Teaching anyone about "fairness" is a laudable goal. As humans, we may not necessarily agree on what's fair. It sometimes depends on the context. Teaching kids to be fair -- both at home and in school -- is fundamental, but it's easier said than done. With this in mind, how can we, as a society, communicate the nuances of "being fair" to artificial intelligence (AI) systems?

Artificial Intelligence (AI) approaches to problem-solving and decision-making are becoming more and more complex, leading to a decrease in the understandability of solutions. The European Union's new General Data Protection Regulation tries to tackle this problem by stipulating a "right to explanation" for decisions made by AI systems. One of the AI paradigms that may be affected by this new regulation is Answer Set Programming (ASP). Thanks to the emergence of efficient solvers, ASP has recently been used for problem-solving in a variety of domains, including medicine, cryptography, and biology. To ensure the successful application of ASP as a problem-solving paradigm in the future, explanations of ASP solutions are crucial. In this survey, we give an overview of approaches that provide an answer to the question of why an answer set is a solution to a given problem, notably off-line justifications, causal graphs, argumentative explanations and why-not provenance, and highlight their similarities and differences. Moreover, we review methods explaining why a set of literals is not an answer set or why no solution exists at all.

The following is an interview with Daniel Wagner, ahead of the release of his new book, AI Supremacy: Winning in the Era of Machine Learning. Can you briefly explain the differences between artificial intelligence, machine learning, and deep learning? Artificial intelligence (AI) is the overarching science and engineering associated with intelligent algorithms, whether or not they learn from data. However, the definition of intelligence is subject to philosophical debate-even the terms algorithms can be interpreted in a wide context. This is one of the reasons why there is some confusion about what AI is and what is not, because people use the word loosely and have their own definition of what they believe AI is. People should understand AI to be a catch-all term for technology which tends to imply the latest advances in intelligent algorithms, but the context in how the phrase is used determines its meaning, which can vary quite widely. Machine learning (ML) is a subfield of AI that focuses on intelligent algorithms that can learn automatically (without being explicitly programmed) from data.

The legal profession is being challenged now as never before to embrace new technologies to transform the way they deliver legal services. And the impetus is coming from clients, particular general counsel of large multinationals, who in this new digital age are simply refusing to countenance paying for 30 associates to do a job they now know full well an intelligent piece of software can do in their place. Gone are the days of junior litigators barricading themselves in a war room for days, sifting through boxes and boxes of evidence; or trainee conveyancers sweating 100s of hours reviewing a 300-page lease. Today, clients expect this work to be despatched in hours or minutes with the aid of artificial intelligence tools, leaving the humans to focus on the real value-adds: deciding on litigation strategy and tactics; or handling a sensitive lease negotiation. Law firms are now required to become adept in separating out the different strands of the service they provide to clients and working out which bits are still best delivered by (wo)man and which by machine.

Robert Jones was driving home through the pretty town of Todmorden, in West Yorkshire, when he noticed the fuel light flashing on the dashboard of his car. He had just a few miles to find a petrol station, which was cutting things rather fine, but thankfully his GPS seemed to have found a short cut – sending him on a narrow winding path up the side of the valley. Robert followed the machine's instructions, but as he drove, the road got steeper and narrower. After a couple of miles, it turned into a dirt track, but Robert wasn't fazed. After all, he thought, he had "no reason not to trust the satnav".

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. AI Magazine also maintains a calendar listing that includes nonaffiliated conferences at www.aaai.org/Magazine/calendar.php. KR Tenth International Conference on The Thirty-Third AAAI Conference 2018 will be held October 30 - No - Artificial Intelligence and Law. AAAI-19 vember 2, 2018, in Tempe, Arizona, ICAIL-2019 will be held 17-21 June in will be held 27 January - 1 February USA.

As Artificial Intelligence (AI) technologies proliferate, concern has centered around the long-term dangers of job loss or threats of machines causing harm to humans. All of this concern, however, detracts from the more pertinent and already existing threats posed by AI today: its ability to amplify bias found in training datasets, and swiftly impact marginalized populations at scale. Government and public sector institutions have a responsibility to citizens to establish a dialogue with technology developers and release thoughtful policy around data standards to ensure diverse representation in datasets to prevent bias amplification and ensure that AI systems are built with inclusion in mind.

Bogota - Carmenza Gomez was planning a surprise Christmas dinner in the winter of 2008 to celebrate having her eight children back together under one roof in their home in an impoverished suburb in Bogota, the capital of Colombia. That summer, the family had finally been reunited after years apart due to the sons' military service. It was months away, but Carmenza wanted to throw an elaborate dinner to share their first Christmas together in years. But just days after the last of her sons arrived home, 23-year-old Victor Fernando, her third youngest, disappeared. "I didn't tell any of them what I was planning [for Christmas]," Carmenza recalled nearly a decade later.

Continuing advancements in artificial intelligence (AI) for use in both the public and private sectors warrant serious ethical consideration. As the capability of AI improves, the issues of transparency, fairness, privacy, and accountability associated with using these technologies become more serious. Many developers in the private sector acknowledge the threats AI poses and have created their own codes of ethics to monitor AI development responsibly. However, many experts believe government regulation may be required to resolve issues ranging from racial bias in facial recognition software to the use of autonomous weapons in warfare. On Sept. 14, the Center for Technology Innovation hosted a panel discussion at the Brookings Institution to consider the ethical dilemmas of AI.

If you're a black or Asian user of gay dating app Grindr, then it's possible you've encountered racism while using it. Some users of the app have said they've come across what they believe are discriminatory statements on other profiles - things like "no blacks and no Asians". Others say they've faced racist comments in conversation with users when they've rejected their advances. Now Grindr has taken a stand against discrimination on its platform and says no user is entitled to tear another down for "being who they are". It's launched the #KindrGrindr campaign to raise awareness of racism and discrimination and promote inclusivity among users. It says it will ban users who "bully or defame" others and will remove offensive language from profiles.