Chief technology officers should follow the do no harm mantra of the Hippocratic Oath when incorporating artificial intelligence software into company platforms. While an overarching goal of introducing AI is to increase efficiencies or remove biases, there are often unexpected consequences when good ideas unintentionally cause harm. For example, use of facial recognition technology to identify criminal suspects can sometimes result in the arrest (or worse) of an innocent person. Or the development of a weaponized drone for the military that falls into the wrong hands can stray far from the developer's original intention. Here is how technology companies and those who use the technology might look beyond the intended uses of AI to identify potential unforeseen consequences.

Quantitative investment (``quant'') is an interdisciplinary field combining financial engineering, computer science, mathematics, statistics, etc. Quant has become one of the mainstream investment methodologies over the past decades, and has experienced three generations: Quant 1.0, trading by mathematical modeling to discover mis-priced assets in markets; Quant 2.0, shifting quant research pipeline from small ``strategy workshops'' to large ``alpha factories''; Quant 3.0, applying deep learning techniques to discover complex nonlinear pricing rules. Despite its advantage in prediction, deep learning relies on extremely large data volume and labor-intensive tuning of ``black-box'' neural network models. To address these limitations, in this paper, we introduce Quant 4.0 and provide an engineering perspective for next-generation quant. Quant 4.0 has three key differentiating components. First, automated AI changes quant pipeline from traditional hand-craft modeling to the state-of-the-art automated modeling, practicing the philosophy of ``algorithm produces algorithm, model builds model, and eventually AI creates AI''. Second, explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black-boxes, and explains complicated and hidden risk exposures. Third, knowledge-driven AI is a supplement to data-driven AI such as deep learning and it incorporates prior knowledge into modeling to improve investment decision, in particular for quantitative value investing. Moreover, we discuss how to build a system that practices the Quant 4.0 concept. Finally, we propose ten challenging research problems for quant technology, and discuss potential solutions, research directions, and future trends.

Informed consent has become increasingly salient for data privacy and its regulation. Entities from governments to for-profit companies have addressed concerns about data privacy with policies that enumerate the conditions for personal data storage and transfer. However, increased enumeration of and transparency in data privacy policies has not improved end-users' comprehension of how their data might be used: not only are privacy policies written in legal language that users may struggle to understand, but elements of these policies may compose in such a way that the consequences of the policy are not immediately apparent. We present a framework that uses Answer Set Programming (ASP) -- a type of logic programming -- to formalize privacy policies. Privacy policies thus become constraints on a narrative planning space, allowing end-users to forward-simulate possible consequences of the policy in terms of actors having roles and taking actions in a domain. We demonstrate through the example of the Health Insurance Portability and Accountability Act (HIPAA) how to use the system in various ways, including asking questions about possibilities and identifying which clauses of the law are broken by a given sequence of events.

The study of biases, such as gender or racial biases, is an important topic in the social and behavioural sciences. However, the literature does not always clearly define the concept. Definitions of bias are often ambiguous or not provided at all. To study biases in a precise manner, it is important to have a well-defined concept of bias. We propose to define bias as a direct causal effect that is unjustified. We propose to define the closely related concept of disparity as a direct or indirect causal effect that includes a bias. Our proposed definitions can be used to study biases and disparities in a more rigorous and systematic way. We compare our definitions of bias and disparity with various criteria of fairness introduced in the artificial intelligence literature. We also illustrate our definitions in two case studies, focusing on gender bias in science and racial bias in police shootings. Our proposed definitions aim to contribute to a better appreciation of the causal intricacies of studies of biases and disparities. We hope that this will also promote an improved understanding of the policy implications of such studies.

This paper presents an automatic approach to creating taxonomies of technical terms based on the Cooperative Patent Classification (CPC). The resulting taxonomy contains about 170k nodes in 9 separate technological branches and is freely available. We also show that a Text-to-Text Transfer Transformer (T5) model can be fine-tuned to generate hypernyms and hyponyms with relatively high precision, confirming the manually assessed quality of the resource. The T5 model opens the taxonomy to any new technological terms for which a hypernym can be generated, thus making the resource updateable with new terms, an essential feature for the constantly evolving field of technological terminology.

What if simple face paint could fool some of the best facial recognition tools available at the Qatar World Cup? A team at creative agency Virtue Worldwide sought to answer that question with a project called Camoflags: AI-generated, Juggalo-inspired face paint designs that could be used to evade facial recognition cameras. The experiment is uniquely suited to the World Cup, as face paint is a common feature at soccer matches for fans showing support for their teams. The Qatar World Cup, which ends on December 18, has been criticized for its approach to security, with concerns that the event could become a hotbed for espionage and that visitors could be monitored on their phones through app surveillance. The event has also been criticized for human rights abuses: The death of American sports journalist Grant Wahl, who died on the way to the hospital after collapsing at the World Cup stadium, resulted in renewed attention on the rights of LGBTQ individuals in Qatar.

Aires Investment Holdings (the "Company" or "Aires"), a groundbreaking technology company based in Singapore, announced today that the United States Patent and Trademark Office has granted US Patent No. 11,522,674 to Aires Investment Holdings. The patent, titled "Encryption, Decryption, And Key Generation Apparatus And Method Involving Diophantine Equation And Artificial Intelligence" illustrates the company's multi-year research into artificial intelligence and cybersecurity. US Patent No. 11,522,674 is among the the first in the world to utilize the concept of artificial intelligence paired with undecidable encryption. Artificial intelligence is designed to improve the encryption with data over periods of time while undecidable encryption are based on undecidable problems that no algorithm can solve. The patent grant is the first for Aires, with upcoming patent applications pending.

"AI systems (will) take decisions that have ethical grounds and consequences." On March 23, 2016, Microsoft released its AI-based chatbot Tay via Twitter. The bot was trained to generate its responses based on interactions with users. But there was a catch. Various users started posting offensive tweets toward the bot, resulting in Tay making replies in the same language.

Is AI delivering as promised? Last week, I was at the AI Summit in New York (as co-chair and presenter), and I am happy to report that everyone is now comfortable and excited about artificial intelligence. Okay, sorry, that's a skewed sample of people who naturally would be comfortable and excited about AI -- data scientists, AI developers, AI vendors, and the like. For mainstream business leaders and professionals, comfort with and acceptance of AI is a tad bit more muddled. Maybe there are fewer misgivings as AI develops and proves its worthiness, but people are still nervous about it.

Nearly 200 years ago, Victorian mathematician Ada Lovelace wrote what is commonly seen today as a computer program as the beginning of computer science. At the time, I wondered where the limits of analytical machines (precursors to computers) were. Already at that time Lovelace stated something that in the following years was the norm for people dealing with the mechanisms of analysis. Computers do what we ask them to do and nothing moreThey do not have the will to act. They can follow the analysis and share with us the findings from the input we give them.