The development of artificial intelligence (AI) that can act autonomously has raised important questions about the nature of machine decision-making and its potential capabilities. Is it possible to implement an ethical dimension to autonomous machines, i.e., is ethics "computable"? Are autonomous machines capable of factoring moral considerations into their decisions? Can AI be programmed to know the difference between "right" and "wrong?" Once the topic of science fiction novels, these questions are leading to discussions about the actual creation of moral machines, also known as artificial moral agents, and have largely contributed to the expansion of the field of machine ethics.

With novel artificial intelligence (AI) applications multiplying like rabbits these days, it may seem like the current wave of AI innovation is all beer and skittles. Lawsuits have a way of sobering up any metaphorical party and, in the wake of numerous high-profile racial bias and fairness cases, The Wall Street Journal reports that companies including Google, Twitter and Salesforce say they "plan to bulk up ethics teams responsible for evaluating the behavior of algorithms." In today's litigious environment, AI-powered business decisions must be more than explainable, ethical and responsible; we need Auditable AI. As the mainstream business world moves from the theoretical use of AI to production-scale decisioning, Auditable AI is essential because it encompasses more than the tenets of Responsible AI (AI that is robust, explainable, ethical and efficient). It's important to note that although the word "audit" has an after-the-fact connotation, Auditable AI emphasizes laying down (and using) a clearly prescribed record of work while the model is being built and before the model is put into production.

Art theft has become a major problem in the world of Non-Fungible Tokens (NFTs) as grifters look to make a quick buck from the works of others. The nature of the online goods means it's very difficult to confirm who owns the NFTs being sold and if the sellers have the legal right to sell that work on any platform. Progress on a solution has been slow, but it does appear new tactics from hosting companies like DeviantArt are working. DeviantArt recently implemented a new system designed to help identify stolen artwork in the wild by using machine learning to locate works that may have been stolen. It's even able to detect subtle variations in stolen artwork, including if an image is cropped, flipped or slightly altered to avoid traditional image detection systems.

Large language models (LM) generate remarkably fluent text and can be efficiently adapted across NLP tasks. Measuring and guaranteeing the quality of generated text in terms of safety is imperative for deploying LMs in the real world; to this end, prior work often relies on automatic evaluation of LM toxicity. We critically discuss this approach, evaluate several toxicity mitigation strategies with respect to both automatic and human evaluation, and analyze consequences of toxicity mitigation in terms of model bias and LM quality. We demonstrate that while basic intervention strategies can effectively optimize previously established automatic metrics on the RealToxicityPrompts dataset, this comes at the cost of reduced LM coverage for both texts about, and dialects of, marginalized groups. Additionally, we find that human raters often disagree with high automatic toxicity scores after strong toxicity reduction interventions -- highlighting further the nuances involved in careful evaluation of LM toxicity.

We propose an Ontology-Based Information Extraction (OBIE) system to automate the extraction of the criteria and values applied in Land Use Suitability Analysis (LUSA) from bylaw and regulation documents related to the geographic area of interest. The results obtained by our proposed LUSA OBIE system (land use suitability criteria and their values) are presented as an ontology populated with instances of the extracted criteria and property values. This latter output ontology is incorporated into a Multi-Criteria Decision Making (MCDM) model applied for constructing suitability maps for different kinds of land uses. The resulting maps may be the final desired product or can be incorporated into the cellular automata urban modeling and simulation for predicting future urban growth. A case study has been conducted where the output from LUSA OBIE is applied to help produce a suitability map for the City of Regina, Saskatchewan, to assist in the identification of suitable areas for residential development. A set of Saskatchewan bylaw and regulation documents were downloaded and input to the LUSA OBIE system. We accessed the extracted information using both the populated LUSA ontology and the set of annotated documents. In this regard, the LUSA OBIE system was effective in producing a final suitability map.

The lawsuit is one of several against Activision Blizzard, which was previously investigated by California's Department of Fair Employment and Housing (DFEH) over claims of widespread sexual harassment and gender-based discrimination. The DFEH sued Activision Blizzard in July, alleging the company had a "frat boy culture" that included gender-based discrimination and harassment. It was followed by a class action suit from shareholders in August that claimed the company had violated federal securities laws.

When developing new technologies, we must ensure that they operate fairly. At a time when identity is increasingly being used as the key to digital access, any technology based on identity must function fairly and equally for everyone, regardless of race, age, gender, or other characteristics leading to human physical diversity. While digital services have proliferated across many industries, this issue is particularly relevant in the financial sector, as Covid-19 accelerates a shift towards automated platforms delivered remotely by banks and other providers – with biases in AI having stark implications for unfairly rewarding certain groups over others. How does AI bias creep into machine learning models? Algorithmic decision making relies on machine learning techniques that recognise patterns from historical data.

Event extraction (EE) aims to identify structured events, including event triggers and their corresponding arguments, from unstructured text. Most of the existing works rely on a large number of labeled instances to train models, while the labeled data could be expensive to be obtained. In this work, we present a data-efficient event extraction method by formulating event extraction as a natural language generation problem. The formulation allows us to inject knowledge of label semantics, event structure, and output dependencies into the model. Given a passage and an event type, our model learns to summarize this passage into a templated sentence in a predefined structure. The template is event-type-specific, manually created, and contains event trigger and argument information. Lastly, a rule-based algorithm is used to derive the trigger and argument predictions from the generated sentence. Our method inherently enjoys the following benefits: (1) The pretraining of the generative language models help incorporate the semantics of the labels for generative EE. (2) The autoregressive generation process and our end-to-end design for extracting triggers and arguments force the model to capture the dependencies among the output triggers and their arguments. (3) The predefined templates form concrete yet flexible rules to hint the models about the valid patterns for each event type, reducing the models' burden to learn structures from the data. Empirical results show that our model achieves superior performance over strong baselines on EE tasks in the low data regime and achieves competitive results to the current state-of-the-art when more data becomes available.

Given the recent proliferation of false claims online, there has been a lot of manual fact-checking effort. As this is very time-consuming, human fact-checkers can benefit from tools that can support them and make them more efficient. Here, we focus on building a system that could provide such support. Given an input document, it aims to detect all sentences that contain a claim that can be verified by some previously fact-checked claims (from a given database). The output is a re-ranked list of the document sentences, so that those that can be verified are ranked as high as possible, together with corresponding evidence. Unlike previous work, which has looked into claim retrieval, here we take a document-level perspective. We create a new manually annotated dataset for the task, and we propose suitable evaluation measures. We further experiment with a learning-to-rank approach, achieving sizable performance gains over several strong baselines. Our analysis demonstrates the importance of modeling text similarity and stance, while also taking into account the veracity of the retrieved previously fact-checked claims. We believe that this research would be of interest to fact-checkers, journalists, media, and regulatory authorities.

A key part of the NLP ethics movement is responsible use of data, but exactly what that means or how it can be best achieved remain unclear. This position paper discusses the core legal and ethical principles for collection and sharing of textual data, and the tensions between them. We propose a potential checklist for responsible data (re-)use that could both standardise the peer review of conference submissions, as well as enable a more in-depth view of published research across the community. Our proposal aims to contribute to the development of a consistent standard for data (re-)use, embraced across NLP conferences.