Abstract Like electoral systems, decision-making methods are also vulnerable to manipulation by decision-makers. The ability to effectively defend against such threats can only come from thoroughly understanding the manipulation mechanisms. In the presented article, we show two algorithms that can be used to launch a manipulation attack. They allow for equating the weights of two selected alternatives in the pairwise comparison method and, consequently, choosing a leader. The theoretical considerations are accompanied by a Monte Carlo simulation showing the relationship between the size of the PC matrix, the degree of inconsistency, and the ease of manipulation. This work is a continuation of our previous research published in the paper (Szybowski et al., 2023)

Despite its technological breakthroughs, eXplainable Artificial Intelligence (XAI) research has limited success in producing the {\em effective explanations} needed by users. In order to improve XAI systems' usability, practical interpretability, and efficacy for real users, the emerging area of {\em Explainable Interfaces} (EIs) focuses on the user interface and user experience design aspects of XAI. This paper presents a systematic survey of 53 publications to identify current trends in human-XAI interaction and promising directions for EI design and development. This is among the first systematic survey of EI research.

Nowadays an ever-growing concerning phenomenon, the emergence of algorithmic biases that can lead to unfair models, emerges. Several debiasing approaches have been proposed in the realm of deep learning, employing more or less sophisticated approaches to discourage these models from massively employing these biases. However, a question emerges: is this extra complexity really necessary? Is a vanilla-trained model already embodying some ``unbiased sub-networks'' that can be used in isolation and propose a solution without relying on the algorithmic biases? In this work, we show that such a sub-network typically exists, and can be extracted from a vanilla-trained model without requiring additional training. We further validate that such specific architecture is incapable of learning a specific bias, suggesting that there are possible architectural countermeasures to the problem of biases in deep neural networks.

Deep learning-based image generation has seen significant advancements with diffusion models, notably improving the quality of generated images. Despite these developments, generating images with unseen characteristics beneficial for downstream tasks has received limited attention. To bridge this gap, we propose Style-Extracting Diffusion Models, featuring two conditioning mechanisms. Specifically, we utilize 1) a style conditioning mechanism which allows to inject style information of previously unseen images during image generation and 2) a content conditioning which can be targeted to a downstream task, e.g., layout for segmentation. We introduce a trainable style encoder to extract style information from images, and an aggregation block that merges style information from multiple style inputs. This architecture enables the generation of images with unseen styles in a zero-shot manner, by leveraging styles from unseen images, resulting in more diverse generations. In this work, we use the image layout as target condition and first show the capability of our method on a natural image dataset as a proof-of-concept. We further demonstrate its versatility in histopathology, where we combine prior knowledge about tissue composition and unannotated data to create diverse synthetic images with known layouts. This allows us to generate additional synthetic data to train a segmentation network in a semi-supervised fashion. We verify the added value of the generated images by showing improved segmentation results and lower performance variability between patients when synthetic images are included during segmentation training. Our code will be made publicly available at [LINK].

Social media platforms are online fora where users engage in discussions, share content, and build connections. This review explores the dynamics of social interactions, user-generated contents, and biases within the context of social media analysis (analyzing works that use the tools offered by complex network analysis and natural language processing) through the lens of three key points of view: online debates, online support, and human-AI interactions. On the one hand, we delineate the phenomenon of online debates, where polarization, misinformation, and echo chamber formation often proliferate, driven by algorithmic biases and extreme mechanisms of homophily. On the other hand, we explore the emergence of online support groups through users' self-disclosure and social support mechanisms. Online debates and support mechanisms present a duality of both perils and possibilities within social media; perils of segregated communities and polarized debates, and possibilities of empathy narratives and self-help groups. This dichotomy also extends to a third perspective: users' reliance on AI-generated content, such as the ones produced by Large Language Models, which can manifest both human biases hidden in training sets and non-human biases that emerge from their artificial neural architectures. Analyzing interdisciplinary approaches, we aim to deepen the understanding of the complex interplay between social interactions, user-generated content, and biases within the realm of social media ecosystems.

