Vasyl Bilous's last name means "white mustache." His actual mustache is dark brown with a hint of gray. He's worn one since high school. In a picture that he took on the first day of Russia's full-scale invasion of Ukraine, Vasyl has a chevron mustache, a neat barbershop cut--close on the sides, paintbrush-thick on top. At the time, he was an assistant professor of forensics at the National Law University, in Kharkiv, and a lawyer in private practice.

Gleb Kuznetsov has more than 15 years experience leading product, UI and UX design across web, mobile, and TV ecosystems. AI-generated art is everywhere on the web. If you are an active Instagram, Twitter, or Pinterest user, you likely saw interesting artworks created using text-based tools like DALLE, Midjourney, or Stable Diffusion. The magic of these tools is that to generate images, all you need to do is to provide a string of text that describes what the image is all about. Many AI-generated works look stunning, but it's only the beginning.

In recent years, there has been a flurry of research focusing on the fairness of machine learning models, and in particular on quantifying and eliminating bias against protected subgroups. One line of work generalizes the notion of protected subgroups beyond simple discrete classes by introducing the notion of a "rich subgroup", and seeks to train models that are calibrated or equalize error rates with respect to these richer subgroup classes. Largely orthogonally, local model explanation methods have been developed that given a classifier h and test point x, attribute influence for the prediction h(x) to the individual features of x. This raises a natural question: Do local model explanation methods attribute different feature importance values on average across different protected subgroups, and can we detect these disparities efficiently? If the model places high weight on a given feature in a specific protected subgroup, but not on the dataset overall (or vice versa), this could be a potential indicator of bias in the predictive model or the underlying data generating process, and is at the very least a useful diagnostic that signals the need for a domain expert to delve deeper. In this paper, we formally introduce the notion of feature importance disparity (FID) in the context of rich subgroups, design oracle-efficent algorithms to identify large FID subgroups, and conduct a thorough empirical analysis that establishes auditing for FID as an important method to investigate dataset bias. Our experiments show that across 4 datasets and 4 common feature importance methods our algorithms find (feature, subgroup) pairs that simultaneously: (i) have subgroup feature importance that is often an order of magnitude different than the importance on the dataset as a whole (ii) generalize out of sample, and (iii) yield interesting discussions about potential bias inherent in these datasets.

Survival analysis is the branch of statistics that studies the relation between the characteristics of living entities and their respective survival times, taking into account the partial information held by censored cases. A good analysis can, for example, determine whether one medical treatment for a group of patients is better than another. With the rise of machine learning, survival analysis can be modeled as learning a function that maps studied patients to their survival times. To succeed with that, there are three crucial issues to be tackled. First, some patient data is censored: we do not know the true survival times for all patients. Second, data is scarce, which led past research to treat different illness types as domains in a multi-task setup. Third, there is the need for adaptation to new or extremely rare illness types, where little or no labels are available. In contrast to previous multi-task setups, we want to investigate how to efficiently adapt to a new survival target domain from multiple survival source domains. For this, we introduce a new survival metric and the corresponding discrepancy measure between survival distributions. These allow us to define domain adaptation for survival analysis while incorporating censored data, which would otherwise have to be dropped. Our experiments on two cancer data sets reveal a superb performance on target domains, a better treatment recommendation, and a weight matrix with a plausible explanation.

Sharing data can often enable compelling applications and analytics. However, more often than not, valuable datasets contain information of sensitive nature, and thus sharing them can endanger the privacy of users and organizations. A possible alternative gaining momentum in the research community is to share synthetic data instead. The idea is to release artificially generated datasets that resemble the actual data -- more precisely, having similar statistical properties. So how do you generate synthetic data? What is that useful for? What are the benefits and the risks? What are the open research questions that remain unanswered? In this article, we provide a gentle introduction to synthetic data and discuss its use cases, the privacy challenges that are still unaddressed, and its inherent limitations as an effective privacy-enhancing technology.

