When you think of a lawyer's office, you typically visualize a cabin with row of books stacked up from top to bottom. Now imagine if they didn't have to read them either! ROSS Intelligence, an artificial intelligence based legal research software that gives answers to questions put to it in plain English. For instance, one could type in "is a non-compete clause legal in India? What is the position of the Bombay High Court?" and have Ross generate a response backed up with references including relevant judgments and readings.

Fixing algorithms may not be the best response to bias. Ethicist Tom Douglas offers a more radical approach to creating fairness, that aims for'substantive' rather than'procedural' fairness outside of design. 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.

Artificially intelligent systems, given a set of non-trivial ethical rules to follow, will inevitably be faced with scenarios which call into question the scope of those rules. In such cases, human reasoners typically will engage in interpretive reasoning, where interpretive arguments are used to support or attack claims that some rule should be understood a certain way. Artificially intelligent reasoners, however, currently lack the ability to carry out human-like interpretive reasoning, and we argue that bridging this gulf is of tremendous importance to human-centered AI. In order to better understand how future artificial reasoners capable of human-like interpretive reasoning must be developed, we have collected a dataset of ethical rules, scenarios designed to invoke interpretive reasoning, and interpretations of those scenarios. We perform a qualitative analysis of our dataset, and summarize our findings in the form of practical recommendations.

Companies, policymakers, and scholars alike are paying increasing attention to algorithmic recruitment and hiring tools that leverage artificial intelligence, machine learning, and Big Data. To its advocates, algorithmic employee selection processes can be more effective in choosing the strongest candidates, increasing diversity, and reducing the influence of human prejudices. Many observers, however, express concern about other forms of bias that can infect algorithmic selection procedures, leading to fears regarding the potential for algorithms to create unintended discriminatory effects or mask more deliberate forms of discrimination. This article represents the most comprehensive analysis to date of the legal, ethical, and practical challenges associated with using these tools. The article begins with background on both the nature of algorithmic selection tools and the legal backdrop of antidiscrimination laws. It then breaks down the key reasons why employers, courts and policymakers will struggle to fit these tools within the existing legal framework.

An Israeli artificial intelligence startup with investments from a number of American companies including Microsoft has been linked to the biometric surveillance of Palestinians. AnyVision is an international tech company based in Israel that raised $78 million in June from an investment group including American tech giant Microsoft. One of their flagship products -- dubbed'Better Tomorrow' -- is a platform that leverages biometrics and facial recognition software to track objects and people on live video, including across independent camera feeds. NBC and Israeli news site Haaretz report that this technology is at the centre of a military surveillance operation focused in the West Bank at "at least 27 checkpoints", according to a statement from the Israeli Defence Forces from February. The aim of the operation is to "upgrade the crossings" and "deter terror attacks" using a network of 1,700 cameras featuring biometric and facial recognition capabilities.

Police Scotland has unveiled a new aerial drone system to help in searches for missing and vulnerable people. The remotely-piloted aircraft system (RPAS) can see things we can't to try to work out where people are. It uses advanced cameras and neural computer networks to spot someone it is looking for - from "a speck" up to 150 metres away. Its recognition software is compact enough to be run on a phone, with the technology learning as it goes. "The drone itself has very special sensors on it," said Insp Nicholas Whyte, of Police Scotland's air support unit.

This fair usage agreement ("Agreement") is between CyberExtruder.com, Inc., a New York corporation with its principal office located at 1401 Valley Road, Wayne, New Jersey, 07470, USA ("Company") and the user of the Data Set, as defined below ("Licensee"). Whereas the Licensee is interested in the fair use of the Data Set for the non-commercial purposes of testing face recognition algorithms, and the Company wants to facilitate Licensee's testing of face recognition algorithms, the parties agree as follows: The Data Set contains 10,205 images of 1000 people collected randomly from the internet and is unrestricted with regard to the subject's pose, environmental lighting conditions, facial expression, subject's race and subject's age and contains images which are artistic impressions, drawings, paintings and other non-photographic representations of faces, and a multitude of facial occlusions like hats, glasses and makeup. All images are sized to 600 x 600 pixels and are stored with jpeg compression. LICENSE GRANT A non-exclusive, nontransferable, royalty-free license is granted to Licensee to use the Data Set on an appropriate computer system located at Licensee's premises. The Company is free, at its sole discretion, to distribute the Data Set to others and to use it for its own purposes.

If you ask a hundred lawyers whether a software system or a robot can buy a house or file a lawsuit, all of them would be likely to answer'no.' But because of the extreme flexibility of limited liability companies (LLCs) under US law, software and other artificial systems in fact can get basic legal personhood or at least a very close surrogate of it. That is, they can enter contracts, own property, sue, and be sued. The modern American LLC is an amazingly flexible structure. At its core, it is a legally recognized entity controlled however its organizers want it to be controlled.

A new report suggests consumers fully expect more automation and technology to be involved in their interactions with brands over the next ten years, but that presents a fine line between effectiveness and privacy for brands to walk. The report is based on a survey of 4,000 respondents across three dozen countries, conducted by Futurum Research on behalf of analytics company SAS. The respondents were a mix of consumers and marketing professionals and were asked about their anticipated use of technology in the year 2030. The main finding of the report is that there will be an increasing openness to automation when it comes to interactions between brands and consumers. According to SAS' analysis of the data, 67% of engagements done through digital channels in 2030 will be completed by smart systems, while 69% of business decisions made during that engagement will be completed by smart machines.

AI holds the greatest promise for eliminating bias in hiring for two primary reasons. It can eliminate unconscious human bias, and it can assess the entire pipeline of candidates rather than forcing time-constrained humans to implement biased processes to shrink the pipeline from the start. Like any new technology, artificial intelligence is capable of immensely good or bad outcomes. The public seems increasingly focused on the bad, especially when it comes to the potential for bias in AI. This concern is both well-founded and well-documented. It is the simulation of human processes by machines.