We paraphrase Descartes' famous dictum in the area of AI ethics where the "I doubt and therefore I am" is suggested as a necessary aspect of morality. Therefore AI, which cannot doubt itself, cannot possess moral agency. Of course, this is not the end of the story. We explore various aspects of the human mind that substantially differ from AI, which includes the sensory grounding of our knowing, the act of understanding, and the significance of being able to doubt ourselves. The foundation of our argument is the discipline of ethics, one of the oldest and largest knowledge projects of human history, yet, we seem only to be beginning to get a grasp of it. After a couple of thousand years of studying the ethics of humans, we (humans) arrived at a point where moral psychology suggests that our moral decisions are intuitive, and all the models from ethics become relevant only when we explain ourselves. This recognition has a major impact on what and how we can do regarding AI ethics. We do not offer a solution, we explore some ideas and leave the problem open, but we hope somewhat better understood than before our study.

We present a new methodology for utilising machine learning technology in symbolic computation research. We explain how a well known human-designed heuristic to make the choice of variable ordering in cylindrical algebraic decomposition may be represented as a constrained neural network. This allows us to then use machine learning methods to further optimise the heuristic, leading to new networks of similar size, representing new heuristics of similar complexity as the original human-designed one. We present this as a form of ante-hoc explainability for use in computer algebra development.

In recent years there has been increased use of machine learning (ML) techniques within mathematics, including symbolic computation where it may be applied safely to optimise or select algorithms. This paper explores whether using explainable AI (XAI) techniques on such ML models can offer new insight for symbolic computation, inspiring new implementations within computer algebra systems that do not directly call upon AI tools. We present a case study on the use of ML to select the variable ordering for cylindrical algebraic decomposition. It has already been demonstrated that ML can make the choice well, but here we show how the SHAP tool for explainability can be used to inform new heuristics of a size and complexity similar to those human-designed heuristics currently commonly used in symbolic computation.

This paper discusses and evaluates ideas of data balancing and data augmentation in the context of mathematical objects: an important topic for both the symbolic computation and satisfiability checking communities, when they are making use of machine learning techniques to optimise their tools. We consider a dataset of non-linear polynomial problems and the problem of selecting a variable ordering for cylindrical algebraic decomposition to tackle these with. By swapping the variable names in already labelled problems, we generate new problem instances that do not require any further labelling when viewing the selection as a classification problem. We find this augmentation increases the accuracy of ML models by 63% on average. We study what part of this improvement is due to the balancing of the dataset and what is achieved thanks to further increasing the size of the dataset, concluding that both have a very significant effect. We finish the paper by reflecting on how this idea could be applied in other uses of machine learning in mathematics.

Lewis Wynne-Jones is the Vice President of Product at ThinkData Works. In 2012, two things happened that set the tone for the next decade of data investments. One was technological, the other was professional, and they both revolutionized the way we think about data. Together, these events directly led to the emergence of artificial intelligence as a business prerogative. Today, however, AI is fraught with problems, and fewer businesses, not more, are saying they're data-driven.

"There is no shortage of commentary on what artificial intelligence will do to human jobs. It's easy to find a multiplicity of predictions, prescriptions, or denunciations," says Thomas H. Davenport, one of the co-authors of the book. "It is not so easy, however, to find descriptions of how people work day-to-day with smart machines." Davenport joined a Conversation That Matters about our emerging and ever-expanding relationship with a technology that scares a wide range of people including, Elon Musk and Bill Gates.

One of the earliest predictions about artificial intelligence was that robots would steal jobs from people, particularly for seemingly monotonous and menial tasks. Case in point: Flippy 2, the "autonomous robotic kitchen assistant" developed by Miso Robotics to turn hamburgers as they cook. The restaurant chain CaliBurger is testing Flippy at various locations. But what the robot is best at doing isn't what it was designed to do. "[A CaliBurger franchise owner] said, 'Flippy isn't actually as good as I might have liked at flipping hamburgers, but it's pretty good at pulling baskets of French fries out when they're done,'" said Thomas Davenport, a digital fellow at the MIT Initiative on the Digital Economy.

The algorithms employed by our communities are often underspecified, and thus have multiple implementation choices, which do not effect the correctness of the output, but do impact the efficiency or even tractability of its production. In this extended abstract, to accompany a keynote talk at the 2021 SC-Square Workshop, we survey recent work (both the author's and from the literature) on the use of Machine Learning technology to improve algorithms of interest to SC-Square.

Inspiration for the ads came from an unlikely source: artificial intelligence. Kayak worked with New York advertising agency Supernatural Development LLC, whose internal AI platform combines marketers' answers to questions about their business with consumer data drawn from social media and market research to suggest campaign strategies, then automatically generates ideas for advertising copy and other marketing materials. Supernatural's AI found that Kayak should target its campaign largely toward young, upper-income men, who it said would respond to humor about Americans' inability to agree on basic facts in politics and pop culture, said Michael Barrett, co-founder and chief strategy officer at Supernatural. CMO Today delivers the most important news of the day for media and marketing professionals. "That gave us a good amount of license to zig where the category was zagging and to be more relevant, more provocative," Mr. Clarke said of the AI findings.

We've reached a point "where AI and machine learning are converging" and it's helping drive operational efficiencies and customer experience (CX) innovation for a wide range of businesses. That's how author and veteran IT expert Tom Davenport began a wide-ranging discussion about the state of AI and automation in the financial services sector at a recent virtual salon with senior industry executives. Another speaker, Rob Krugman, Chief Digital Officer at financial services firm Broadridge, said his firm is "increasingly using AI models to define and select the attributes and information that is important to their customers". The session was hosted by Bill Wright, Head of AI Machine Learning and Edge Innovation for Red Hat. It was moderated by CDX.