Warehouse inventory management has become critical in light of pandemic-related supply chain issues. Unfortunately, it's a practice that can sometimes fall by the wayside. According to one estimate, 43% of businesses in the U.S. don't track inventory or do so using a manual system. Inventory accuracy often suffers as a result. A 2017 Peoplevox survey found that 34% of businesses have shipped an order late because they inadvertently sold a product that was not in stock.

Since 2019, experts at the University of North Carolina at Chapel Hill's University Libraries have investigated the use of machine learning to identify racist laws from North Carolina's past. Now a grant of $400,000 from The Andrew W. Mellon Foundation will allow them to extend that work to two more states. The grant will also fund research and teaching fellowships for scholars interested in using the project's outputs and techniques. On the Books: Jim Crow and Algorithms of Resistance began with a question from a North Carolina social studies teacher: Was there a comprehensive list of all the Jim Crow laws that had ever been passed in the state? Finding little beyond scholar and activist Pauli Murray's 1951 book "States' laws on race and color," a team of librarians, technologists and data experts set out to fill the gap.

Analysis For campaigners hoping that 2022 could be the year that deepfaked imagery falls within a stricter legal purview, the early indicators are unpromising. Last Thursday the European Parliament ratified amendments to the Digital Services Act (DSA, due to take effect in 2023), in regards to the dissemination of deepfakes. The modifications address deepfakes across two sections, each directly related to online advertising: amendment 1709 pertaining to Article 30, and a related amendment to article 63. 'Where a very large online platform becomes aware that a piece of content is a generated or manipulated image, audio or video content that appreciably resembles existing persons, objects, places or other entities or events and falsely appears to a person to be authentic or truthful (deep fakes), the provider shall label the content in a way that informs that the content is inauthentic and that is clearly visible for the recipient of the services.' The second adds text to the existing article 63, which is itself mainly concerned with increasing the transparency of large advertising platforms. 'In addition, very large online platforms should label any known deep fake videos, audio or other files.'

Many applications affecting human lives rely on models that have come to be known under the umbrella of machine learning and artificial intelligence. These AI models are usually complicated mathematical functions that map from an input space to an output space. Stakeholders are interested to know the rationales behind models' decisions and functional behavior. We study this functional behavior in relation to the data used to create the models. On this topic, scholars have often assumed that models do not extrapolate, i.e., they learn from their training samples and process new input by interpolation. This assumption is questionable: we show that models extrapolate frequently; the extent of extrapolation varies and can be socially consequential. We demonstrate that extrapolation happens for a substantial portion of datasets more than one would consider reasonable. How can we trust models if we do not know whether they are extrapolating? Given a model trained to recommend clinical procedures for patients, can we trust the recommendation when the model considers a patient older or younger than all the samples in the training set? If the training set is mostly Whites, to what extent can we trust its recommendations about Black and Hispanic patients? Which dimension (race, gender, or age) does extrapolation happen? Even if a model is trained on people of all races, it still may extrapolate in significant ways related to race. The leading question is, to what extent can we trust AI models when they process inputs that fall outside their training set? This paper investigates several social applications of AI, showing how models extrapolate without notice. We also look at different sub-spaces of extrapolation for specific individuals subject to AI models and report how these extrapolations can be interpreted, not mathematically, but from a humanistic point of view.

When explaining AI behavior to humans, how is the communicated information being comprehended by the human explainee, and does it match what the explanation attempted to communicate? When can we say that an explanation is explaining something? We aim to provide an answer by leveraging theory of mind literature about the folk concepts that humans use to understand behavior. We establish a framework of social attribution by the human explainee, which describes the function of explanations: the concrete information that humans comprehend from them. Specifically, effective explanations should be coherent (communicate information which generalizes to other contrast cases), complete (communicating an explicit contrast case, objective causes, and subjective causes), and interactive (surfacing and resolving contradictions to the generalization property through iterations). We demonstrate that many XAI mechanisms can be mapped to folk concepts of behavior. This allows us to uncover their modes of failure that prevent current methods from explaining effectively, and what is necessary to enable coherent explanations.

The forum r/AmITheAsshole in Reddit hosts discussion on moral issues based on concrete narratives presented by users. Existing analysis of the forum focuses on its comments, and does not make the underlying data publicly available. In this paper we build a new dataset of comments and also investigate the classification of the posts in the forum. Further, we identify textual patterns associated with the provocation of moral judgement by posts, with the expression of moral stance in comments, and with the decisions of trained classifiers of posts and comments.

Language models increasingly rely on massive web dumps for diverse text data. However, these sources are rife with undesirable content. As such, resources like Wikipedia, books, and newswire often serve as anchors for automatically selecting web text most suitable for language modeling, a process typically referred to as quality filtering. Using a new dataset of U.S. high school newspaper articles -- written by students from across the country -- we investigate whose language is preferred by the quality filter used for GPT-3. We find that newspapers from larger schools, located in wealthier, educated, and urban ZIP codes are more likely to be classified as high quality. We then demonstrate that the filter's measurement of quality is unaligned with other sensible metrics, such as factuality or literary acclaim. We argue that privileging any corpus as high quality entails a language ideology, and more care is needed to construct training corpora for language models, with better transparency and justification for the inclusion or exclusion of various texts.

Since proposed in 1965, Lotfi Zadeh's idea of "fuzzy logic" has penetrated every science and engineering field. Physix is a "fuzzy metric system" to measure people's opinion and emotional response more accurately. AI lacks a logic that that can interpret the subtleties of thought, emotion and communication. The continua provide simple, unbiased metrics of personal experience and belief for any given topic; every opinion and feeling can be seen relative to others'. A shared visualization of perspectives will defuse antagonism and reduce racism leaving a positive impact on our society.

Dupin's pinned a homicide on you." Special Agent Dinah Carter and I had worked on only a couple of cases as partners in the FBI. Now she was programming me for survival mode, to elude the bureau's AI-based crime-solving system, Dupin. "Lose your phone," Dinah told me. Dupin had named me prime suspect in a crime that had occurred just moments before and miles away because I had guessed that the AI was fabricating evidence. I didn't realize then how soon I'd be declared dead. I took the emergency stairs to street level, dropping my cell phone behind a fire hose. With my hood up and watching out for security cameras, I headed onto 10th Street Northwest. When I gave the barista cash for my half-hour online and thimbleful of espresso, he looked at me as if I was something he needed to wipe off his shoe. That left me with a dollar and some loose change. I sat down at a screen and pulled out the AI Primer that Dinah had given me. In the back were the author's details--Professor Francesca Adriaco from Georgetown University. If I had any chance of surviving this, I needed her help. I need to speak with you urgently about artificial intelligence. I guess she had a system monitoring her emails; she replied in minutes and invited me to her office. If I ever survived this, I needed to exercise more. "Hey, Saskia, good to meet you.

Shlomi Hod (shlomi@bu.edu) is a computer science Ph.D. student at Boston University, USA. Karni Chagal-Feferkorn (karni111@gmail.com) is a Postdoctoral Fellow in AI and Regulation at the Faculty of Law, Common Law Section, University of Ottawa, Canada. Niva Elkin-Koren (elkiniva@tauex.tau.ac.il) is a Professor of Law at Tel Aviv University, Faculty of Law, Israel. Avigdor Gal (avigal@ie.technion.ac.il) is the Benjamin and Florence Free Chaired Professor of Data Science at Technion--Israel Institute of Technology, Israel.