This paper presents a game theoretic framework for participation and free-riding in federated learning (FL), and determines the Nash equilibrium strategies when FL is executed over wireless links. To support spectrum sensing for NextG communications, FL is used by clients, namely spectrum sensors with limited training datasets and computation resources, to train a wireless signal classifier while preserving privacy. In FL, a client may be free-riding, i.e., it does not participate in FL model updates, if the computation and transmission cost for FL participation is high, and receives the global model (learned by other clients) without incurring a cost. However, the free-riding behavior may potentially decrease the global accuracy due to lack of contribution to global model learning. This tradeoff leads to a non-cooperative game where each client aims to individually maximize its utility as the difference between the global model accuracy and the cost of FL participation. The Nash equilibrium strategies are derived for free-riding probabilities such that no client can unilaterally increase its utility given the strategies of its opponents remain the same. The free-riding probability increases with the FL participation cost and the number of clients, and a significant optimality gap exists in Nash equilibrium with respect to the joint optimization for all clients. The optimality gap increases with the number of clients and the maximum gap is evaluated as a function of the cost. These results quantify the impact of free-riding on the resilience of FL in NextG networks and indicate operational modes for FL participation.

Problem statement: Standardisation of AI fairness rules and benchmarks is challenging because AI fairness and other ethical requirements depend on multiple factors such as context, use case, type of the AI system, and so on. In this paper, we elaborate that the AI system is prone to biases at every stage of its lifecycle, from inception to its usage, and that all stages require due attention for mitigating AI bias. We need a standardised approach to handle AI fairness at every stage. Gap analysis: While AI fairness is a hot research topic, a holistic strategy for AI fairness is generally missing. Most researchers focus only on a few facets of AI model-building. Peer review shows excessive focus on biases in the datasets, fairness metrics, and algorithmic bias. In the process, other aspects affecting AI fairness get ignored. The solution proposed: We propose a comprehensive approach in the form of a novel seven-layer model, inspired by the Open System Interconnection (OSI) model, to standardise AI fairness handling. Despite the differences in the various aspects, most AI systems have similar model-building stages. The proposed model splits the AI system lifecycle into seven abstraction layers, each corresponding to a well-defined AI model-building or usage stage. We also provide checklists for each layer and deliberate on potential sources of bias in each layer and their mitigation methodologies. This work will facilitate layer-wise standardisation of AI fairness rules and benchmarking parameters.

Content-Controllable Summarization generates summaries focused on the given controlling signals. Due to the lack of large-scale training corpora for the task, we propose a plug-and-play module RelAttn to adapt any general summarizers to the content-controllable summarization task. RelAttn first identifies the relevant content in the source documents, and then makes the model attend to the right context by directly steering the attention weight. We further apply an unsupervised online adaptive parameter searching algorithm to determine the degree of control in the zero-shot setting, while such parameters are learned in the few-shot setting. By applying the module to three backbone summarization models, experiments show that our method effectively improves all the summarizers, and outperforms the prefix-based method and a widely used plug-and-play model in both zero- and few-shot settings. Tellingly, more benefit is observed in the scenarios when more control is needed.

Evisort, the no-code contract intelligence platform beloved by legal, procurement and sales operation teams worldwide, is entering a new era with the launch of the industry's first generative artificial intelligence (AI) capabilities. Evisort's reimagined AI technology provides transparent, understandable contract recommendations to drive decision-making and contract execution. Evisort's generative AI builds upon its existing AI capabilities by creating entirely new content. The Evisort AI Labs' innovation empowers legal and contracting professionals to use Large Language Models to draft, redline and negotiate contracts automatically -- freeing up time for strategic counseling. Evisort AI Labs' technology can also suggest edits that speed re-negotiations on existing complicated contracts.

