Clear and effective explanations are essential for human understanding and knowledge dissemination. The scope of scientific research aiming to understand the essence of explanations has recently expanded from the social sciences to include the fields of machine learning and artificial intelligence. Important contributions from social sciences include [18, 17, 22, 13, 5, 11] with works that examine critical aspects such as causality (cause-and-effect relationships), contrast (distinctions between differing scenarios), relevance (applicability of explanations), and truth (accuracy and verifiability of explanations). However, machine learning and natural language processing focus more on operational definitions and on the importance of constructing datasets, as seen in studies by [21, 23, 6]. Since explanations for machine learning decisions must be both impactful and human-like [10, 3, 20, 12, 4], a major challenge lies in developing explanations that emphasize proximal aspects -- details that are immediately relevant, direct and related to the user -- over broad algorithmic processes [21].

Data protection authorities (DPAs) broadly believe the regulation's underlying principles of lawfulness, fairness, and transparency make it "future-proof" to cover developments in artificial intelligence (AI), machine learning, cloud computing, and data in a way its predecessor, the 1995 EU Data Protection Directive, failed to do. Many legal experts also believe the GDPR is flexible enough to cope with emerging technologies. Will Richmond-Coggan, director and a specialist in data protection and new technology at law firm Freeths, said, "Although it is often presented as a conflict, the reality is there is very little which technology might make possible that the (U.K. or EU) GDPR would outright prohibit." James Castro-Edwards, privacy and cyber counsel at law firm Arnold & Porter, said, "While the GDPR may not have been drafted with these new technologies specifically in mind, the broad principles of lawfulness, fairness, and transparency still apply, along with a number of additional requirements for higher risk processing." "When data protection rules are difficult to apply in practice, organizations can fall into the trap of believing that avoiding them is a pragmatic approach."

High performing teams have never been so critical to the success of our organisations as they are right now. The commonplace nature of remote working brought about by the pandemic has made working in teams more difficult, despite it never being more important to do so. It is not always easy to coordinate and bond with people who you only ever communicate with on a 13-inch screen. How can we make sure that people from different backgrounds, with different stories and mindsets, can align their efforts to make magic happen when working together remotely? People analytics is at the forefront of most aspirational HR strategies. The most personal element of people analytics is behavioural analysis.

The European Union has introduced a proposal to regulate the development of AI, with the goal of protecting the rights and well-being of its citizens. The Artificial Intelligence Act (AIA) is designed to address certain potentially risky, high-stakes use cases of AI, including biometric surveillance, bank lending, test scoring, criminal justice, and behavior manipulation techniques, among others. The goal of the AIA is to regulate the development of these applications of AI in a way that will foster increased trust in its adoption. Similar to the EU's General Data Protection Regulation (GDPR), the AIA law will apply to anyone selling or providing relevant services to EU citizens. GDPR spearheaded data privacy regulations across the United States and around the world.

I started Practical Deep Learning for Coders 10 days ago. I am compelled to say their pragmatic approach is exactly what I needed. I started data science by learning Python, Pandas, NumPy, and whatever I needed in a short few months. I did whatever courses I need to do (e.g. Kaggle micro-courses) and whatever books I needed to read (e.g.

AI could strip emerging markets of their competitive edge, keep inflation low despite QE, cause huge labour market disruption and drive populism according to a new report. The report – Future proofing your asset allocation in an age of megatrends published by BNY Mellon – shows a huge range of complex challenges facing economies, markets and thus investors from two megatrends – AI and climate change. To compile the report, Create Research conducted detailed interviews with 45 chief investment officers, investment strategists and portfolio managers among asset owners, asset managers and pension consultants in 16 countries. AI is seen as a risk and opportunity by 52%, a risk alone by 33% and a pure opportunity by 7%. The report says that investment-specific challenges related to climate change centre on areas that are unknowable, requiring judgemental calls about the future.

Petra Terasaho works as Stora Enso Group Controller since February 2016. The responsibilities are Group Accounting & Reporting, Group Business Controlling, Internal Controls, Financial Shared Service Center, Finance tools, processes & policies. During the past two years Petra has specialized in finance digitalization, robotics, AI solutions, Advanced Data Analytics and how these changes will impact the finance organization. Petra is driving a big finance transformation and digitalization currently. Prior to Stora Enso Petra worked as Group Controller at Outotec.

Artificial intelligence, and the related concept of automation, have moved from discussion topic for CTOs and CIOs to agenda item in the board-room. Investments are flowing rapidly into the development of AI technologies--including deep learning, machine learning, natural language processing, cognitive and computer vision--both at the startup level as well as within some of the world's leading technology companies. In this interview with Kalyan Kumar, CTO of HCL Technologies, he offers some guiding principles for companies as they adopt AI technologies. How can enterprises begin to implement and reap the benefits of AI? You start with automation, which allows companies to improve efficiency and reduce costs by streamlining repetitive processes.

Most legal expert systems attempt to implement complex models of legal reasoning. This book argues that a complex model is unnecessary. It advocates a simpler, pragmatic approach in which the utility of a legal expert system is evaluated by reference, not to the extent to which it simulates a lawyer's approach to a legal problem, but to the quality of its predictions and of its arguments. The author describes the development of a legal expert system, called SHYSTER, which takes a pragmatic approach to case law. He discusses the testing of SHYSTER in four different and disparate areas of case law, and draws conclusions about the advantages and limitations of this approach to legal expert system development.

By this paper we would like to open a discussion on the need ofBy this paper we would like to open a discussion on the need of Emotional Intelligence as a feature in machines interacting with humans. However, we restrain from making a statement about the need of emotional experience in machines. We argue that providing machines computable means for processing emotions is a practical need requiring implementation of a set of abilities included in the Emotional Intelligence Framework. We introduce our methods and present the results of some of the first experiments we performed in this matter.