The vast majority of techniques to train fair models require access to the protected attribute (e.g., race, gender), either at train time or in production. However, in many important applications this protected attribute is largely unavailable. In this paper, we develop methods for measuring and reducing fairness violations in a setting with limited access to protected attribute labels. Specifically, we assume access to protected attribute labels on a small subset of the dataset of interest, but only probabilistic estimates of protected attribute labels (e.g., via Bayesian Improved Surname Geocoding) for the rest of the dataset. With this setting in mind, we propose a method to estimate bounds on common fairness metrics for an existing model, as well as a method for training a model to limit fairness violations by solving a constrained non-convex optimization problem. Unlike similar existing approaches, our methods take advantage of contextual information -- specifically, the relationships between a model's predictions and the probabilistic prediction of protected attributes, given the true protected attribute, and vice versa -- to provide tighter bounds on the true disparity. We provide an empirical illustration of our methods using voting data. First, we show our measurement method can bound the true disparity up to 5.5x tighter than previous methods in these applications. Then, we demonstrate that our training technique effectively reduces disparity while incurring lesser fairness-accuracy trade-offs than other fair optimization methods with limited access to protected attributes.

Recent advancements in artificial intelligence (AI) are fundamentally reshaping computing, with large language models (LLMs) now effectively being able to generate and interpret source code and natural language instructions. These emergent capabilities have sparked urgent questions in the computing education community around how educators should adapt their pedagogy to address the challenges and to leverage the opportunities presented by this new technology. In this working group report, we undertake a comprehensive exploration of LLMs in the context of computing education and make five significant contributions. First, we provide a detailed review of the literature on LLMs in computing education and synthesise findings from 71 primary articles. Second, we report the findings of a survey of computing students and instructors from across 20 countries, capturing prevailing attitudes towards LLMs and their use in computing education contexts. Third, to understand how pedagogy is already changing, we offer insights collected from in-depth interviews with 22 computing educators from five continents who have already adapted their curricula and assessments. Fourth, we use the ACM Code of Ethics to frame a discussion of ethical issues raised by the use of large language models in computing education, and we provide concrete advice for policy makers, educators, and students. Finally, we benchmark the performance of LLMs on various computing education datasets, and highlight the extent to which the capabilities of current models are rapidly improving. Our aim is that this report will serve as a focal point for both researchers and practitioners who are exploring, adapting, using, and evaluating LLMs and LLM-based tools in computing classrooms.

The asymptotically efficient online learning problem is investigated for stochastic censored regression models, which arise from various fields of learning and statistics but up to now still lacks comprehensive theoretical studies on the efficiency of the learning algorithms. For this, we propose a two-step online algorithm, where the first step focuses on achieving algorithm convergence, and the second step is dedicated to improving the estimation performance. Under a general excitation condition on the data, we show that our algorithm is strongly consistent and asymptotically normal by employing the stochastic Lyapunov function method and limit theories for martingales. Moreover, we show that the covariances of the estimates can achieve the Cramér-Rao(C-R) bound asymptotically, indicating that the performance of the proposed algorithm is the best possible that one can expect in general. Unlike most of the existing works, our results are obtained without resorting to the traditionally used but stringent conditions such as independent and identically distributed (i.i.d) assumption on the data, and thus our results do not exclude applications to stochastic dynamical systems with feedback. A numerical example is also provided to illustrate the superiority of the proposed online algorithm over the existing related ones in the literature. Keywords: stochastic dynamical systems, censored regression models, online learning, non-i.i.d data, cramér-Rao bound.

Liberalism-oriented political philosophy reasons that all individuals should be treated equally independently of their protected characteristics. Related work in machine learning has translated the concept of \emph{equal treatment} into terms of \emph{equal outcome} and measured it as \emph{demographic parity} (also called \emph{statistical parity}). Our analysis reveals that the two concepts of equal outcome and equal treatment diverge; therefore, demographic parity does not faithfully represent the notion of \emph{equal treatment}. We propose a new formalization for equal treatment by (i) considering the influence of feature values on predictions, such as computed by Shapley values decomposing predictions across its features, (ii) defining distributions of explanations, and (iii) comparing explanation distributions between populations with different protected characteristics. We show the theoretical properties of our notion of equal treatment and devise a classifier two-sample test based on the AUC of an equal treatment inspector. We study our formalization of equal treatment on synthetic and natural data. We release \texttt{explanationspace}, an open-source Python package with methods and tutorials.

Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models. First, we taxonomize the vast space of potential scenarios (i.e. use cases) and metrics (i.e. desiderata) that are of interest for LMs. Then we select a broad subset based on coverage and feasibility, noting what's missing or underrepresented (e.g. question answering for neglected English dialects, metrics for trustworthiness). Second, we adopt a multi-metric approach: We measure 7 metrics (accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency) for each of 16 core scenarios when possible (87.5% of the time). This ensures metrics beyond accuracy don't fall to the wayside, and that trade-offs are clearly exposed. We also perform 7 targeted evaluations, based on 26 targeted scenarios, to analyze specific aspects (e.g. reasoning, disinformation). Third, we conduct a large-scale evaluation of 30 prominent language models (spanning open, limited-access, and closed models) on all 42 scenarios, 21 of which were not previously used in mainstream LM evaluation. Prior to HELM, models on average were evaluated on just 17.9% of the core HELM scenarios, with some prominent models not sharing a single scenario in common. We improve this to 96.0%: now all 30 models have been densely benchmarked on the same core scenarios and metrics under standardized conditions. Our evaluation surfaces 25 top-level findings. For full transparency, we release all raw model prompts and completions publicly for further analysis, as well as a general modular toolkit. We intend for HELM to be a living benchmark for the community, continuously updated with new scenarios, metrics, and models.

Although you'd never guess it from mainstream media, the most significant antitrust case in more than 20 years is under way in Washington. In it, the US justice department, alongside the attorneys general of eight states, is suing Google for abusively monopolising digital advertising technologies, thereby subverting competition through "serial acquisitions" and anti-competitive auction manipulation. Or, to put it more prosaically, arguing that Google – which has between 90% and 95% of the search market – has maintained its monopoly not by making a better product, but by locking down almost every avenue through which consumers might find a different search engine and making sure they only see Google wherever they look. Basically, because the US government has been asleep at the wheel for almost a quarter of a century and has finally woken up to its democratic responsibilities. The last time it stirred itself to take on an aggressive monopolist was in 2001, when it sued Microsoft for illegally tying its Internet Explorer browser to Windows as part of a (successful) campaign to destroy Netscape, maker of the first distinctive commercial web browser, which Bill Gates and co perceived as a potentially lethal competitive threat.

As human access to space expands, the influx of new actors promises to forever alter the dynamics of space. The head-to-head U.S.–Soviet rivalry that once dominated the Space Race will evolve into something more inclusive--but also messier. Aspiring space nations, such as Luxembourg, India, and China, together with new categories of nonstate actors, including large industrial players, startups, and universities, raise questions about how we should regulate space. Explosive commercialization is particularly challenging for existing space law, whose foundations were set in the 1960s and designed with national governments in mind. This rapidly changing environment is dramatized in "Little Assistance," a new Future Tense Fiction story from Stephen Harrison.

This story is part of Future Tense Fiction, a monthly series of short stories from Future Tense and Arizona State University's Center for Science and the Imagination about how technology and science will change our lives. The other homesteaders, mostly engineers and technicians, seemed to enjoy outings in the lunar rover. But for Eugene, this was a grinding chore that frayed his nerves. Suddenly, Mel's soothing feminine voice reverberated in his cochlear implant. "Would you like some affirmations?" You are a well-respected judge … You have worked hard to get here, to this special time and place …" As Mel went on, it seemed the suit hugged his chest a little less tightly. He relaxed his grip on the wheel. Why, he wondered, had he not remembered this technique without her prompting? Strange how the basic principles of cognitive psych were always slipping from his mind. Fortunately, she was there to remind him. "You are someone who wants what is best for the American lunar community and ...

Federal Trade Commission chair Lina Khan has said the agency is watching closely for signs of anticompetitive behavior. In March, the FTC opened an inquiry into cloud computing providers, asking whether AI products are dependent on the cloud provider they're built on. Regulators elsewhere are watching, too. The offices of Nvidia, which makes the computer chips and software necessary to train large language models, were raided Wednesday by French competition authorities, according to the Wall Street Journal.

Dianne Feinstein's office announced Friday morning that she has died at the age of 90, after more than 30 years representing California in the Senate. As her colleagues share memories of her, some huge, high-stakes questions are looming--namely, who will take her seat, and what will become of her spot on the powerful Judiciary Committee. Jim Newell walks us through what seems likely to happen, and what still remains unknown. Plus: The Waves reflects on the senator's legacy of fighting gun violence and conflict with her left-wing constituents. Unless Congress passes a bill to fund the government by Oct. 1, we're cruising for a government shutdown.