Google is at CES this week touting all the different partners it has to bring the personal Assistant to speakers, smart displays, phones and the like. One partner is popping mad - wireless speaker pioneer Sonos. The Santa Barbara, California maker of speakers that can be added to home systems for improved sound without that last-century accessory, speaker wire, filed two complaints against Google Tuesday, and called for an immediate cease-and-desist order. If granted, it would mean Google would have to stop selling the Google and Nest Home speakers, Pixel phones and laptops. That was the cease-and-desist request sought by Sonos in a complaint filed with the International Trade Commission, along with a separate patent violation lawsuit in federal court in California.

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a variety of algorithms in attempts to satisfy subsets of these parities or to trade off the degree to which they are satisfied against utility. In this paper, we connect this approach to \emph{fair machine learning} to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future research.

Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regression adjustment for randomly censored regression quantile models. Regression adjustment is based on a new estimating equation that adapts to censoring and leads to quantile score whenever the data do not exhibit censoring. The proposed procedure named {\it censored quantile regression forest}, allows us to estimate quantiles of time-to-event without any parametric modeling assumption. We establish its consistency under mild model specifications. Numerical studies showcase a clear advantage of the proposed procedure.

To ensure the security of the general mass, crime prevention is one of the most higher priorities for any government. An accurate crime prediction model can help the government, law enforcement to prevent violence, detect the criminals in advance, allocate the government resources, and recognize problems causing crimes. To construct any future-oriented tools, examine and understand the crime patterns in the earliest possible time is essential. In this paper, I analyzed a real-world crime and accident dataset of Denver county, USA, from January 2014 to May 2019, which containing 478,578 incidents. This project aims to predict and highlights the trends of occurrence that will, in return, support the law enforcement agencies and government to discover the preventive measures from the prediction rates. At first, I apply several statistical analysis supported by several data visualization approaches. Then, I implement various classification algorithms such as Random Forest, Decision Tree, AdaBoost Classifier, Extra Tree Classifier, Linear Discriminant Analysis, K-Neighbors Classifiers, and 4 Ensemble Models to classify 15 different classes of crimes. The outcomes are captured using two popular test methods: train-test split, and k-fold cross-validation. Moreover, to evaluate the performance flawlessly, I also utilize precision, recall, F1-score, Mean Squared Error (MSE), ROC curve, and paired-T-test. Except for the AdaBoost classifier, most of the algorithms exhibit satisfactory accuracy. Random Forest, Decision Tree, Ensemble Model 1, 3, and 4 even produce me more than 90% accuracy. Among all the approaches, Ensemble Model 4 presented superior results for every evaluation basis. This study could be useful to raise the awareness of peoples regarding the occurrence locations and to assist security agencies to predict future outbreaks of violence in a specific area within a particular time.

As we enter a new decade, we take with us the growing challenges we face in many fields, including artificial intelligence and conducting business while ensuring human rights. These hot topics are not going away any time soon. With the speed of innovation and technology, the responsibility of keeping up with development and regulating practices is all the more crucial to ensure a just world. Our upcoming winter academies on AI and international law, and due diligence as a key to responsible conduct, will empower you with the skills and knowledge you need to tackle those issues in your daily work. Winter academy on Artificial Intelligence and International law (20 – 24 January) 2020 will be a critical year to set the tone for the next decade of innovations in Artificial Intelligence (AI), one of the most complex technologies to monitor or regulate.

In this paper, we proposed a novel automated model, called Vulnerability Index for Population at Risk (VIPAR) scores, to identify rare populations for their future shooting victimizations. Likewise, the focused deterrence approach identifies vulnerable individuals and offers certain types of treatments (e.g., outreach services) to prevent violence in communities. The proposed rule-based engine model is the first AI-based model for victim prediction. This paper aims to compare the list of focused deterrence strategy with the VIPAR score list regarding their predictive power for the future shooting victimizations. Drawing on the criminological studies, the model uses age, past criminal history, and peer influence as the main predictors of future violence. Social network analysis is employed to measure the influence of peers on the outcome variable. The model also uses logistic regression analysis to verify the variable selections. Our empirical results show that VIPAR scores predict 25.8% of future shooting victims and 32.2% of future shooting suspects, whereas focused deterrence list predicts 13% of future shooting victims and 9.4% of future shooting suspects. The model outperforms the intelligence list of focused deterrence policies in predicting the future fatal and non-fatal shootings. Furthermore, we discuss the concerns about the presumption of innocence right.

