In 1996 Nasa and the White House made the explosive announcement that the rock contained traces of Martian bugs. The meteorite, catalogued as Allen Hills (ALH) 84001, crashed onto the frozen wastes of Antarctica 13,000 years ago and was recovered in 1984. Photographs were released showing elongated segmented objects that appeared strikingly lifelike.

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.

My left arm extends all the way up to and just barely past my elbow, tapering into a small, fleshy stump. For prosthetists, I've always been a weird fit--that funny little kid in the office with my arm held out like a bird with a broken wing, waiting for the plaster mold to dry. Since I do not have a forearm, a prosthesis socket must fit over my elbow to stay on, but the socket necessarily limits the range of motion and makes it harder to prevent falling off during a full day of bending and extending. My most recent prosthetist had devised their own patented method of molding a socket that better accommodates bodies like mine. What I didn't realize was how else they have applied this knowledge, before I even became their patient.

The UK's spy agencies have reportedly given a contract to Amazon Web Services (AWS) to host classified material in a deal aimed at boosting the use of data analytics and artificial intelligence for espionage. The Financial Times reported that the UK's signals agency GCHQ had supported the procurement of a high-security cloud system, which would be used by its sister services, MI5 and MI6. Other government departments, such as the Ministry of Defence, would also use the system during joint operations, it was claimed. The agreement was signed this year with AWS, Amazon.com's Any contract with Amazon is likely to ignite concerns over sovereignty because the UK's most secret data will be hosted by a single US tech company.

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.

Since 2017, when Canada became the first country to adopt a national AI strategy, at least 60 countries have adopted some form of policy for artificial intelligence (AI). The prospect of an estimated boost of 16 percent, or US$13 trillion, to global output by 2030 has led to an unprecedented race to promote AI uptake across industry, consumer markets, and government services. Global corporate investment in AI has reportedly reached US$60 billion in 2020 and is projected to more than double by 2025. At the same time, the work on developing global standards for AI has led to significant developments in various international bodies. These encompass both technical aspects of AI (in standards development organizations (SDOs) such as the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), and the Institute of Electrical and Electronics Engineers (IEEE) among others) and the ethical and policy dimensions of responsible AI.

Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define. In this paper, we describe a method for creating cloud artifact masks which can be used to remove artifacts from satellite imagery using a combination of traditional image processing together with deep learning based on U-Net. Compared to previous methods, our approach does not require multi-channel spectral imagery but performs successfully on single-channel Digital Elevation Models (DEMs). DEMs are a representation of the topography of the Earth and have a variety applications including planetary science, geology, flood modeling, and city planning.

The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like Label Propagation (LP) or in Graph Neural Networks (GNN). Nonetheless, uncertainty estimation for non-independent node-level predictions is under-explored. In this work, we explore uncertainty quantification for node classification in three ways: (1) We derive three axioms explicitly characterizing the expected predictive uncertainty behavior in homophilic attributed graphs. (2) We propose a new model Graph Posterior Network (GPN) which explicitly performs Bayesian posterior updates for predictions on interdependent nodes. GPN provably obeys the proposed axioms. (3) We extensively evaluate GPN and a strong set of baselines on semi-supervised node classification including detection of anomalous features, and detection of left-out classes. GPN outperforms existing approaches for uncertainty estimation in the experiments.

Contracts are a common type of legal document that frequent in several day-to-day business workflows. However, there has been very limited NLP research in processing such documents, and even lesser in generating them. These contracts are made up of clauses, and the unique nature of these clauses calls for specific methods to understand and generate such documents. In this paper, we introduce the task of clause recommendation, asa first step to aid and accelerate the author-ing of contract documents. We propose a two-staged pipeline to first predict if a specific clause type is relevant to be added in a contract, and then recommend the top clauses for the given type based on the contract context. We pretrain BERT on an existing library of clauses with two additional tasks and use it for our prediction and recommendation. We experiment with classification methods and similarity-based heuristics for clause relevance prediction, and generation-based methods for clause recommendation, and evaluate the results from various methods on several clause types. We provide analyses on the results, and further outline the advantages and limitations of the various methods for this line of research.