Shlomi Hod (shlomi@bu.edu) is a computer science Ph.D. student at Boston University, USA. Karni Chagal-Feferkorn (karni111@gmail.com) is a Postdoctoral Fellow in AI and Regulation at the Faculty of Law, Common Law Section, University of Ottawa, Canada. Niva Elkin-Koren (elkiniva@tauex.tau.ac.il) is a Professor of Law at Tel Aviv University, Faculty of Law, Israel. Avigdor Gal (avigal@ie.technion.ac.il) is the Benjamin and Florence Free Chaired Professor of Data Science at Technion--Israel Institute of Technology, Israel.

If we want to broaden participation, we must educate our students based on the early 17th-century origins of the word "computer," a human who performs calculations.1 Computers were exclusively human until the early 19th century when English polymath and inventor Charles Babbage introduced the Difference Engine, the first mechanical computer. The term "human computer" was then used to differentiate a person who computes from a mechanical computer. Human computers were often women who undertook long and tedious calculations to power some of the most significant advances in science, industry, and space technology in the 20th century.

Thanks to large datasets and machine learning, computers have become surprisingly adept at finding statistical relationships among many variables--and exploiting these patterns to make useful predictions. Whether the task involves recognizing objects in photographs or translating text from one language to another, much of what today's intelligent machines can accomplish stems from the computers' ability to make predictions based on statistical associations, or correlations. By and large, computers are very good at this kind of prediction. Yet for many tasks, that is not enough. "In reality, we often want to not only predict things, but we want to improve things," says Jonas Peters, a professor of statistics at the University of Copenhagen.

You hear the terms all the time. But even as a tech savvy consumer who might understand what those terms mean, does that mean you actually understand how a computer thinks for itself? Or how to craft an algorithm? Or what decision trees do? If you've got the world's greatest app idea, maybe it's time to learn enough about computer science to actually make your idea real.

In 2021, the UK has found itself under the bright lights of the world stage many times. A global audience has watched our pandemic response, the fruition of BREXIT and, most recently, the UK's COP26 presidency. So, why has the UK's AI and wider tech scene still not made it close to global superpower status that we see from China, Russia and the US? The results of the government's recent National AI Strategy are yet to be seen, but I predict there is deeper change needed. A thriving AI industry needs a combination of education, ambition, and nurtured innovation.

While technologies like artificial intelligence (AI) and machine learning (ML) have become part of many aspects of our daily lives in recent times, they are increasingly being used for the social good as well. Take the case of the Foundation to Educate Girls Globally (FEGG), a not-for-profit organisation that aims to find solutions to enrollment and learning gaps in primary education. The foundation identifies girls who are out of school, enrols them and makes sure that they continue going to school.

As a beginner in the data science industry, you must have read countless articles describing the importance of creating data science projects. In fact, I landed my first data science role due to the projects I showcased on my portfolio. However, not every data science project can land you a role in the industry. I have reviewed resumes of data science applicants in the past, most of whom were rejected from entry-level positions without even making it to the interview phase. Some of these candidates did include projects on their resume -- but the projects they showcased were too simple.

AI applications have made their way into almost every sector, yet; businesses fail to adopt them. According to Forbes, 83 percent of companies think AI is a strategic priority for them, yet there is a shortage of skilled data scientists. This is not only because AI solutions and expertise are expensive but also because firms lack the infrastructure to support these solutions. Companies are increasingly deploying AI and machine learning models using no-code AI, a no-code development platform with a visual, code-free, and typically drag-and-drop interface. Non-technical people may quickly classify, evaluate, and develop accurate models to make predictions with no coding AI.

HCI researchers' and practitioners' awareness of intersectionality has been expanding, producing knowledge, recommendations, and prototypes for supporting intersectional populations. However, doing intersectional HCI work is uniquely expensive: it leads to a combinatorial explosion of empirical work (expense 1), and little of the work on one intersectional population can be leveraged to serve another (expense 2). In this paper, we explain how representations employed by certain analytical design methods correspond to type abstractions, and use that correspondence to identify a (de)compositional model in which a population's diverse identity properties can be joined and split. We formally prove the model's correctness, and show how it enables HCI designers to harness existing analytical HCI methods for use on new intersectional populations of interest. We illustrate through four design use-cases, how the model can reduce the amount of expense 1 and enable designers to leverage prior work to new intersectional populations, addressing expense 2.

We study the problem of learning the preferences of drivers and planners in the context of last mile delivery. Given a data set containing historical decisions and delivery locations, the goal is to capture the implicit preferences of the decision-makers. We consider two ways to use the historical data: one is through a probability estimation method that learns transition probabilities between stops (or zones). This is a fast and accurate method, recently studied in a VRP setting. Furthermore, we explore the use of machine learning to infer how to best balance multiple objectives such as distance, probability and penalties. Specifically, we cast the learning problem as a structured output prediction problem, where training is done by repeatedly calling the TSP solver. Another important aspect we consider is that for last-mile delivery, every address is a potential client and hence the data is very sparse. Hence, we propose a two-stage approach that first learns preferences at the zone level in order to compute a zone routing; after which a penalty-based TSP computes the stop routing. Results show that the zone transition probability estimation performs well, and that the structured output prediction learning can improve the results further. We hence showcase a successful combination of both probability estimation and machine learning, all the while using standard TSP solvers, both during learning and to compute the final solution; this means the methodology is applicable to other, real-life, TSP variants, or proprietary solvers.