Legislation introduced last week would require companies to assess the impact of AI and automated systems they use to make decisions affecting people's employment, finances, housing and more. The Algorithmic Accountability Act of 2022, sponsored by Oregon Democratic Sen. Ron Wyden, would give the FTC more tech staff to oversee enforcement and let the agency publish information about the algorithmic tech that companies use. In fact, it follows an approach to AI accountability and transparency already promoted by key advisers inside the FTC. Algorithms used by social media companies are often the ones in the regulatory spotlight. However, all sorts of businesses -- from home loan providers and banks to job recruitment services -- use algorithmic systems to make automated decisions.

Legislation introduced last week would require companies to assess the impact of AI and automated systems they use to make decisions affecting people's employment, finances, housing and more. The Algorithmic Accountability Act of 2022, sponsored by Oregon Democratic Sen. Ron Wyden, would give the FTC more tech staff to oversee enforcement and let the agency publish information about the algorithmic tech that companies use. In fact, it follows an approach to AI accountability and transparency already promoted by key advisers inside the FTC. Algorithms used by social media companies are often the ones in the regulatory spotlight. However, all sorts of businesses -- from home loan providers and banks to job recruitment services -- use algorithmic systems to make automated decisions. In an effort to enable more oversight and control of technologies that make discriminatory decisions or create safety risks or other harms, the bill would require companies deploying automated systems to assess them, mitigate negative impacts and submit annual reports about those assessments to the FTC.

In a number of application domains, one observes a sequence of network data; for example, repeated measurements between users interactions in social media platforms, financial correlation networks over time, or across subjects, as in multi-subject studies of brain connectivity. One way to analyze such data is by stacking networks into a third-order array or tensor. We propose a principal components analysis (PCA) framework for sequence network data, based on a novel decomposition for semi-symmetric tensors. We derive efficient algorithms for computing our proposed "Coupled CP" decomposition and establish estimation consistency of our approach under an analogue of the spiked covariance model with rates the same as the matrix case up to a logarithmic term. Our framework inherits many of the strengths of classical PCA and is suitable for a wide range of unsupervised learning tasks, including identifying principal networks, isolating meaningful changepoints or outliers across observations, and for characterizing the "variability network" of the most varying edges. Finally, we demonstrate the effectiveness of our proposal on simulated data and on examples from political science and financial economics. The proof techniques used to establish our main consistency results are surprisingly straight-forward and may find use in a variety of other matrix and tensor decomposition problems.

On the same day the two gaming goliaths announced the deal, the FTC and DOJ launched a joint public inquiry with the goal of better detecting and preventing anti-competitive deals. Shortly after, Bloomberg reported the FTC had assumed responsibility for reviewing the Microsoft and Activision Blizzard deal. The FTC declined to comment or confirm an existing investigation. Stoller, though, pointed out that the stock market appears to be reacting to the looming specter of this investigation. "Activision is [trading at] $80, and the purchase price is at $95," he said.

Argumentation is a human-like reasoning mechanism contributing to the formalization of commonsense reasoning. In the last decade, several argument-based formalisms have emerged, with application in many areas, such as legal reasoning, autonomous agents and multi-agent systems; many are based on Dung's seminal work characterizing Abstract Argumentation Frameworks (AF). Recent research in the area has led to Temporal Argumentation Frameworks (TAF) that extend Dung's by considering the temporal availability of arguments. In this work we introduce a novel framework, called Extended Temporal Argumentation Framework (E-TAF), extending TAF with the capability of modeling availability of attacks among arguments, which allows for instance to model reliability of arguments varying over time. We show how E-TAF can be enriched by considering Structured Abstract Argumentation, adding compositional elements to the abstract arguments involved based on a simplified version of the recently introduced Dynamic Argumentation Frameworks.

The proliferation of personalized recommendation technologies has raised concerns about discrepancies in their recommendation performance across different genders, age groups, and racial or ethnic populations. This varying degree of performance could impact users' trust in the system and may pose legal and ethical issues in domains where fairness and equity are critical concerns, like job recommendation. In this paper, we investigate several potential factors that could be associated with discriminatory performance of a recommendation algorithm for women versus men. We specifically study several characteristics of user profiles and analyze their possible associations with disparate behavior of the system towards different genders. These characteristics include the anomaly in rating behavior, the entropy of users' profiles, and the users' profile size.

"Reverse-engineered" models of brain-like structures are viable candidates for developing increasing complexification (via generatively encoded "intelligence") that could instantiate some form of consciousness – albeit not identical to human consciousness. This essay posits how such trajectories could lead to the iterative development of "machine sentience" and addresses issues of what "machine consciousness" might mean for: 1) the ways that humans regard such machine entities as "beings" and/or "persons", and 2) philosophical, ethical and socio-legal positions which might need to be adapted to guide and govern human treatment of, and interactions with such entities. Herein, I argue that neuroethics contributes crucial insights and viable tools to any meaningful approach to this topic (in synergy with extant discourse in "robo-ethics"). As the fields of neuro- and cognitive science, and computational engineering become increasingly convergent, so too must the philosophical and ethical approaches that can – and should – be employed to direct what convergent science may create. The speed and breadth of such technological development are such that neuroethical address and engagement of these issues and questions must be equivalently paced and iterative, so as to retain preparatory value.

Online "notice and choice" is an essential concept in the US FTC's Fair Information Practice Principles. Privacy laws based on these principles include requirements for providing notice about data practices and allowing individuals to exercise control over those practices. Internet users need control over privacy, but their options are hidden in long privacy policies which are cumbersome to read and understand. In this paper, we describe several approaches to automatically extract choice instances from privacy policy documents using natural language processing and machine learning techniques. We define a choice instance as a statement in a privacy policy that indicates the user has discretion over the collection, use, sharing, or retention of their data. We describe supervised machine learning approaches for automatically extracting instances containing opt-out hyperlinks and evaluate the proposed methods using the OPP-115 Corpus, a dataset of annotated privacy policies. Extracting information about privacy choices and controls enables the development of concise and usable interfaces to help Internet users better understand the choices offered by online services. The focus of this paper, however, is to describe such methods to automatically extract useful opt-out hyperlinks from privacy policies.

Government regulations are critical to understanding how to do business with a government entity and receive other benefits. However, government regulations are also notoriously long and organized in ways that can be confusing for novice users. Developing cognitive assistance tools that remove some of the burden from human users is of potential benefit to a variety of users. The volume of data found in United States federal government regulation suggests a multiple-step approach to process the data into machine-readable text, create an automated legal knowledge base capturing various facts and rules, and eventually building a legal question and answer system to acquire understanding from various regulations and provisions. Our work discussed in this paper represents our initial efforts to build a framework for Federal Acquisition Regulations System (Title 48, Code of Federal Regulations) in order to create an efficient legal knowledge base representing relationships between various legal elements, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules.

We identify and classify users' self-narration of racial discrimination and corresponding community support in social media. We developed natural language models first to distinguish self-narration of racial discrimination in Reddit threads, and then to identify which types of support are provided and valued in subsequent replies. Our classifiers can detect the self-narration of personally experienced racism in online textual accounts with 83% accuracy and can recognize four types of supportive actions in replies with up to 88% accuracy. Descriptively, our models identify types of racism experienced and the racist concepts (e.g., sexism, appearance or accent related) most experienced by people of different races. Finally, we show that commiseration is the most valued form of social support.