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.

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 ...

As we increasingly delegate decision-making to algorithms, whether directly or indirectly, important questions emerge in circumstances where those decisions have direct consequences for individual rights and personal opportunities, as well as for the collective good. A key problem for policymakers is that the social implications of these new methods can only be grasped if there is an adequate comprehension of their general technical underpinnings. The discussion here focuses primarily on the case of enforcement decisions in the criminal justice system, but draws on similar situations emerging from other algorithms utilised in controlling access to opportunities, to explain how machine learning works and, as a result, how decisions are made by modern intelligent algorithms or 'classifiers'. It examines the key aspects of the performance of classifiers, including how classifiers learn, the fact that they operate on the basis of correlation rather than causation, and that the term 'bias' in machine learning has a different meaning to common usage.An example of a real world 'classifier', the Harm Assessment Risk Tool (HART), is examined, through identification of its technical features: the classification method, the training data and the test data, the features and the labels, validation and performance measures. Four normative benchmarks are then considered by reference to HART: (a) prediction accuracy (b) fairness and equality before the law (c) transparency and accountability (d) informational privacy and freedom of expression, in order to demonstrate how its technical features have important normative dimensions that bear directly on the extent to which the system can be regarded as a viable and legitimate support for, or even alternative to, existing human decision-makers.

We study the following problem: given a labeled dataset and a specific datapoint x, how did the i-th feature influence the classification for x? We identify a family of numerical influence measures - functions that, given a datapoint x, assign a numeric value phi_i(x) to every feature i, corresponding to how altering i's value would influence the outcome for x. This family, which we term monotone influence measures (MIM), is uniquely derived from a set of desirable properties, or axioms. The MIM family constitutes a provably sound methodology for measuring feature influence in classification domains; the values generated by MIM are based on the dataset alone, and do not make any queries to the classifier. While this requirement naturally limits the scope of our framework, we demonstrate its effectiveness on data.

An Amazon Echo smart speaker could provide crucial evidence in a double murder case in the US after a judge in New Hampshire ordered the tech giant to provide investigators with recordings from the device. The speaker, which features the artificial intelligence voice assistant Alexa, was seized from a home in Farmington where two women were killed in January 2017. Timothy Verrill, 36, is charged with killing Christine Sullivan and Jenna Pellegrini by stabbing each woman multiple times. Judge Steven M Houran wrote in the court order that an Echo device present in the home may have captured audio that could provide key evidence in the case. How Alexa recorded a family's conversation then sent it to someone How Alexa recorded a family's conversation then sent it to someone "The court finds there is probable cause to believe the server[s] and/or records maintained for or by Amazon.com

FOR the second straight year, Deloitte surveyed executives in the US knowledgeable about cognitive technologies and artificial intelligence,1 representing companies that are testing and implementing them today. We found that these early adopters2 remain bullish on cognitive technologies' value. As in last year's survey, the level of support for AI is truly extraordinary. These findings illustrate that cognitive technologies hold enticing promise, some of which is being fulfilled today. However, AI technologies may deliver their best returns when companies balance excitement over their potential with the ability to execute. A year later, and the thrill isn't gone. In Deloitte's 2017 cognitive survey, we were struck by early adopters' enthusiasm for cognitive technologies.4 That excitement owed much to the returns they said cognitive technologies were generating: 83 percent stated they were seeing either "moderate" or "substantial" benefits. Respondents also said they expected that cognitive technologies would change both their companies and their industries rapidly. In 2018, respondents remain enthusiastic about the value cognitive technologies bring. Their companies are investing in foundational cognitive capabilities, and using them with more skill. Thirty-seven percent of respondents say their companies have invested US$5 million or more in cognitive technologies. Another reason is that companies have more ways to acquire cognitive capabilities, and they are taking advantage.

A judge in New Hampshire has requested that Amazon hand over audio recordings from an Echo device present in a house where two women were found dead. Timothy Verrill is accused of murdering 48-year-old Christine Sullivan and 32-year-old Jenna Pellegrini at a Farmington home in January 2017. Their bodies were found underneath a porch on the property with multiple stab wounds. Mr. Verrill pleaded not guilty the following month. But prosecutors believe that there could be corroborating evidence recorded by an Amazon Echo device which was inside the house.

An Amazon Echo may have captured audio of a cold-blooded killing in New Hampshire, and prosecutors have won the rights to the recordings, a judge ruled Friday. Authorities investigating a 2017 double homicide in the town of Farmington believe that the Alexa voice assistant on the Echo may have recorded one of the killings. The double homicide took place when Timothy Verrill allegedly stabbed Christine Sullivan, 48, and Jenna Pellegrini, 32, to death. Verrill pleaded not guilty to killing the two women. Prosecutors believe the Echo captured both the killing of Sullivan and the removal of her body.

This issue is multifaceted as legal experts try to apply existing law to fast-evolving circumstances -- something that does not always work. There are also differences in national legal systems, so technology companies need to take a global perspective to ensure full realisation of all implications. Another way to protect intellectual property is through patents -- and here the law is even clearer. Patent law requires inventors to be individuals who contributed to the conception or conversion of a concept to a practicality. For example, if an AI created an entirely new semiconductor chip, it could not be protected by patents unless some human intervention took place in the creative process, such as through the person who programmed the AI.

HAL 9000 will soon no longer be science fiction: sentient machines will quickly be with us. As "smart agents" make decisions for human actors a number of important legal issues will emerge. In this short film, LSE Professor Andrew Murray looks at how AI will fundamentally alter our understanding of what the law is and what it can achieve. These themes will be the focus of Professor Murray's forthcoming book - The Objective Self: Identity and Law in the Digital Society.