Talking about acts of violence like mass shootings with your children is not easy. If you have to have that difficult talk, remember the four S's. Video games again have been invoked as one of the causes of violence in the U.S. in the wake of mass shootings this weekend in El Paso, Texas, and Dayton, Ohio. President Donald Trump, who last year held a video game summit after the February 2018 Parkland, Florida, shooting that killed 17 people at Marjory Stoneman Douglas High School, was among several public officials who called out video games as a potential factor in shootings, mentioning video games and violence. President Donald Trump on Monday condemned white nationalism and said he supported "red flag" laws, which could limit a person's access to firearms if the person is determined to be a potential threat to the public.

Facial analysis permits many investigations some of the most important of which are craniofacial identification, facial recognition, and age and sex estimation. In forensics, photo-anthropometry describes the study of facial growth and allows the identification of patterns in facial skull development by using a group of cephalometric landmarks to estimate anthropological information. In several areas, automation of manual procedures has achieved advantages over and similar measurement confidence as a forensic expert. This manuscript presents an approach using photo-anthropometric indexes, generated from frontal faces cephalometric landmarks, to create an artificial neural network classifier that allows the estimation of anthropological information, in this specific case age and sex. The work is focused on four tasks: i) sex estimation over ages from 5 to 22 years old, evaluating the interference of age on sex estimation; ii) age estimation from photo-anthropometric indexes for four age intervals (1 year, 2 years, 4 years and 5 years); iii) age group estimation for thresholds of over 14 and over 18 years old; and; iv) the provision of a new data set, available for academic purposes only, with a large and complete set of facial photo-anthropometric points marked and checked by forensic experts, measured from over 18,000 faces of individuals from Brazil over the last 4 years. The proposed classifier obtained significant results, using this new data set, for the sex estimation of individuals over 14 years old, achieving accuracy values greater than 0.85 by the F_1 measure. For age estimation, the accuracy results are 0.72 for measure with an age interval of 5 years. For the age group estimation, the measures of accuracy are greater than 0.93 and 0.83 for thresholds of 14 and 18 years, respectively.

That starts with recruitment and onboarding. "Having a face-to-face meeting with a human seems to be an incredibly powerful way to communicate," Samer Al Moubayed, co-founder and CEO of Furhat Robotics, told Engadget. However, he points out that even the most experienced and well-trained recruiters occasionally succumb to subconscious biases while conducting interviews -- be they based on age, gender, race or even just a candidate's responses to pre-interview chit-chat. And that's where Furhat's social robot comes in. The 16-inch tall, nearly 8-pound robot is designed to sit at eye level and provide a physical presence with which to interact, as opposed to an onscreen chatbot or virtual phone assistant.

Our lives are increasingly affected by algorithms. People may be denied loans, jobs, insurance policies, or even parole on the basis of risk scores that they produce. Yet algorithms are notoriously prone to biases. For example, algorithms used to assess the risk of criminal recidivism often have higher error rates in minority ethic groups. As ProPublica found, the COMPAS algorithm – widely used to predict re-offending in the US criminal justice system – had a higher false positive rate in black than in white people; black people were more likely to be wrongly predicted to re-offend.

On March 7th, the Center for Responsible Business and the Human Rights Center, in collaboration with Microsoft, hosted the third annual conference on Business, Technology, and Human Rights. The event gathered technologists and practitioners across industry, nonprofit, and academia, as well as students around the topic of Artificial Intelligence (AI) for Social Impact. As student advisory board members at the Center for Responsible Business, we want to be part of the movement to move business to the forefront of social and environmental impact. Attending this conference and meeting some of the leading practitioners in the field of AI gave us the chance to understand how we can ensure this burgeoning technology can help achieve that goal. AI can serve humanity and promote positive social, environmental, and economic outcomes through a focus on human-centered, inclusive AI design. This means adopting a more conscientious design of AI algorithms and tools that puts the full diversity of human needs and consequences at the center.

Corrigibility is a safety property for artificially intelligent agents. A corrigible agent will not resist attempts by authorized parties to alter the goals and constraints that were encoded in the agent when it was first started. This paper shows how to construct a safety layer that adds corrigibility to arbitrarily advanced utility maximizing agents, including possible future agents with Artificial General Intelligence (AGI). The layer counter-acts the emergent incentive of advanced agents to resist such alteration. A detailed model for agents which can reason about preserving their utility function is developed, and used to prove that the corrigibility layer works as intended in a large set of non-hostile universes. The corrigible agents have an emergent incentive to protect key elements of their corrigibility layer. However, hostile universes may contain forces strong enough to break safety features. Some open problems related to graceful degradation when an agent is successfully attacked are identified. The results in this paper were obtained by concurrently developing an AGI agent simulator, an agent model, and proofs. The simulator is available under an open source license. The paper contains simulation results which illustrate the safety related properties of corrigible AGI agents in detail.

More troubling: Studies have shown that risk-assessment algorithms used to figure out criminal sentences tend to make harsher predictions about black defendants than white defendants. And Tay, a chatbot developed by Microsoft, was supposed to figure out how to emulate natural conversation by interacting with Twitter users. Instead, it began communicating in vulgar and racist hate speech.

When it comes to AI (Artificial Intelligence), there's usually a major focus on using large datasets, which allow for the training of models. What may seem like a robust dataset could instead be highly skewed, such as in terms of race, wealth and gender. Then what can be done? Well, to help answer this question, I reached out to Dr. Rebecca Parsons, who is the Chief Technology Officer of ThoughtWorks, a global technology company with over 6,000 employees in 14 countries. She has a strong background in both the business and academic worlds of AI.

Artificial intelligence, specifically machine learning (ML), enables a new world of complex decision-making using novel relationships between data. This paradigm of a system "learning" from data instead of tedious rules-based programming on an outcome, while exciting in its possibilities, opens up a series of new challenges. Distrust, unfairness, bias and ethical ramifications of automated ML decisions are now increasingly common. The recent story about the inadvertent bias in Amazon's recruiting or face recognition software are examples of unforeseen effects of these applications of AI. They occur because, by and large, the relationships absorbed are opaque, thereby dissuading model developers in fixing it.

Like most retail owners, he'd had problems with shoplifting – largely carried out by a relatively small number of repeat offenders. Then a year or so ago, exasperated, he installed something called Facewatch. It's a facial-recognition system that watches people coming into the store; it has a database of "subjects of interest" (SOIs), and if it recognises one, it sends a discreet alert to the store manager. "If someone triggers the alert," says Paul, "they're approached by a member of management, and asked to leave, and most of the time they duly do." Facial recognition, in one form or another, is in the news most weeks at the moment. Recently, a novelty phone app, FaceApp, which takes your photo and ages it to show what you'll look like in a few decades, caused a public freakout when people realised it was a Russian company and decided it was using their faces for surveillance.