"creating a specific legal status for robots in the long run, so that at least the most sophisticated autonomous robots could be established as having the status of electronic persons responsible for making good any damage they may cause, and possibly applying electronic personality to cases where robots make autonomous decisions or otherwise interact with third parties independently." In this article, we ask whether a purely synthetic entity could and should be made a legal person. Drawing on the legal and philosophical framework used to evaluate the legal personhood of other non-human entities like corporations, we argue that the case for electronic personhood is weak. Though this article begins with philosophical premises, its orientation is ultimately pragmatic. A legal system by the people exists ultimately to protect the interests of the people.

A collective eyebrow was raised by the AI and robotics community when the robot Sophia was given Saudia citizenship in 2017 The AI sharks were already circling as Sophia's fame spread with worldwide media attention. Were they just jealous buzz-kills or is something deeper going on? Sophia is not the first show robot to attain celebrity status. Yet accusations of hype and deception have proliferated about the misrepresentation of AI to public and policymakers alike. In an AI-hungry world where decisions about the application of the technologies will impact significantly on our lives, Sophia's creators may have crossed a line.

OSAKA – Despite advances in facial recognition technology, the police in Osaka still rely on pure skill to find fugitives, with investigators using only their memory to arrest dozens of wanted criminals every year. While other police forces in the world have "super recognizer" units that hunt down fugitives, the so-called miatari (look and hit) technique used in Osaka has contributed to the arrests of over 4,000 criminals in Japan since the Osaka Prefectural Police introduced it as a formal investigative method in November 1978. There has not been a single wrongful arrest. "The best part of this method is being able to detect fugitives who are hard to find in normal investigations," said a senior investigator in Osaka. He says a forensic analysis is an imperative part of criminal investigations, but "we want to pass on the tradition because our job is to make sure no one gets away with a crime."

Technology is revolutionizing the work we do and how we do it. Increasingly, artificial intelligence (AI) and robots are taking over menial and repetitive tasks, leaving humans to concentrate on work that requires critical thinking. But as machines become better at imitating human intelligence, they're beginning to do more and more thinking for us.

As AI becomes more advanced, more and more aspects of our daily life are touched by invisible algorithms. However, the more we entrust vital decisions to software, the greater the need becomes to interrogate how they work, and why they reach the conclusions they do. Concern has been slowly bubbling, with the book'Weapons of Math Destruction' by Cathy O'Neil highlighting the ways in which these algorithms can influence crucial decision making processes including whether to grant a loan, who to hire, college admissions, and bail decisions. One of the most potent dangers of algorithms is how they incorporate and perpetuate intentional and unintentional bias. Rachel Bellamy leads the IBM Research Human-Agent Collaboration group which examines, among other things, cognitive bias and how it's coded into AI.

Amazon boss Jeff Bezos has warned staff not to be complacent, claiming the firm'is not too big to fail' At an all-hands meeting last Thursday in Seattle, days before the firm announced the winners of its HQ2 contest, Bezos was asked about the recent failures of giant retailers like Sears. 'Amazon is not too big to fail,' Bezos said, in a recording of the meeting CNBC said it had heard. 'In fact, I predict one day Amazon will fail. If you look at large companies, their lifespans tend to be 30-plus years, not a hundred-plus years.' Bezos told the meeting the key to survival is to'obsess over customers'.

An MIT study has revealed the way artificial intelligence system collect data often makes them racist and sexist. Researchers looked at a range of systems, and found many of them exhibited a shocking bias. The team then developed system to help researchers make sure their systems are less biased. 'Computer scientists are often quick to say that the way to make these systems less biased is to simply design better algorithms,' said lead author Irene Chen, a PhD student who wrote the paper with MIT professor David Sontag and postdoctoral associate Fredrik D. Johansson. 'But algorithms are only as good as the data they're using, and our research shows that you can often make a bigger difference with better data.'

Intersectionality is a framework that analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability. Intersectionality theory therefore implies it is important that fairness in artificial intelligence systems be protected with regard to multi-dimensional protected attributes. However, the measurement of fairness becomes statistically challenging in the multi-dimensional setting due to data sparsity, which increases rapidly in the number of dimensions, and in the values per dimension. We present a Bayesian probabilistic modeling approach for the reliable, data-efficient estimation of fairness with multi-dimensional protected attributes, which we apply to novel intersectional fairness metrics. Experimental results on census data and the COMPAS criminal justice recidivism dataset demonstrate the utility of our methodology, and show that Bayesian methods are valuable for the modeling and measurement of fairness in an intersectional context.

In this paper we introduce a novel pattern match neural network architecture that uses neighbor similarity scores as features, eliminating the need for feature engineering in a disfluency detection task. We evaluate the approach in disfluency detection for four different speech genres, showing that the approach is as effective as hand-engineered pattern match features when used on in-domain data and achieves superior performance in cross-domain scenarios.

While these bring tremendous benefits, AI also raises concerns, ranging from security, to human rights abuses. Speaking in Paris last weekend, Secretary-General Antonio Guterres praised AI but cautioned that "technology should empower not overpower us" and that the world needs to set policies that contain unintended consequences or malicious use of frontier technologies. UN News asked Eleonore Pauwels, Research Fellow on Emerging Cybertechnologies at United Nations University (UNU), about AI – what it is, how it works, and what she sees happening in the next few years. In its current form, called "deep learning", AI is a growing set of autonomous and self-learning algorithms she told us, capable of performing tasks it was commonly thought could only be done by the human brain. At its core, AI produces powerful predictive reasoning while minimizing the noise from unpredictable and complex human behaviour.