"If you look at the key names in the global debate on AI ethics, it is in fact dominated by women who have many different types of backgrounds, not only tech backgrounds." Artificial Intelligence (AI) is the game-changer in the industry, turbocharging new use cases in transportation, law enforcement, e-commerce, retail, healthcare, and entertainment. However, the quick pace of transformation and adoption is not accompanied by concrete industry standards on AI ethics and fairness in Machine Learning algorithms. While ethics in AI have been a dominant narrative for sometime, Big Tech is still seeking ways to design a code of conduct when building ML algorithms. Some tech giants like Microsoft have laid down guidelines to responsible AI and has operationalized responsible AI at scale, others are yet to follow suit.

In 2019, former German Defense Minister Karl-Theodor zu Guttenberg took on a senior leadership position in the artificial intelligence (AI) company Augustus Intelligence. Guttenberg previously urged Europe to take the lead in AI. We have alluded for quite some time to the possibility of Guttenberg leading a united Europe. His gained experience in the United States may further qualify him to lead Europe's digital transformation and more. Augustus Intelligence has recently been involved in a legal dispute with two fired managers, former sales director Marco Pacelli and consultant Ed Crump.

In order to take preventive steps to maintain air quality, forecasting the evolution of pollution levels becomes a useful tool for decision makers: detecting pollution peaks beforehand could give cities enough time to take and communicate effective measures. Multiple research papers have focused on this issue and have dealt with the prediction of air quality. Bai et al. [1] describes the state of the art in this exercise and collects a range of diverse solutions applied to this problem. However, the prediction of the expected value of pollution concentrations through point-forecasting does not provide enough information about the likelihood of the pollutant levels reaching a certain threshold. Indeed, we have an estimate but we usually do not have a description of the confidence of the model nor the uncertainty in the predictions. Therefore, it is difficult to estimate the probability of the pollutant reaching above a certain threshold. The reason this probability estimation is so important is because the measures taken by cities to limit pollution (for example, limiting traffic) impact the daily routines of citizens and prove themselves to be quite unpopular. Therefore, local governments need to have an estimation of the confidence in the prediction to safely engage in those preventive measures.

In an new column, "Tech-Enabled A2J", we will take a look at how legal start-ups and legal technology innovations are impacting the push toward better Access to Justice for more citizens. Whereas LSOs have found past success in reaching clients through basic tools like texting, they are now moving to more advanced platforms like document automation to better streamline internal processes. Some are even going one step further by embarking on artificial intelligence (AI) and machine learning (ML) projects to determine how they can help address the 86% of civil legal problems reported by low-income Americans that aren't fully resolved. Access to justice starts with literal access: figuring out how clients best receive, digest, and act on legal information. On the lower-tech end, text messaging has proven to be a successful tool for reaching those in need.

Consider the grocery clerks at two Safeway stores in the San Francisco Bay Area. A few weeks ago, over 200 workers who are members of the United Food and Commercial Workers Local 5 (UFCW5) union picketed a Safeway store in San Jose, Calif. to voice concerns about a push by parent company Albertsons to add more A.I to its operations. Albertsons recently partnered with the startup Takeoff Technologies to create mini warehouses where computer vision technology automatically sorts items that shoppers order online. Using A.I. reduces the need for Safeway staff to manually locate and grab items for delivery--workers now just retrieve the finalized orders from a conveyor belt and sign off on them for eventual delivery. Several grocery store chains are investing heavily in micro-fulfillment centers after Amazon helped to popularize as-fast-as-you-can deliveries, said Andrew Lipsman, a principal analyst at research firm eMarketer.

Innovation in compliance can come in many forms. One such form was described by Vincent M. Walden, Managing Director at Alvarez and Marsal Holdings, LLC (A&M), in his article entitled "Profit & Loss-of-One"(P&L-of-One). In it, Walden detailed how he and his then colleagues at Ernest & Young (EY) worked in conjunction with the General Electric (GE) compliance function to "improve compliance by using forensic data analytics to provide behavioral insights to their compliance program." They did this through the innovative use of "digital twins" which Walden described as "digital replicas of physical assets that organizations can use for multiple purposes such as the maintenance of power generation equipment, jet engines and heavy machinery." In a more expansive definition, the consulting firm Gartner, Inc. described "digital twins" as dynamic software models of physical things or systems.

Our 2020 presidential candidates will be questioned about their stance on artificial intelligence (AI) policy, especially with regard to the job displacement AI could cause in manufacturing, transportation, and other industries. An over-regulation of AI could hand technical superiority to countries like China and Russia, leading to a ripple effect on America's GDP and even threatening national security. But under-regulation could lead to a massive consolidation of power among a handful of American technology companies, millions of jobs lost without replacement planning, and algorithms that show bias based on age, race, gender, and more. We're certain to hear statements about upskilling -- the process of helping displaced workers acquire new skills so they can find other employment -- and about taxing robots to slow down job loss. But the candidates will need to offer up more than a few soundbites.

Modelling deontic notions through preferences [12] has the advantage of linking deontic notions to the manifold research on preferences, in multiple disciplines, such as philosophy, mathematics, economics and politics. In recent years, preferences have also been addressed within AI [15,8,18] and applications can be found in multi-agent systems [19] and recommender systems [17]. We shall model deontic notions through ceteris-paribus preferences, namely, conditional preferences for a state of affairs over another state of affairs, all the rest being equal. In particular, we shall focus on the ceteris-paribus preference for a proposition over its complement. The idea of ceteris-paribus preferences was originally introduced by the philosopher and logician Georg von Wright [22].