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.

Humanoid robots have apparently similar body structure like human beings. Due to their technical design, they are sharing the same workspace with humans. They are placed to clean things, to assist old age people, to entertain us and most importantly to serve us. To be acceptable in the household, they must have higher level of intelligence than industrial robots and they must be social and capable of interacting people around it, who are not supposed to be robot specialist. All these come under the field of human robot interaction (HRI). There are various modes like speech, gesture, behavior etc. through which human can interact with robots. To solve all these challenges, a multimodel technique has been introduced where gesture as well as speech is used as a mode of interaction.

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.

Big data technology and machine learning techniques play a growing role across all areas of modern society. Machine learning provides the ability to predict likely future outcomes, to calculate risks between competing choices, to make sense of vast amounts of data at speed, and to draw insights from data that would be otherwise invisible to human analysts. Despite the significant attention given to machine learning generally in academic writing and public discourse, however, there has been little analysis of how it may affect war-making decisions, and even less analysis from an international law perspective. The advantages that flow from machine learning algorithms mean that it is inevitable that governments will begin to employ them to help officials decide whether, when, and how to resort to force internationally. In some cases, these algorithms may lead to more accurate and defensible uses of force than we see today; in other cases, states may intentionally abuse these algorithms to engage in acts of aggression, or unintentionally misuse algorithms in ways that lead them to make inferior decisions relating to force. This essay's goal is to draw attention to current and near future developments that may have profound implications for international law, and to present a blueprint for the necessary analysis. More specifically, this article seeks to identify the most likely ways in which states will begin to employ machine learning algorithms to guide their decisions about when and how to use force, to identify legal challenges raised by use of force-related algorithms, and to recommend prophylactic measures for states as they begin to employ these tools.

When the Arecibo message was sent into space in 1974 – blasting the most powerful signal ever broadcast deep into the universe – it was a pioneering attempt to reach out to aliens, wherever they might be. When a response came back in 2001, it was a hoax that showed just how much some people hope we can actually communicate with extraterrestrials. But that fake reply might actually be the message we ever get back from aliens. That's because some of the greatest minds on Earth fear rather than hope for contact with extraterrestrial life. Because the real reply to the Arecibo message might actually be something far more terrifying.

If you're a nefarious sort, you might use a commercial drone to smuggle drugs, carry explosives, or to just spy on your neighbors. Drones are appealing to criminals in part because they seem fairly anonymous, flitting through the sky with an invisible digital tether to its owner. But anonymity is no longer a safe bet. In the hands of crime investigators, a drone can reveal a range of personal and financial information about its owner. Most of these details are stored in memory chips inside the drone's circuit board.

A tiny white cube that uses Artificial Intelligence to monitor the lives of elderly people who live alone could save thousands of lives, according to its creator. The miiCube, a kind of'Amazon Echo for the elderly', learns people's routines and tracks their movements so it can alert the relevant authorities if something is wrong. The device will also sense if there's a break in routine, such as not getting up at the normal time or not following usual daily routines. Creator Kelvin Summoogum - who set up miiCARE, the firm behind the gadget, in March - got the idea when his grandmother broke her hip at home. He said she spent twelve hours in agony on the floor before anyone knew until a neighbour found her and brought her to the hospital.

For many avid listeners of public radio, Intelligence Squared U.S. has been a mainstay program for more than ten years. The premise of the show, which debuted in 2006, is reasoned yet passionate debate, with two sides arguing for or against a motion. Recent resolutions include "Globalization Has Undermined America's Working Class" and "The More We Evolve, The Less We Need God." With so much consternation now focused on technology, the show, in partnership with Techonomy, took on Silicon Valley, proposing "Silicon Valley Has Lost Its Soul." Arguing for the motion were Noam Cohen, WIRED contributor and author of The Know-It-Alls: The Rise of Silicon Valley as a Political Powerhouse and Social Wrecking Ball and Dipayan Ghosh, the Pozen Fellow at the Harvard Kennedy School. Holding against were Leslie Berlin, project historian for the Silicon Valley Archives at Stanford, and Joshua McKenty, vice president at Pivotal, and founder and chief architect of NASA Nebula. To see who prevailed in ...

For many criminal organizations, attack techniques are evaluated not only in terms of their effectiveness, but in the overhead required to develop, modify, and implement them. To maximize revenue, for example, they are responding to digital transformation by adopting mainstream strategies, such as agile development to more efficiently produce and refine their attack software, and reducing risk and exposure to increase profitability. Knowing this, one defensive response is to make changes to people, processes, and technologies that impact the economic model of the attacker. For example, adopting new technologies and strategies such as machine learning and automation to harden the attack surface by updating and patching systems or identifying threats forces criminals to shift attack methods and accelerate their own development efforts. In an effort to adapt to the increased use of machine learning and automation on the part of their targets, we predict that the cybercriminal community is likely to adopt the following strategies, which the cybersecurity industry as a whole will need to closely follow.

Business pundits trumpet AI as the future for U.S. employment, but a large-scale survey of U.S. workers indicates that more than 32% are already exposed to some form of AI in their jobs. An additional 6% of workers will begin using AI tools for the first time in 2019. Optimized Workforce – a crowd-sourced think tank that studies the intersection of technology and employment – surveyed more than 10,000 U.S. workers to understand the time they spend on specific tasks, the technologies they work with, and the technologies they will deploy next year to help with those tasks. The survey sampled workers from 19 of the 20 Census Bureau NAICS codes and all of the Bureau of Labor Statistics' top-level occupational codes. The findings, released in a report available on the think tank's Web site, titled "AI Opportunity Report 2018: Which Industries Are Investing in AI? Which Ones Should Be?" reveal that AI-enabled document classification and document creation technologies lead all AI penetration and will continue to see strong investment in 2019.