The field of mobile and wearable computing is undergoing a revolutionary integration of machine learning. Devices can now diagnose diseases, predict heart irregularities, and unlock the full potential of human cognition. However, the underlying algorithms powering these predictions are not immune to biases with respect to sensitive attributes (e.g., gender, race), leading to discriminatory outcomes. The goal of this work is to explore the extent to which the mobile and wearable computing community has adopted ways of reporting information about datasets and models to surface and, eventually, counter biases. Our systematic review of papers published in the Proceedings of the ACM Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) journal from 2018-2022 indicates that, while there has been progress made on algorithmic fairness, there is still ample room for growth. Our findings show that only a small portion (5%) of published papers adheres to modern fairness reporting, while the overwhelming majority thereof focuses on accuracy or error metrics. To generalize these results across venues of similar scope, we analyzed recent proceedings of ACM MobiCom, MobiSys, and SenSys, IEEE Pervasive, and IEEE Transactions on Mobile Computing Computing, and found no deviation from our primary result. In light of these findings, our work provides practical guidelines for the design and development of mobile and wearable technologies that not only strive for accuracy but also fairness.

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework -- which we call ${\tt HydroGraphs}$ -- for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset). The graph representation provides an flexible intuitive approach for capturing connectivity and for identifying upstream pollutant sources and for tracing downstream impacts within small and large hydrological systems. Moreover, the graph representation can facilitate the use of advanced algorithms and tools of graph theory, topology, optimization, and machine learning to aid data analytics and decision-making. We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams. Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices and evaluate how surface waters respond to such practices.

Judge Amit Mehta will consider legal questions that are both wonky and technical. Being a monopoly is not illegal, but abusing such power to quash competition is. The Justice Department is arguing that Google strong-armed Apple and other smartphone makers into these deals. "The exclusive default was not Apple's choice," Dintzer told the court last week, citing Apple's interest in also dealing with Yahoo, before Google demanded exclusivity.

The proposed class-action lawsuit filed late on Tuesday by the Authors Guild joins several others from writers, source code owners and visual artists against generative AI providers. In addition to Microsoft-backed OpenAI, similar lawsuits are pending against Meta Platforms and Stability AI over the data used to train their AI systems. Other authors involved in the latest lawsuit include The Lincoln Lawyer writer Michael Connelly and lawyer-novelists David Baldacci and Scott Turow. An OpenAI spokesperson said on Wednesday that the company respects authors' rights and is "having productive conversations with many creators around the world, including the Authors Guild". The suit was organised by the Authors Guild and also includes David Baldacci, Sylvia Day, Jonathan Franzen and Elin Hilderbrand, among others.

A report published this week by the U.K.'s Competition & Markets Authority (CMA) has raised concerns about the potential ways the artificial intelligence industry could become monopolized or harm consumers in future, but stressed that it is too soon to tell whether these scenarios would materialize. The issues raised by the report highlight the difficulties policymakers face in governing AI, a source of both huge potential commercial value and many risks. Rishi Sunak, the British Prime Minister, is pushing for the U.K. to occupy a central role in international AI policy discussions, with a particular focus on risks from advanced AI systems. If the U.K. competition watchdog decides to start taking action against AI developers, tech companies around the world could be affected. The report, published on Monday, focuses on foundation models, which the CMA defines as "a type of AI technology that are trained on vast amounts of data that can be adapted to a wide range of tasks and operations." Examples include text-generating AI models, such as GPT-3.5, the model that powers OpenAI's ChatGPT, as well as image-generating AI models, such as Stable Diffusion.

