MINNEAPOLIS, October 18, 2021 – Setting the global standards for e-discovery, the Electronic Discovery Reference Model (EDRM) is pleased to announce the release of its artificial intelligence (AI) paper titled "The Use of Artificial Intelligence in eDiscovery." Kelly Atherton, senior manager cyber incident response at Norton Rose Fulbright, served as the project trustee. Our drafting team comprised of attorneys, data scientists and legal technologists sought to create an objective, easy to digest overview for the bench and bar to aid them in better understanding the use of AI in e-discovery. "We are inundated in the e-discovery space with broad talk of technologies that will help us perform our work more efficiently and accurately at a lower cost. But what does this all even mean?" asks Atherton. "Our drafting team comprised of attorneys, data scientists and legal technologists sought to create an objective, easy to digest overview for the bench and bar to aid them in better understanding the use of AI in e-discovery. We adopted a broad, working definition of AI for the purpose of this paper and discussed the types of AI used in e-discovery, common uses cases and ethical considerations. Our hope is those new to AI can use this paper as a starting point to become a more informed consumer and adopter of AI in e-discovery."

Dynamic Linear Models (DLMs) are commonly employed for time series analysis due to their versatile structure, simple recursive updating, and probabilistic forecasting. However, the options for count time series are limited: Gaussian DLMs require continuous data, while Poisson-based alternatives often lack sufficient modeling flexibility. We introduce a novel methodology for count time series by warping a Gaussian DLM. The warping function has two components: a transformation operator that provides distributional flexibility and a rounding operator that ensures the correct support for the discrete data-generating process. Importantly, we develop conjugate inference for the warped DLM, which enables analytic and recursive updates for the state space filtering and smoothing distributions. We leverage these results to produce customized and efficient computing strategies for inference and forecasting, including Monte Carlo simulation for offline analysis and an optimal particle filter for online inference. This framework unifies and extends a variety of discrete time series models and is valid for natural counts, rounded values, and multivariate observations. Simulation studies illustrate the excellent forecasting capabilities of the warped DLM. The proposed approach is applied to a multivariate time series of daily overdose counts and demonstrates both modeling and computational successes.

Individuals often make different decisions when faced with the same context, due to personal preferences and background. For instance, judges may vary in their leniency towards certain drug-related offenses, and doctors may vary in their preference for how to start treatment for certain types of patients. With these examples in mind, we present an algorithm for identifying types of contexts (e.g., types of cases or patients) with high inter-decision-maker disagreement. We formalize this as a causal inference problem, seeking a region where the assignment of decision-maker has a large causal effect on the decision. Our algorithm finds such a region by maximizing an empirical objective, and we give a generalization bound for its performance. In a semi-synthetic experiment, we show that our algorithm recovers the correct region of heterogeneity accurately compared to baselines. Finally, we apply our algorithm to real-world healthcare datasets, recovering variation that aligns with existing clinical knowledge.

Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other. Recently, new Graph Neural Networks (GNNs) have been developed that move beyond the homophily regime; however, their evaluation has often been conducted on small graphs with limited application domains. We collect and introduce diverse non-homophilous datasets from a variety of application areas that have up to 384x more nodes and 1398x more edges than prior datasets. We further show that existing scalable graph learning and graph minibatching techniques lead to performance degradation on these non-homophilous datasets, thus highlighting the need for further work on scalable non-homophilous methods. To address these concerns, we introduce LINKX -- a strong simple method that admits straightforward minibatch training and inference. Extensive experimental results with representative simple methods and GNNs across our proposed datasets show that LINKX achieves state-of-the-art performance for learning on non-homophilous graphs. Our codes and data are available at https://github.com/CUAI/Non-Homophily-Large-Scale.

A. Lawfulness: AI applications will be developed and used in accordance with national and international law, including international humanitarian law and human rights law, as applicable. B. Responsibility and Accountability: AI applications will be developed and used with appropriate levels of judgment and care; clear human responsibility shall apply in order to ensure accountability. C. Explainability and Traceability: AI applications will be appropriately understandable and transparent, including through the use of review methodologies, sources, and procedures. This includes verification, assessment and validation mechanisms at either a NATO and/or national level. D. Reliability: AI applications will have explicit, well-defined use cases.

As the use of machine learning has increased in areas such as criminal justice, hiring, health care delivery and social service interventions, concerns have grown over whether such applications introduce new or amplify existing inequities, especially among racial minorities and people with economic disadvantages. To guard against this bias, adjustments are made to the data, labels, model training, scoring systems and other aspects of the machine learning system. The underlying theoretical assumption is that these adjustments make the system less accurate. A CMU team aims to dispel that assumption in a new study, recently published in Nature Machine Intelligence. Rayid Ghani, a professor in the School of Computer Science's Machine Learning Department (MLD) and the Heinz College of Information Systems and Public Policy; Kit Rodolfa, a research scientist in MLD; and Hemank Lamba, a post-doctoral researcher in SCS, tested that assumption in real-world applications and found the trade-off was negligible in practice across a range of policy domains.

We've all been in situations where we had to make tough ethical decisions. Why not dodge that pesky responsibility by outsourcing the choice to a machine learning algorithm? That's the idea behind Ask Delphi, a machine-learning model from the Allen Institute for AI. You type in a situation (like "donating to charity") or a question ("is it okay to cheat on my spouse?"),

In late February 2020, the European Commission published a white paper on artificial intelligence (AI)a and an accompanying report on the safety and liability implications of AI, the Internet of Things (IoT), and robotics.b In the white paper, the Commission highlighted the "European Approach" to AI, stressing "it is vital that European AI is grounded in our values and fundamental rights such as human dignity and privacy protection." In April 2021, the proposal of a Regulation entitled "Artificial Intelligence Act" was presented.2 This Regulation shall govern the use of "high-risk" AI applications which will include most medical AI applications. Referring to the above-mentioned statement, this Viewpoint aims to show European fundamental rights already provide important legal (and not merely ethical) guidelines for the development and application of medical AI.7 As medical AI can affect a person's physical and mental integrity in a very intense way and any malfunction could have serious consequences, it is a particularly relevant field of AI in terms of fundamental rights.

Public concern about computer ethics and worry about the social impacts of computing has fomented the "techlash." Newspaper headlines describe company data scandals and breaches; the ways that communication platforms promote social division and radicalization; government surveillance using systems developed by private industry; machine learning algorithms that reify entrenched racism, sexism, cisnormativity, ablism, and homophobia; and mounting concerns about the environmental impact of computing resources. How can we change the field of computing so that ethics is as central a concern as growth, efficiency, and innovation? There is no one intervention to change an entire field: instead, broad change will take a combination of guidelines, governance, and advocacy. None is easy and each raises complex questions, but each approach represents a tool for building an ethical culture of computing.

Text and data mining (TDM) uses statistical analysis tools to extract new knowledge from large quantities of text or data for purposes by finding patterns, discovering relationships, and analyzing semantics. It is used in a wide variety of fields from biomedical research to digital humanities. Japan has enacted laws to allow TDM research copying. These lawsuits grew out of the Google Book Search Project (GBS). GBS is a corpus of millions of digital books to improve its search technologies that Google developed after making a deal with the University of Michigan in 2004 to scan all eight million books in its library's collections. In return, Michigan got back from Google digital copies of the books it scanned.