In his monthly column, Brian Gray from Glasgow-based consultancy Lascivious Marketing offers his thoughts on all things marketing…and perhaps one or two other things. This month he plays Devil's Advocate to the sex robot detractors. Oscar Wilde famously opined that he could resist everything but temptation. Opinion pieces with questionable arguments have the same effect on me. The British newspaper The Guardian published an article on Monday 25th September written by "robotics expert" Jenny Kleeman, titled "Should we ban sex robots while we have the chance?" The sub-heading posits: "AI sex dolls are on their way, with potentially sinister social consequences.

Last week, reports surfaced that the FTC was opening an investigation into Facebook regarding Cambridge Analytica's use of its data. The agency has now confirmed that it is indeed investigating the social media giant. "The FTC takes very seriously recent press reports raising substantial concerns about the privacy practices of Facebook," the FTC said in a statement. "Today, the FTC is confirming that it has an open non-public investigation into these practices." Just over a week ago, a number of reports detailed how Cambridge Analytica obtained Facebook profile information on over 50 million Facebook users through a researcher that collected the data with an app.

The Federal Trade Commission's Bureau of Consumer Protection confirmed Monday that has undertaken a non-public investigation into Facebook's data practices, according to a statement from Tom Pahl, the agency's acting director. The announcement comes just over a week after The New York Times and the The Guardian published explosive reports about the reported improper use of data belonging to 50 million Facebook users by the Trump-campaign affiliated data firm Cambridge Analytica. This isn't the first time the FTC has investigated the social network's data practices. In 2011, Facebook agreed to settle charges--though admitted no actual fault--that it "deceived consumers by telling them they could keep their information on Facebook private, and then repeatedly allowing it to be shared and made public," among other overreaches. The settlement barred Facebook from making further deceptive privacy claims, required it obtain a user's explicit approval before changing the way it handles their data, and mandated that Facebook receive periodic assessments of its privacy practices by third-party auditors for the next 20 years.

One of the latest topics of 2018 is artificial intelligence. It is an automated tool that streamlines various time-consuming legal tasks. Instead of wading through piles of paper, lawyers can manage and store their documents electronically. Contract review and management, which is one of the most sophisticated legal tasks; now with the help of AI it is being processed faster and in a thorough manner than what lawyers may be able to do so on their own.

Artificial intelligence promises to be a paradigm shift for many applications from manufacturing to finance, and from defense to education. Given the vast potential, focus on AI has sharpened around the world, including in China. Decision makers in Beijing and around the country are paying attention and have begun shaping a legal and policy regime that favors the development of AI. Research and investment in AI on both sides of the Pacific has led to cross-border collaboration – both in terms of talent and capital. Last December, Google announced that it will open an AI research center in Beijing, in part to leverage AI talent there. A month earlier, San Diego-based Qualcomm announced a strategic investment in SenseTime, a Chinese company specializing in facial-recognition software.

Artificial intelligence promises to be a paradigm shift for many applications from manufacturing to finance, and from defense to education. Given the vast potential, focus on AI has sharpened around the world, including in China. Decision makers in Beijing and around the country are paying attention and have begun shaping a legal and policy regime that favors the development of AI. Research and investment in AI on both sides of the Pacific has led to cross-border collaboration – both in terms of talent and capital. Last December, Google announced that it will open an AI research center in Beijing, in part to leverage AI talent there. A month earlier, San Diego-based Qualcomm announced a strategic investment in SenseTime, a Chinese company specializing in facial-recognition software.

The World Economic Forum (WEF)'s Global Future Council on Human Rights recently issued a white paper to provide a framework for developers to prevent discrimination in the development and application of machine learning (ML). The paper is based on research and interviews with industry experts, academics, human rights professionals and others working at the intersection of machine learning and human rights. The paper proposes a framework based on four guiding principles - active inclusion, fairness, right to understanding, and access to redress - for developers and businesses looking to use machine learning. Artificial intelligence systems based on machine learning are already being used to make decisions which have significant, life-altering impact on people, such as hiring of job applicants, granting loans and releasing prisoners on parole. Machine learning systems can help to eliminate human bias in decision-making, but they can also end up reinforcing and perpetuating systemic bias and discrimination.

The era of big data led to companies all over the world embracing data as a key competitor driver. The more they knew about their operations, customers and products, the more successful they would be. And now these same companies are embracing artificial intelligence (AI) in order to make sense of all this data. But there's just one problem here: the implementation of AI-based systems is raising a whole host of new legal issues and stimulating a robust public debate about data privacy. It is important, first and foremost, to recognize that data is the "raw material" of artificial intelligence.

Domestic violence (DV) is a global social and public health issue that is highly gendered. Being able to accurately predict DV recidivism, i.e., re-offending of a previously convicted offender, can speed up and improve risk assessment procedures for police and front-line agencies, better protect victims of DV, and potentially prevent future re-occurrences of DV. Previous work in DV recidivism has employed different classification techniques, including decision tree (DT) induction and logistic regression, where the main focus was on achieving high prediction accuracy. As a result, even the diagrams of trained DTs were often too difficult to interpret due to their size and complexity, making decision-making challenging. Given there is often a trade-off between model accuracy and interpretability, in this work our aim is to employ DT induction to obtain both interpretable trees as well as high prediction accuracy. Specifically, we implement and evaluate different approaches to deal with class imbalance as well as feature selection. Compared to previous work in DV recidivism prediction that employed logistic regression, our approach can achieve comparable area under the ROC curve results by using only 3 of 11 available features and generating understandable decision trees that contain only 4 leaf nodes.

A self-driving car operated by Uber struck and killed a woman last week in Tempe, Ariz. Few details have emerged, but it's reportedly the first fatality involving a self-driving vehicle. In January, a Pittsburgh car crash sent two people to the hospital; the accident involved a self-driving Fusion from Ford-backed Argo AI. The Fusion was hit by a truck that ran a red light, and at the last second, the human backup driver reportedly took the car out of autonomous mode and took control of the Fusion's wheel. Could these crashes have been avoided?