A trio of computer scientists from the Rensselaer Polytechnic Institute in New York recently published research detailing a potential AI intervention for murder: an ethical lockout. The big idea here is to stop mass shootings and other ethically incorrect uses for firearms through the development of an AI that can recognize intent, judge whether it's ethical use, and ultimately render a firearm inert if a user tries to ready it for improper fire. That sounds like a lofty goal, in fact the researchers themselves refer to it as a "blue sky" idea, but the technology to make it possible is already here. Predictably, some will object as follows: "The concept you introduce is attractive. Is this AI really feasible, science- and engineering-wise?" We answer in the affirmative, confidently.

In the statement sent Tuesday to Riot employees, the special committee tasked with reviewing the results of the third-party investigation into Laurent outlined a timeline for the investigation, the rules governing the work of the special committee, and ultimately, the group's recommendation that no action be taken. The three-person special committee, a part of Riot's board of directors, is made up of Youngme Moon, a professor at Harvard Business School and the only publicly-named member of Riot Games's board. She is joined by two male C-Level executives at the Chinese tech giant Tencent, which owns Riot Games. The company declined to name these members of the special committee.

Are robots coming for your job? New York Times tech columnist Kevin Roose says companies and governments are increasingly using automation and artificial intelligence to cut costs, transform workplaces and eliminate jobs -- and more changes are coming. "We need to prepare for the possibility that a lot of people are going to fall through the cracks of this technological transformation," Roose says. "It's happened during every technological transformation we've ever had, and it's going to happen this time. And in fact, it already is happening."

AI, or artificial intelligence, seems to be on the tip of everyone's tongue these days. While I've been aware of this major trend in tech development for a while, I've noticed AI appearing more and more as one of the most in-demand areas of expertise for job seekers. I'm sure that for many of us, the term "AI" conjures up sci-fi fantasies or fear about robots taking over the world. The depictions of AI in the media have run the gamut, and while no one can predict exactly how it will evolve in the future, the current trends and developments paint a much different picture of how AI will become part of our lives. In reality, AI is already at work all around us, impacting everything from our search results, to our online dating prospects, to the way we shop.

When Google forced out two well-known artificial intelligence experts, a long-simmering research controversy burst into the open.

Hundreds of people gathered for the first lecture at what had become the world's most important conference on artificial intelligence -- row after row of faces. Some were East Asian, a few were Indian, and a few were women. But the vast majority were white men. More than 5,500 people attended the meeting, five years ago in Barcelona, Spain. Timnit Gebru, then a graduate student at Stanford University, remembers counting only six Black people other than herself, all of whom she knew, all of whom were men. The big thinkers of tech say A.I. is the future.

Businesses and organizations worldwide spend a large amount of time scrutinizing their legal, IT, and data handling services on their efficacy to be data compliant. Data compliance in this tech-driven world is critical and any lapses in adhering to regulations cost companies their reputation and could lead to substantial fines. Conversational systems that collect data and AI automation require that businesses train their staff to manage and deal with data compliance laws in addition to the other services they offer. The General Data Protection Regulations (GDPR) by the European Union in 2018 has imposed every organization in any part of the world that handles personal data to ensure data compliance. Even if businesses are not directly affected by the GDPR, many countries and states have their own data compliance laws that come into force with regards to AI technologies and the information they collect.

In the not-too-distant future, an AI system could generate works with very minimal human input at all. In particular, there is a requirement for a work to be "original" meaning "skill, labour or judgement" must be expended by the author. There are various interpretations of what this could involve. However, it is not clear how it applies to an AI system in practice and whether a machine could meet the test. Specifically, the author (being defined as the person by whom the arrangements necessary for the creation of the work are undertaken) is no longer responsible for creative input and so authorship and creativity must be separated out.

Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these new algorithmic objectives must be communicated transparently in a fairness-aware recommender system. While explanation has a long history in recommender systems research, there has been little work that attempts to explain systems that use a fairness objective. Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation. Here, we consider user perspectives of fairness-aware recommender systems and techniques for enhancing their transparency. We describe the results of an exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives. We propose three features -- informed by the needs of our participants -- that could improve user understanding of and trust in fairness-aware recommender systems.

TYSONS, Va.--(BUSINESS WIRE)--QOMPLX, a leader in cloud-native risk analytics, has entered into a definitive agreement to acquire RPC Tyche LLP ("Tyche"), a rapidly growing insurance software modeling and consulting firm based in London, Cambridge, Paris and Chicago. Tyche bolsters QOMPLX's insurance analytics offerings, and the combined business will offer more comprehensive insurance underwriting, pricing, risk modeling, capital modeling, and reserving functionality. It is an exceptional software business that combines innovative technology with actuarial expertise to help reduce the time and costs that insurers, reinsurers and intermediaries face in producing actionable data feeding today's commercial and regulatory decision-making. Tyche and QOMPLX's combined team are building the insurance data factory of the future with superior capabilities for data integration, transformation, analysis, and contextualization for corporations, employees, and consumers. Tyche's core modeling platform focuses on the complex challenges facing insurers: pricing risks, modeling and reserving capital, and improving efficiency.