As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether the decisions made by such systems are discriminatory, through the lens of causal models. We introduce two definitions of group fairness grounded in causality: fair on average causal effect (FACE), and fair on average causal effect on the treated (FACT). We use the Rubin-Neyman potential outcomes framework for the analysis of cause-effect relationships to robustly estimate FACE and FACT. We demonstrate the effectiveness of our proposed approach on synthetic data. Our analyses of two real-world data sets, the Adult income data set from the UCI repository (with gender as the protected attribute), and the NYC Stop and Frisk data set (with race as the protected attribute), show that the evidence of discrimination obtained by FACE and FACT, or lack thereof, is often in agreement with the findings from other studies. We further show that FACT, being somewhat more nuanced compared to FACE, can yield findings of discrimination that differ from those obtained using FACE.

On 21 November 2015, James Bates had three friends over to watch the Arkansas Razorbacks play the Mississippi State Bulldogs. Bates, who lived in Bentonville, Arkansas, and his friends drank beer and did vodka shots as a tight football game unfolded. After the Razorbacks lost 51–50, one of the men went home; the others went out to Bates's hot tub and continued to drink. Bates would later say that he went to bed around 1am and that the other two men – one of whom was named Victor Collins – planned to crash at his house for the night. When Bates got up the next morning, he didn't see either of his friends. But when he opened his back door, he saw a body floating face-down in the hot tub. A grim local affair, the death of Victor Collins would never have attracted international attention if it were not for a facet of the investigation that pitted the Bentonville authorities against one of the world's most powerful companies – Amazon. Collins' death triggered a broad debate about privacy in the voice-computing era, a discussion that makes the big tech companies squirm.

The present study provides a comparative assessment of non-invasive sensors as means of estimating the microbial contamination and time-on-shelf (i.e. storage time) of leafy green vegetables, using a novel unified spectra analysis workflow. Two fresh ready-to-eat green salads were used in the context of this study for the purpose of evaluating the efficiency and practical application of the presented workflow: rocket and baby spinach salads. The employed analysis workflow consisted of robust data normalization, powerful feature selection based on random forests regression, and selection of the number of partial least squares regression coefficients in the training process by estimating the knee-point on the explained variance plot. Training processes were based on microbiological and spectral data derived during storage of green salad samples at isothermal conditions (4, 8 and 12C), whereas testing was performed on data during storage under dynamic temperature conditions (simulating real-life temperature fluctuations in the food supply chain). Since an increasing interest in the use of non-invasive sensors in food quality assessment has been made evident in recent years, the unified spectra analysis workflow described herein, by being based on the creation/usage of limited sized featured sets, could be very useful in food-specific low-cost sensor development.

Machine learning algorithms process vast quantities of data and spot correlations, trends and anomalies, at levels far beyond even the brightest human mind. But as human intelligence relies on accurate information, so too do machines. Algorithms need training data to learn from. This training data is created, selected, collated and annotated by humans. And therein lies the problem.

The area of formal ethics is experiencing a shift from a unique or standard approach to normative reasoning, as exemplified by so-called standard deontic logic, to a variety of application-specific theories. However, the adequate handling of normative concepts such as obligation, permission, prohibition, and moral commitment is challenging, as illustrated by the notorious paradoxes of deontic logic. In this article we introduce an approach to design and evaluate theories of normative reasoning. In particular, we present a formal framework based on higher-order logic, a design methodology, and we discuss tool support. Moreover, we illustrate the approach using an example of an implementation, we demonstrate different ways of using it, and we discuss how the design of normative theories is now made accessible to non-specialist users and developers.

Explaining complex or seemingly simple machine learning models is a practical and ethical question, as well as a legal issue. Can I trust the model? Is it biased? Can I explain it to others? We want to explain individual predictions from a complex machine learning model by learning simple, interpretable explanations. Of existing work on interpreting complex models, Shapley values is the only method with a solid theoretical foundation. Kernel SHAP is a computationally efficient approximation to Shapley values in higher dimensions. Like most other existing methods, this approach assumes independent features, which may give very wrong explanations. This is the case even if a simple linear model is used for predictions. We extend the Kernel SHAP method to handle dependent features. We provide several examples of linear and non-linear models with linear and non-linear feature dependence, where our method gives more accurate approximations to the true Shapley values. We also propose a method for aggregating individual Shapley values, such that the prediction can be explained by groups of dependent variables.

Though Elon Musk has famously warned humanity about the dangers of artificial intelligence, his shareholders might be well-served by having an algorithm on Tesla's board of directors. In recent years, Tesla has become a cautionary tale for how difficult it is for part-time directors to oversee charismatic, strong-willed CEOs--especially ones who are the founding visionaries of their companies. Given how Elon Musk has landed the company in hot water with the Securities and Exchange Commission with his erratic tweets and mocking disregard for the regulatory regime dictating the proper behavior of a publicly traded company, it's little wonder that Tesla's board has been accused of being "asleep at the wheel." Perhaps their seeming unwillingness to rein him in is due to the Tesla directors' personal loyalty to Musk. Or maybe they simply don't want to spend the time to "preapprove" Musk's tweets about the company, especially with the less conventional hours and fast pace the CEO keeps.

The patentability of artificial intelligence (AI) has been increasingly scrutinized in light of the surge in AI technology development and the ambiguity regarding the interpretation of software-related patents. The Federal Circuit has gradually refined the criteria for determining subject matter eligibility for software-related patents, and based in part on such jurisprudence, earlier this year the U.S. Patent and Trademark Office (USPTO) released revised guidance on examining patent subject matter eligibility under 35 U.S.C. §101. See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Considering the advances in AI technology and intellectual property law, how do these recent developments shape the outlook of AI patentability?

On Wednesday night, Tesla sued four former employees and the self-driving startup Zoox for misappropriation of trade secrets. No, you're not having driverless-car lawsuit déjà vu--you're just remembering the time last year when Waymo and Uber settled their own trade secrets case after four days of trial. Tesla's suit, filed in the Northern California federal district court, alleges that four of its former employees took proprietary information related to "warehousing, logistics, and inventory control operations" when they left the electric automaker, and later, while working for Zoox, used that proprietary information to improve its technology and operations. Tesla says the former employees--Scott Turner, Sydney Cooper, Chrisian Dement, and Craig Emigh--worked in product distribution and warehouse supervising. It alleges they forwarded the trade secrets to their own personal email accounts, or the accounts of other former Tesla employees.

Walking around without being constantly identified by AI could soon be a thing of the past, legal experts have warned. The use of facial recognition software could signal the end of civil liberties if the law doesn't change as quickly as advancements in technology, they say. Software already being trialled around the world could soon be adopted by companies and governments to constantly track you wherever you go. Shop owners are already using facial recognition to track shoplifters and could soon be sharing information across a broad network of databases, potentially globally. Previous research has found that the technology isn't always accurate, mistakenly identifying women and individuals with darker shades of skin as the wrong people.