were large, boxy affairs too expensive for most of us, and artificial intelligence was a fringe science, treated with derision by most. He explained: "As society and the problems that face it become more and more complex and as machines become more and more intelligent, people will let machines make more and more of their decisions for them, simply because machine-made decisions will bring better results than man-made ones. "Eventually a stage may be reached at which the decisions necessary to keep the system running will be so complex that human beings will be incapable of making them intelligently. At that stage the machines will be in effective control. People won't be able to just turn the machines off, because they will be so dependent on them that turning them off would amount to suicide.

The tech worker is on the front lines of a major breakthrough in work productivity and business performance: artificial intelligence (AI). The role of AI in the future of almost every industry is practically a given. Business and technology analysts the world over have agreed that AI will have an impact across all industries. For the IT profession, the future could involve being called upon to work with AI, develop AI solutions, and potentially help their customers strike the perfect balance between technology and humanity. Almost every new technology arrives with a fan base claiming it will revolutionise life on Earth.

The rapidly advancing area of artificial intelligence will require a new field of law and new regulations governing a growing pool of businesses involved, according to Microsoft Corp., a 25-year participant in AI research. Companies making and selling AI software will need to be held responsible for potential harm caused by "unreasonable practices" – if a self-driving car program is set up in an unsafe manner that causes injury or death, for example, Microsoft said. And as AI and automation boost the number of laborers in the gig-economy or on-demand jobs, Microsoft said technology companies need to take responsibility and advocate for protections and benefits for workers, rather than passing the buck by claiming to be just the technology platform'' enabling all this change. Microsoft broaches these ideas in a 149-page book entitled "The Future Computed," which will also be the subject of a panel at the World Economic Forum in Davos, Switzerland, next week. As Redmond, Washington-based Microsoft seeks to be a leader in AI and automating work tasks, it's also trying to get out in front of the challenges expected to arise from promising new technologies, such as job losses and everyday citizens who may be hurt or disadvantaged by malfunctioning or biased algorithms.

The 21st century has witnessed AI (Artificial Intelligence) accomplishing tasks like handily defeating humans at chess or teaching them foreign languages quickly. A more advanced task for the computer would be predicting an offender's likelihood of committing another crime. That's the job for an AI system called COMPAS (Correctional Offender Management Profiling for Alternative Sanctions). But it turns out that tool is no better than an average bloke, and can be racist too. Well, that's exactly what a research team has discovered after extensively studying the AI system which is widely used by judicial institutions.

In today's complicated financial landscape accurate models are a necessity for banks to remain competitive, but developing accurate models is challenging. Models are inherently complex -- and if developed poorly can do more harm than good. Minimizing Model Risk with Automated Machine Learning will demonstrate how banks can use Automated Machine Learning to gain a competitive advantage, while quickly aligning their business operation to regulatory requirements. We'll provide an overview of current trends and expectations for model risk management regulatory compliance, and how industry leading financial institutions are leveraging Automated Machine Learning to provide a much stronger framework for model development and validation than traditional manual efforts.

Throughout the near entirety of human history, a population's understanding of what's going on in the world has been controlled by those in power. The men in charge controlled what the people were told about rival populations, the history of their tribe and its leadership, etc. When the written word was invented, men in charge dictated what books were permitted to be written and circulated, what ideas were allowed, what narratives the public would be granted access to. This continued straight on into modern times. Where power is not overtly totalitarian, wealthy elites have bought up all media, first in print, then radio, then television, and used it to advance narratives that are favorable to their interests.

A computer that is used to determine if a criminal is likely to reoffend in courtrooms is'no more accurate than untrained humans', claim scientists. Used across the United States for 20 years, the algorithm decides if a criminal is too high-risk to be released on bail. The researchers say that a group of untrained members of the public contacted through an online survey had the same success rate as the software. Results of the study cast doubt on the accuracy of the machine used in more than one million cases. A computer programme used in US courts has been found to be only as accurate at predicting reoffending as untrained humans that have answered questions in an online survey.

China is adding facial recognition to its overarching surveillance systems in Xinjiang, a Muslim-dominated region in the country's far west that critics claim is under abusive security controls. The geo-fencing tools alert authorities when targets venture beyond a designated 300-meter safe zone, according to an anonymous source who spoke to Bloomberg. Managed by a state-run defense contractor, the so-called "alert project" matches faces from surveillance camera footage to a watchlist of suspects. The pilot forms part of the company's efforts to thwart terrorist attacks by collecting the biometric data of millions of citizens (aged between 12 to 65), which is then linked to China's household registration ID cards. Beijing insists the strict security measures are necessary to tackle numerous incidents of violence and unrest, which it links to Islamic extremists.