This White Paper assesses the progress of three pillars of U.S. leadership in AI innovation and trustworthy AI that carry the force of law: (i) the AI in Government Act of 2020; (ii) the Executive Order on "AI Leadership"; and (iii) the Executive Order on "AI in Government." Collectively, these Executive Orders and the AI in Government Act have been critical to defining the U.S. national strategy on AI and envisioning an ecosystem where the U.S. government leads in AI and promotes trustworthy AI. We systematically examined the implementation status of each requirement and performed a comprehensive search across 200 federal agencies to assess implementation of key requirements to identify regulatory authorities pertaining to AI and to enumerate AI use cases. While much progress has been made, our findings are sobering. America's AI innovation ecosystem is threatened by weak and inconsistent implementation of these legal requirements.

The season of holiday gift buying is upon us, and it can be hard to resist the coolest new tech gadgets. But not all items are created equal when it comes to privacy, experts say. In the US, there are few limits on what companies can do with your data, putting the onus on us to do our homework, says Hayley Tsukayama, a senior legislative activist at the digital advocacy group the Electronic Frontier Foundation. She urges people to think through the privacy implications of gifts they're giving to friends and family. "Think about what information is going to be collected," she said.

"Throughout 2021 and 2022, the world began to experiment with a massive influx of commercially available AI-assisted and AI-powered tools that can be used, whether knowingly or unknowingly, during the process of creating, researching, and innovating. Looking ahead to 2023, we will start witnessing the legal and regulatory impact of these tools." In general, the adoption of artificial intelligence (AI) and machine learning technologies has the potential to impact society in many ways. These technologies can automate tasks and make them more efficient, which can lead to job displacement and other economic impacts. They can also be used to make decisions that affect people's lives, such as in the criminal justice system or in hiring, which raises ethical concerns.

Neural models that do not rely on pre-training have excelled in the keyphrase generation task with large annotated datasets. Meanwhile, new approaches have incorporated pre-trained language models (PLMs) for their data efficiency. However, there lacks a systematic study of how the two types of approaches compare and how different design choices can affect the performance of PLM-based models. To fill in this knowledge gap and facilitate a more informed use of PLMs for keyphrase extraction and keyphrase generation, we present an in-depth empirical study. Formulating keyphrase extraction as sequence labeling and keyphrase generation as sequence-to-sequence generation, we perform extensive experiments in three domains. After showing that PLMs have competitive high-resource performance and state-of-the-art low-resource performance, we investigate important design choices including in-domain PLMs, PLMs with different pre-training objectives, using PLMs with a parameter budget, and different formulations for present keyphrases. Further results show that (1) in-domain BERT-like PLMs can be used to build strong and data-efficient keyphrase generation models; (2) with a fixed parameter budget, prioritizing model depth over width and allocating more layers in the encoder leads to better encoder-decoder models; and (3) introducing four in-domain PLMs, we achieve a competitive performance in the news domain and the state-of-the-art performance in the scientific domain.

We introduce camouflaged data poisoning attacks, a new attack vector that arises in the context of machine unlearning and other settings when model retraining may be induced. An adversary first adds a few carefully crafted points to the training dataset such that the impact on the model's predictions is minimal. The adversary subsequently triggers a request to remove a subset of the introduced points at which point the attack is unleashed and the model's predictions are negatively affected. In particular, we consider clean-label targeted attacks (in which the goal is to cause the model to misclassify a specific test point) on datasets including CIFAR-10, Imagenette, and Imagewoof. This attack is realized by constructing camouflage datapoints that mask the effect of a poisoned dataset.

Evaluation is the central means for assessing, understanding, and communicating about NLP models. In this position paper, we argue evaluation should be more than that: it is a force for driving change, carrying a sociological and political character beyond its technical dimensions. As a force, evaluation's power arises from its adoption: under our view, evaluation succeeds when it achieves the desired change in the field. Further, by framing evaluation as a force, we consider how it competes with other forces. Under our analysis, we conjecture that the current trajectory of NLP suggests evaluation's power is waning, in spite of its potential for realizing more pluralistic ambitions in the field. We conclude by discussing the legitimacy of this power, who acquires this power and how it distributes. Ultimately, we hope the research community will more aggressively harness evaluation for change.