Artificial intelligence (AI) looks to be the most disruptive class of technologies in driving digital business forward during the next decade. Yet, even amongst the most tech-savvy professionals, there is divided-opinion over what AI can or cannot be defined as, and what it can and can't achieve. The most exciting aspect of applying AI in the legal profession lies in the automation of repetitive and straightforward tasks, like eDiscovery or judicial bill review, while enabling human experts to improve results beyond what machines or people could do alone. This combination allows for enhanced productivity while driving significant time and resource savings. This piece will clarify what AI is, how it's being used today, and how it can improve legal operations -- now and in the future.

The "LegalTech Artificial Intelligence Market" is evolving at an exciting pace driven by changing dynamics and risk ecosystem, an analysis of which forms the crux of the report. The study on the global LegalTech Artificial Intelligence Market takes a closer look at several regional trends and the emerging regulatory landscape to assess its prospects. The critical evaluation of the various growth factors and opportunities in the global LegalTech Artificial Intelligence Market offered in the analyses helps in assessing the lucrativeness of its key segments. Summary of Market: The global LegalTech Artificial Intelligence market is valued at xx million US$ in 2019 is expected to reach xx million US$ by the end of 2025, growing at a CAGR of xx% during 2019-2025. Legal technology, also known asLegal Tech, refers to the use oftechnologyandsoftwareto providelegal services.

Below we summarize some of the significant developments employers should be on the lookout for in the new year. On August 12, 2019, Governor Andrew Cuomo of New York signed into law a bill that, as previously reported, significantly strengthened and expanded workplace anti-discrimination protections in New York State. For additional information regarding the developments already in effect, refer to our previous posts. In terms of changes still to come, contracts and other agreements entered into on or after January 1, 2020, that prevent the disclosure of information relating to any future claim of discrimination on the basis of any protected characteristic will be unenforceable, unless the provision notifies the individual that it does not prohibit them from speaking with law enforcement, the Equal Employment Opportunity Commission, the New York State Division of Human Rights ("NYSDHR"), a local commission on human rights, or an attorney retained by the individual. Likewise, effective February 8, 2020, the New York State Human Rights Law will be expanded to include all employers in the state, regardless of size.

Three police officers were assaulted and six juveniles were arrested after multiple fights broke out at a Mesa skate rink on Saturday night, authorities said. Weekend wrap-up: Here are the biggest Arizona stories from Jan. 3-5 Blowback: Iran abandons nuclear limits after US killing Luxury garage storage company bringing 2 new locations to the Valley Weinstein's reckoning: Trial looms 2 years after #MeToo wave Valley eye surgeon faces multiple charges for alleged billing scheme Weekend wrap-up: Here are the biggest Arizona stories from Jan. 3-5 Counting whales from space pitched as key to saving them Iraq's Parliament calls for expulsion of U.S. troops Tips on how to create, manage your budget in the new year Iraq's Parliament calls for expulsion of U.S. troops Iraq's Parliament calls for expulsion of U.S. troops Arizona ex-fire chief pleads guilty to theft charges A former fire chief accused of embezzling $40,000 from his Arizona district pleaded guilty to felony charges of theft. Valley doctor says soot from candles can be harmful to your health Candles smell good but they're not all that great for your health for one specific reason, according to one Valley doctor. Phoenix lab uses artificial intelligence to slow, manage Alzheimer's disease Arizona is projected to have one of the fastest growing rates of Alzheimer's disease in the country over the next few years, and a clinical lab testing company in the Valley is trying to reverse that. '1917,' 'Once Upon a Time ...in Hollywood' win Golden Globes See winners from the 2020 Golden Globes, hosted by Ricky Gervais.