In the context of rapid discoveries by leaders in AI, governments must consider how to design regulation that matches the increasing pace of new AI capabilities. Regulatory Markets for AI is a proposal designed with adaptability in mind. It involves governments setting outcome-based targets for AI companies to achieve, which they can show by purchasing services from a market of private regulators. We use an evolutionary game theory model to explore the role governments can play in building a Regulatory Market for AI systems that deters reckless behaviour. We warn that it is alarmingly easy to stumble on incentives which would prevent Regulatory Markets from achieving this goal. These 'Bounty Incentives' only reward private regulators for catching unsafe behaviour. We argue that AI companies will likely learn to tailor their behaviour to how much effort regulators invest, discouraging regulators from innovating. Instead, we recommend that governments always reward regulators, except when they find that those regulators failed to detect unsafe behaviour that they should have. These 'Vigilant Incentives' could encourage private regulators to find innovative ways to evaluate cutting-edge AI systems.

Time series data introduces two key challenges for explainability methods: firstly, observations of the same feature over subsequent time steps are not independent, and secondly, the same feature can have varying importance to model predictions over time. In this paper, we propose Windowed Feature Importance in Time (WinIT), a feature removal based explainability approach to address these issues. Unlike existing feature removal explanation methods, WinIT explicitly accounts for the temporal dependence between different observations of the same feature in the construction of its importance score. We conduct an extensive empirical study on synthetic and real-world data, compare against a wide range of leading explainability methods, and explore the impact of various evaluation strategies. Our results show that WinIT achieves significant gains over existing methods, with more consistent performance across different evaluation metrics. Reliably explaining predictions of machine learning models is important given their wide-spread use. Explanations provide transparency and aid reliable decision making, especially in domains such as finance and healthcare, where explainability is often an ethical and legal requirement (Amann et al., 2020; Prenio & Yong, 2021). Multivariate time series data is ubiquitous in these sensitive domains, however explaining time series models has been relatively under explored. In this work we focus on saliency methods, a common approach to explainability that provides explanations by highlighting the importance of input features to model predictions (Baehrens et al., 2010; Mohseni et al., 2020). It has been shown that standard saliency methods underperform on deep learning models used in the time series domain (Ismail et al., 2020). In time series data, observations of the same feature at different points in time are typically related and their order matters.

Across industries, businesses are expanding their use of artificial intelligence (AI) systems. AI isn't just for the tech giants like Meta and Google anymore; logistics firms leverage AI to streamline operations, advertisers use AI to target specific markets and even your online bank uses AI to power its automated customer service experience. For these companies, dealing with ethical risks and operational challenges related to AI is inevitable – but how should they prepare to face them? Poorly executed AI products can violate individual privacy and in the extreme, even weaken our social and political systems. In the U.S., an algorithm used to predict likelihood of future crime was revealed to be biased against Black Americans, reinforcing racial discriminatory practices in the criminal justice system.

Representative Ted Lieu, Democrat of California, wrote in a guest essay in The New York Times in January that he was "freaked out" by the ability of the ChatGPT chatbot to mimic human writers. Another Democrat, Representative Jake Auchincloss of Massachusetts, gave a one-minute speech -- written by a chatbot -- calling for regulation of A.I. But even as lawmakers put a spotlight on the technology, few are taking action on it. No bill has been proposed to protect individuals or thwart the development of A.I.'s potentially dangerous aspects. And legislation introduced in recent years to curb A.I. applications like facial recognition have withered in Congress.

One technology that is poised to play a critical role in addressing these challenges is artificial intelligence (AI). AI is an advanced technology that has the potential to revolutionize the way we approach global problems by providing powerful tools for analysis, prediction, and decision-making. One of the most pressing challenges facing the world today is climate change. Rising temperatures, extreme weather events, and natural disasters threaten the stability of ecosystems and the well-being of millions of people worldwide. AI has the potential to help address this challenge by providing tools for monitoring and predicting weather patterns, assessing the impact of climate change on ecosystems, and developing innovative solutions for reducing greenhouse gas emissions.