Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many of these systems are opaque in their operation, there is a growing demand for such systems to provide explanations for their decisions. Conventional approaches to this problem attempt to expose or discover the inner workings of a machine learning model with the hope that the resulting explanations will be meaningful to the consumer. In contrast, this paper suggests a new approach to this problem. It introduces a simple, practical framework, called Teaching Explanations for Decisions ( TED), that provides meaningful explanations that match the mental model of the consumer. We illustrate the generality and effectiveness of this approach with two different examples, resulting in highly accurate explanations with no loss of prediction accuracy for these two examples.

Nowadays, many sensitive decision-making tasks rely on automated statistical and machine learning algorithms. Examples include targeted advertising, credit scores and loans, college admissions, prediction of domestic violence, and even investment strategies for venture capital groups. There has been a growing concern about errors, unfairness, and transparency of such mechanisms from governments, civil organizations and research societies [2, 33, 40]. That is, whether or not we can prevent discrimination against protected groups and attributes (e.g., race, gender, etc). Clearly, training a machine learning algorithm with the standard aim of loss function minimization (i.e., high accuracy, low prediction error, etc) may result in predictive behaviors that are unfair towards certain groups or individuals [18, 29, 42]. In many real-world applications, we are not allowed to use some sensitive features. For example, EU anti-discrimination law prohibits the use of protected attributes (directly or indirectly) for several decision-making tasks [13]. A naive approach towards fairness is to discard sensitive attributes from training data. However, if other (seemingly) nonsensitive variables are correlated with the protected ones, the learning algorithm may use them to proxy for protected features in order to achieve a lower loss.

At this moment in history it's impossible not to see the problems that arise from human bias. Now magnify that by compute and you start to get a sense for just how dangerous human bias via machine learning can be. But there is potentially a silver machine-learned lining. Because AI can help expose truth inside messy data sets, it's possible for algorithms to help us better understand bias we haven't already isolated, and spot ethically questionable ripples in human data so we can check ourselves. Exposing human data to algorithms exposes bias, and if we are considering the outputs rationally, we can use machine learning's aptitude for spotting anomalies.

While Mongolia may be most sparsely populated independent country in the world, in the European Union that claim goes to Finland. With just 5.5 million people (that's about the same population as Houston and Chicago put together, just without that whole deadly crime thing) Finland has many claims to fame. She is a country of great natural beauty that has influenced generations of minimalist industrial designers. More importantly though, the country follows the Nordic model of capitalism and has thus became one of the few working examples of a progressive, socially sensitive state with superb welfare, education, and healthcare services. It's no surprise then that the country's liberal administration is keen to explore the possibilities offered by artificial intelligence.

Deep learning is a technology with a lot of promise: helping computers "see" the world, understand speech, and make sense of language. But away from the headlines about computers challenging humans at everything from spotting faces in a crowd to transcribing speech -- real-world performance has been more mixed. One deep-learning technology whose real-world results have often disappointed has been facial-recognition. In the UK, police in Cardiff and London used facial-recognition systems on multiple occasions in 2017 to flag persons of interest captured on video at major events. Unfortunately, more than 90% of people picked out by these systems were false matches.

Speech emotion recognition is an important aspect of human-computer interaction. Prior works propose various transfer learning approaches to deal with limited samples in speech emotion recognition. However, they require labeled data for the source task, which cost much effort to collect them. To solve this problem, we focus on the unsupervised task, predictive coding. Nearly unlimited data for most domains can be utilized. In this paper, we utilize the multi-layer Transformer model for the predictive coding, followed with transfer learning approaches to share knowledge of the pre-trained predictive model for speech emotion recognition. We conduct experiments on IEMOCAP, and experimental results reveal the advantages of the proposed method. Our method reaches 65.03% in the weighted accuracy, which also outperforms some currently advanced approaches.

Prosecutors are once again hoping that smart speaker data could be the key to securing a murder conviction. A New Hampshire judge has ordered Amazon to provide recordings from an Echo speaker between January 27th, 2017 and January 29th, 2017 (plus info identifying paired smartphones) to aid in investigating a double homicide case. The court decided there was probable cause to believe the speaker might have captured audio of the murders and their aftermath. Law enforcement had charged Timothy Verrill with murdering Christine Sullivan and Jenna Pellegrini at the home of Sullivan's boyfriend Dean Smoronk. Verrill had access to the home's security code and had been seen on surveillance cameras with the two women, leading investigators to believe that Smoronk's Echo might have picked up additional information.

Last September, a U.K. House of Commons committee concluded that it is too soon to regulate artificial intelligence (AI). Its recommendation comes too late: The EU General Data Protection Regulation (GDPR), which comes into force next year, includes a right to obtain an explanation of decisions made by algorithms and a right to opt-out of some algorithmic decisions altogether. These regulations do little to help consumers, but they will slow down the development and use of AI in Europe by holding developers to a standard that is often unnecessary and infeasible. Although the GDPR is designed to address the risk of companies making unfair decisions about individuals using algorithms, its rules will provide little benefit because other laws already protect their interests in this regard. For example, when it comes to a decision to fire a worker, laws already exist to require an explanation, even if AI is not used. In other cases where no explanation is required, such as refusing a loan, there is no compelling reason to require an explanation on the basis of whether the entity making the decision is a human or a machine.

Homicide mortality is a worldwide concern and has occupied the agenda of researchers and public managers. In Brazil, homicide is the third leading cause of death in the general population and the first in the 15-39 age group. In South America, Brazil has the third highest homicide mortality, behind Venezuela and Colombia. To measure the impacts of violence it is important to assess health systems and criminal justice, as well as other areas. In this paper, we analyze the spatial distribution of homicide mortality in the state of Goias, Center-West of Brazil, since the homicide rate increased from 24.5 per 100,000 in 2002 to 42.6 per 100,000 in 2014 in this location. Moreover, this state had the fifth position of homicides in Brazil in 2014. We considered socio-demographic variables for the state, performed analysis about correlation and employed three clustering algorithms: K-means, Density-based and Hierarchical. The results indicate the homicide rates are higher in cities neighbors of large urban centers, although these cities have the best socioeconomic indicators.

Google employees across the globe took part in a mass walkout in protest over the company's protection of Android mobile software creator, Andy Rubin (pictured in New York in June), who has been accused of sexual assault allegations Google's CEO has admitted'we didn't always do it right', but insists sexual harassment is a societal problem after the tech giant paid out $90m to a sex-pest executive. Thousands of employees took part in a mass walkout, dubbed the'Walkout For Real Change,' one week after Android software creator Andy Rubin was accused of coercing a woman into performing oral sex on him in a hotel in 2013, reported by the New York Times. Google CEO Sundar Pichai took to the stage yesterday, 'It's been a difficult time here,' he told the New York Times DealBook conference. 'There's been anger and frustration within the company. At Google, we set a very high bar, and we clearly didn't live up to our expectations.'