Synthetic data is seen as the most promising solution to share individual-level data while preserving privacy. Shadow modeling-based Membership Inference Attacks (MIAs) have become the standard approach to evaluate the privacy risk of synthetic data. While very effective, they require a large number of datasets to be created and models trained to evaluate the risk posed by a single record. The privacy risk of a dataset is thus currently evaluated by running MIAs on a handful of records selected using ad-hoc methods. We here propose what is, to the best of our knowledge, the first principled vulnerable record identification technique for synthetic data publishing, leveraging the distance to a record's closest neighbors. We show our method to strongly outperform previous ad-hoc methods across datasets and generators. We also show evidence of our method to be robust to the choice of MIA and to specific choice of parameters. Finally, we show it to accurately identify vulnerable records when synthetic data generators are made differentially private. The choice of vulnerable records is as important as more accurate MIAs when evaluating the privacy of synthetic data releases, including from a legal perspective. We here propose a simple yet highly effective method to do so. We hope our method will enable practitioners to better estimate the risk posed by synthetic data publishing and researchers to fairly compare ever improving MIAs on synthetic data.

Synthetic data is emerging as one of the most promising solutions to share individual-level data while safeguarding privacy. While membership inference attacks (MIAs), based on shadow modeling, have become the standard to evaluate the privacy of synthetic data, they currently assume the attacker to have access to an auxiliary dataset sampled from a similar distribution as the training dataset. This is often seen as a very strong assumption in practice, especially as the proposed main use cases for synthetic tabular data (e.g. medical data, financial transactions) are very specific and don't have any reference datasets directly available. We here show how this assumption can be removed, allowing for MIAs to be performed using only the synthetic data. Specifically, we developed three different scenarios: (S1) Black-box access to the generator, (S2) only access to the released synthetic dataset and (S3) a theoretical setup as upper bound for the attack performance using only synthetic data. Our results show that MIAs are still successful, across two real-world datasets and two synthetic data generators. These results show how the strong hypothesis made when auditing synthetic data releases - access to an auxiliary dataset - can be relaxed, making the attacks more realistic in practice.

The proliferation of data in recent years has led to the advancement and utilization of various statistical and deep learning techniques, thus expediting research and development activities. However, not all industries have benefited equally from the surge in data availability, partly due to legal restrictions on data usage and privacy regulations, such as in medicine. To address this issue, various statistical disclosure and privacy-preserving methods have been proposed, including the use of synthetic data generation. Synthetic data are generated based on some existing data, with the aim of replicating them as closely as possible and acting as a proxy for real sensitive data. This paper presents a systematic review of methods for generating and evaluating synthetic longitudinal patient data, a prevalent data type in medicine. The review adheres to the PRISMA guidelines and covers literature from five databases until the end of 2022. The paper describes 17 methods, ranging from traditional simulation techniques to modern deep learning methods. The collected information includes, but is not limited to, method type, source code availability, and approaches used to assess resemblance, utility, and privacy.

Benchmarking, standards and certification are closely related processes. Standards can provide normative requirements that robotics and AI systems may or may not conform to. Certification generally relies upon conformance with one or more standards as the key determinant of granting a certificate to operate. And benchmarks are sets of standardised tests against which robots and AI systems can be measured. Benchmarks therefore can be thought of as informal standards. In this paper we will develop these themes with examples from benchmarking, standards and certification, and argue that these three linked processes are not only useful but vital to the broader practice of Responsible Innovation.

The emergence of large language models (LLMs) presents an unprecedented opportunity to automate construction contract management, reducing human errors and saving significant time and costs. However, LLMs may produce convincing yet inaccurate and misleading content due to a lack of domain expertise. To address this issue, expert-driven contract knowledge can be represented in a structured manner to constrain the automatic contract management process. This paper introduces the Nested Contract Knowledge Graph (NCKG), a knowledge representation approach that captures the complexity of contract knowledge using a nested structure. It includes a nested knowledge representation framework, a NCKG ontology built on the framework, and an implementation method. Furthermore, we present the LLM-assisted contract review pipeline enhanced with external knowledge in NCKG. Our pipeline achieves a promising performance in contract risk reviewing, shedding light on the combination of LLM and KG towards more reliable and interpretable contract management.