Teenagers deserve to grow, develop, and experiment, says Caitriona Fitzgerald, deputy director at the Electronic Privacy Information Center (EPIC), a nonprofit advocacy group. They should be able to test or abandon ideas "while being free from the chilling effects of being watched or having information from their youth used against them later when they apply to college or apply for a job." She called for the Federal Trade Commission (FTC) to make rules to protect the digital privacy of teens. Hye Jung Han, the author of a Human Rights Watch report about education companies selling personal information to data brokers, wants a ban on personal data-fueled advertising to children. "Commercial interests and surveillance should never override a child's best interests or their fundamental rights, because children are priceless, not products," she said.

Algorithmic disgorgement might sound like a phrase from a science-fiction horror film. In fact, it's a new tool for regulators to address the consequences of autonomous systems, ordering companies to remove or destroy algorithms and models in their products based on data obtained unfairly or deceptively. This is one of topics and papers to be presented and discussed at We Robot, an annual conference where scholars and technologists discuss legal and policy questions relating to robots and artificial intelligence. We Robot is taking place next week, from Sept. 14-16, at the University of Washington in Seattle, with a virtual option, as well. It's also an example of how the legal and regulatory landscape for robots, AI, and autonomous systems have changed in the decade since the conference was first held at the University of Miami in 2012. "We've come very far," said Ryan Calo, one of the organizers of the conference, a University of Washington law professor who specializes in areas including privacy, artificial intelligence and robots.

AI can now generate breathtaking original images based on simple text prompts. Depicted here: "a ... [ ] cute corgi lives in a house made out of sushi." A powerful new form of artificial intelligence has burst onto the scene and captured the public's imagination in recent months: text-to-image AI. Text-to-image AI models generate original images based solely on simple written inputs. Users can input any text prompt they like--say, "a cute corgi lives in a house made out of sushi"--and, as if by magic, the AI will produce a corresponding image. These models produce images that have never existed in the world nor in anyone's imagination.

In-brief New rules drafted by the European Union aimed at regulating AI could prevent developers from releasing open-source models, according to American think tank Brookings. The proposed EU AI Act, yet to be signed into law, states that open source developers have to ensure their AI software is accurate, secure, and be transparent about risk and data use in clear technical documentation. Brookings argues that if a private company were to deploy the public model or use it in a product, and it somehow gets in trouble due to some unforeseen or uncontrollable effects from the model, the company would then probably try to blame the open source developers and sue them. It might force the open source community to think twice about releasing their code, and would, unfortunately, mean the development of AI will be driven by private companies. Proprietary code is difficult to analyse and build upon, meaning innovation will be hampered.

LegalRuleML is a comprehensive XML-based representation framework for modeling and exchanging normative rules. The TPTP input and output formats, on the other hand, are general-purpose standards for the interaction with automated reasoning systems. In this paper we provide a bridge between the two communities by (i) defining a logic-pluralistic normative reasoning language based on the TPTP format, (ii) providing a translation scheme between relevant fragments of LegalRuleML and this language, and (iii) proposing a flexible architecture for automated normative reasoning based on this translation. We exemplarily instantiate and demonstrate the approach with three different normative logics.

Twelve years ago, the European Union began with the gradual phase-out of energy-inefficient incandescent light bulbs under the Ecodesign Directive. In this work, we implement an agent-based simulation to model the consumer behaviour in the EU lighting market with the goal to explain consumer behaviour and explore alternative policies. Agents are based on the Consumat II model, have individual preferences based on empirical market research, gather experience from past actions, and socially interact with each other in a dynamic environment. Our findings suggest that the adoption of energy-friendly lighting alternatives was hindered by a low level of consumer interest combined with high-enough levels of satisfaction about incandescent bulbs and that information campaigns can partially address this. These findings offer insight into both individual-level driving forces of behaviour and society-level outcomes in a niche market. With this, our work demonstrates the strengths of agent-based models for policy generation and evaluation.

Existing efforts to formulate computational definitions of fairness have largely focused on distributional notions of equality, where equality is defined by the resources or decisions given to individuals in the system. Yet existing discrimination and injustice is often the result of unequal social relations, rather than an unequal distribution of resources. Here, we show how optimizing for existing computational and economic definitions of fairness and equality fail to prevent unequal social relations. To do this, we provide an example of a self-confirming equilibrium in a simple hiring market that is relationally unequal but satisfies existing distributional notions of fairness. In doing so, we introduce a notion of blatant relational unfairness for complete-information games, and discuss how this definition helps initiate a new approach to incorporating relational equality into computational systems.

Model explainability has become an important problem in machine learning (ML) due to the increased effect that algorithmic predictions have on humans. Explanations can help users understand not only why ML models make certain predictions, but also how these predictions can be changed. In this thesis, we examine the explainability of ML models from three vantage points: algorithms, users, and pedagogy, and contribute several novel solutions to the explainability problem.

Even in highly-developed countries, as many as 15-30\% of the population can only understand texts written using a basic vocabulary. Their understanding of everyday texts is limited, which prevents them from taking an active role in society and making informed decisions regarding healthcare, legal representation, or democratic choice. Lexical simplification is a natural language processing task that aims to make text understandable to everyone by replacing complex vocabulary and expressions with simpler ones, while preserving the original meaning. It has attracted considerable attention in the last 20 years, and fully automatic lexical simplification systems have been proposed for various languages. The main obstacle for the progress of the field is the absence of high-quality datasets for building and evaluating lexical simplification systems. We present a new benchmark dataset for lexical simplification in English, Spanish, and (Brazilian) Portuguese, and provide details about data selection and annotation procedures. This is the first dataset that offers a direct comparison of lexical simplification systems for three languages. To showcase the usability of the dataset, we adapt two state-of-the-art lexical simplification systems with differing architectures (neural vs.\ non-neural) to all three languages (English, Spanish, and Brazilian Portuguese) and evaluate their performances on our new dataset. For a fairer comparison, we use several evaluation measures which capture varied aspects of the systems' efficacy, and discuss their strengths and weaknesses. We find a state-of-the-art neural lexical simplification system outperforms a state-of-the-art non-neural lexical simplification system in all three languages. More importantly, we find that the state-of-the-art neural lexical simplification systems perform significantly better for English than for Spanish and Portuguese.

Recent AI algorithms are blackbox models whose decisions are difficult to interpret. eXplainable AI (XAI) seeks to address lack of AI interpretability and trust by explaining to customers their AI decision, e.g., decision to reject a loan application. The common wisdom is that regulating AI by mandating fully transparent XAI leads to greater social welfare. This paper challenges this notion through a game theoretic model for a policy-maker who maximizes social welfare, firms in a duopoly competition that maximize profits, and heterogenous consumers. The results show that XAI regulation may be redundant. In fact, mandating fully transparent XAI may make firms and customers worse off. This reveals a trade-off between maximizing welfare and receiving explainable AI outputs. We also discuss managerial implications for policy-maker and firms.