The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

As Machine Learning technologies become increasingly used in contexts that affect citizens, companies as well as researchers need to be confident that their application of these methods will not have unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers to the domain. This article seeks to provide an overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature. It organises approaches into the widely accepted framework of pre-processing, in-processing, and post-processing methods, subcategorizing into a further 11 method areas. Although much of the literature emphasizes binary classification, a discussion of fairness in regression, recommender systems, unsupervised learning, and natural language processing is also provided along with a selection of currently available open source libraries. The article concludes by summarising open challenges articulated as four dilemmas for fairness research.

Deep neural networks are becoming more and more popular due to their revolutionary success in diverse areas, such as computer vision, natural language processing, and speech recognition. However, the decision-making processes of these models are generally not interpretable to users. In various domains, such as healthcare, finance, or law, it is critical to know the reasons behind a decision made by an artificial intelligence system. Therefore, several directions for explaining neural models have recently been explored. In this thesis, I investigate two major directions for explaining deep neural networks. The first direction consists of feature-based post-hoc explanatory methods, that is, methods that aim to explain an already trained and fixed model (post-hoc), and that provide explanations in terms of input features, such as tokens for text and superpixels for images (feature-based). The second direction consists of self-explanatory neural models that generate natural language explanations, that is, models that have a built-in module that generates explanations for the predictions of the model.

Artificial intelligence (AI) and deep learning models can help advance research on neural degeneration, showing its capabilities in identifying and categorizing its forms on a model organism. Using the organism Caenorhabditis elegans or the roundworm – a 1-millimeter near-transparent nematode – researchers used deep learning to conduct a quantitative image-based analysis of neural degeneration patterns observed in the PVD neuron of the organism. Researchers from North Carolina State University have detailed their work in the journal BMC Biology, September 23. The worms were found alive last week in a biological container that was among the debris from the Space Shuttle Columbia recovered in East Texas. The worms are descendants of those that were part of an experiment that flew on Columbia's last mission before the spacecraft broke up on reentry February 1, killing all seven astronauts.

The Legal Affairs Committee adopted three reports on Thursday on specific issues linked to the increased development and use of artificial intelligence systems. The Commission is expected to put forward a legislative proposal on the matter in early 2021. The legislative initiative by Iban García del Blanco (S&D, ES), adopted with 20 votes in favour, none against and 4 abstentions, urges the EU Commission to present a new legal framework outlining the ethical principles to be used when developing, deploying and using artificial intelligence, robotics and related technologies in the EU, including software, algorithms and data. MEPs adopted proposals on several guiding principles that must be taken into account by future laws including a human-centric, human-made and human-controlled AI; safety, transparency and accountability; safeguards against bias and discrimination; right to redress; social and environmental responsibility, and respect for fundamental rights. When it comes to AI with machine-learning (self-improving) capacities, it should be designed to allow for human oversight.

The world has evolved rapidly in the last few years and artificial intelligence (AI) has often been leading the change. The technology has been adopted by almost every industry with companies wanting to explore how AI can automate processes, increase efficiency, and improve business operations. AI has certainly proved how it can be beneficial to us all, but a common misconception is that it is always objective and avoids bias, opinion, and ideologies. Based on this understanding, there has been a rise in recent years with companies utilising AI-based recruiting platforms in a bid to make the hiring process more efficient and devoid of human bias. Yet, a Financial Times article quoted an employment barrister who doubted the progressive nature of AI tools and said that there is "overwhelming evidence available that the machines are very often getting it wrong".

Ranking items by their probability of relevance has long been the goal of conventional ranking systems. While this maximizes traditional criteria of ranking performance, there is a growing understanding that it is an oversimplification in online platforms that serve not only a diverse user population, but also the producers of the items. In particular, ranking algorithms are expected to be fair in how they serve all groups of users -- not just the majority group -- and they also need to be fair in how they divide exposure among the items. These fairness considerations can partially be met by adding diversity to the rankings, as done in several recent works, but we show in this paper that user fairness, item fairness and diversity are fundamentally different concepts. In particular, we find that algorithms that consider only one of the three desiderata can fail to satisfy and even harm the other two. To overcome this shortcoming, we present the first ranking algorithm that explicitly enforces all three desiderata. The algorithm optimizes user and item fairness as a convex optimization problem which can be solved optimally. From its solution, a ranking policy can be derived via a new Birkhoff-von Neumann decomposition algorithm that optimizes diversity. Beyond the theoretical analysis, we provide a comprehensive empirical evaluation on a new benchmark dataset to show the effectiveness of the proposed ranking algorithm on controlling the three desiderata and the interplay between them.

Today, humans may outperform AI in hazardous activities (e.g., road traffic), but there will come a time when AI surpasses humans, and then the question might be whether a reasonable person could have used AI to avoid damage. However, the principle of AI legal neutrality does not mean that AI and people must be treated equally, or that AI should enjoy the same rights as humans. Therefore, the author argues that AI should be recognized as an entity that morally deserves rights and can, for example, claim tangible or intangible property rights "only" if this would exceptionally benefit people. Furthermore, he states that AI legal neutrality should not come at the expense of transparency and accountability.

Imagine that one day you're riding the train and decide to hop the turnstile to avoid paying the fare. It probably won't have a big impact on the financial well-being of your local transportation system. But now ask yourself, "What if everyone did that?" The outcome is much different -- the system would likely go bankrupt and no one would be able to ride the train anymore. Moral philosophers have long believed this type of reasoning, known as universalization, is the best way to make moral decisions. But do ordinary people spontaneously use this kind of moral judgment in their everyday lives?

POLAND - 2020/09/28: In this photo illustration an Amazon Alexa logo is seen displayed on a ... [ ] smartphone. A new report from Capgemini has revealed that 90% of organizations are aware of at least one instance where an AI system had resulted in ethical issues for their business. The report, titled "AI and the Ethical Conundrum: How organizations can build ethically robust AI systems and gain trust" has found that while digital and AI-enabled interactions with customers are on the rise as customers seek contactless or non-touch interfaces amid the COVID-19 pandemic, systems are still being designed without due concern for ethical issues. While two-thirds (68%) of consumers expect AI models to be fair and free of bias, Capgemini's findings show that only 53% of organizations have a leader who is responsible for ethics of AI systems, such as a Chief Ethics Officer, and just 46% of have the ethical implications of their AI systems independently audited. What's more, 60% of organizations have attracted legal scrutiny and 22% have faced customer backlash because of these decisions reached by AI systems. The lacking implementation of ethical AI comes in the face of increased regulatory scrutiny.