A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is more severe in mobile manipulation, where collecting demonstrations is harder than in stationary manipulation due to the lack of available and easy-to-use teleoperation interfaces. In this work, we demonstrate TeleMoMa, a general and modular interface for whole-body teleoperation of mobile manipulators. TeleMoMa unifies multiple human interfaces including RGB and depth cameras, virtual reality controllers, keyboard, joysticks, etc., and any combination thereof. In its more accessible version, TeleMoMa works using simply vision (e.g., an RGB-D camera), lowering the entry bar for humans to provide mobile manipulation demonstrations. We demonstrate the versatility of TeleMoMa by teleoperating several existing mobile manipulators - PAL Tiago++, Toyota HSR, and Fetch - in simulation and the real world. We demonstrate the quality of the demonstrations collected with TeleMoMa by training imitation learning policies for mobile manipulation tasks involving synchronized whole-body motion. Finally, we also show that TeleMoMa's teleoperation channel enables teleoperation on site, looking at the robot, or remote, sending commands and observations through a computer network, and perform user studies to evaluate how easy it is for novice users to learn to collect demonstrations with different combinations of human interfaces enabled by our system. We hope TeleMoMa becomes a helpful tool for the community enabling researchers to collect whole-body mobile manipulation demonstrations. For more information and video results, https://robin-lab.cs.utexas.edu/telemoma-web.

After a relaxing weekend away, Guillermo Ibarrola was walking out of a train station in Argentina's capital when police arrested him and accused him of a robbery committed hundreds of miles away in a place he had never visited. "It was a nightmare," Ibarrola told local media after the 2019 incident, which rights campaigners say highlights the risks of using facial recognition systems to survey populations. The system of 300 cameras linked to a national crime database -- dubbed Buenos Aires' Big Brother -- was suspended two years ago after a court found it may have been used to collect data on journalists, politicians and human rights activists, and ruled it unconstitutional.

We propose a novel approach for automatic chaptering of TV newscast videos, addressing the challenge of structuring and organizing large collections of unsegmented broadcast content. Our method integrates both audio and visual cues through a two-stage process involving frozen neural networks and a trained LSTM network. The first stage extracts essential features from separate modalities, while the LSTM effectively fuses these features to generate accurate segment boundaries. Our proposed model has been evaluated on a diverse dataset comprising over 500 TV newscast videos of an average of 41 minutes gathered from TF1, a French TV channel, with varying lengths and topics. Experimental results demonstrate that this innovative fusion strategy achieves state of the art performance, yielding a high precision rate of 82% at IoU of 90%. Consequently, this approach significantly enhances analysis, indexing and storage capabilities for TV newscast archives, paving the way towards efficient management and utilization of vast audiovisual resources.

We develop Polaris, the first safety-focused LLM constellation for real-time patient-AI healthcare conversations. Unlike prior LLM works in healthcare focusing on tasks like question answering, our work specifically focuses on long multi-turn voice conversations. Our one-trillion parameter constellation system is composed of several multibillion parameter LLMs as co-operative agents: a stateful primary agent that focuses on driving an engaging conversation and several specialist support agents focused on healthcare tasks performed by nurses to increase safety and reduce hallucinations. We develop a sophisticated training protocol for iterative co-training of the agents that optimize for diverse objectives. We train our models on proprietary data, clinical care plans, healthcare regulatory documents, medical manuals, and other medical reasoning documents. We align our models to speak like medical professionals, using organic healthcare conversations and simulated ones between patient actors and experienced nurses. This allows our system to express unique capabilities such as rapport building, trust building, empathy and bedside manner. Finally, we present the first comprehensive clinician evaluation of an LLM system for healthcare. We recruited over 1100 U.S. licensed nurses and over 130 U.S. licensed physicians to perform end-to-end conversational evaluations of our system by posing as patients and rating the system on several measures. We demonstrate Polaris performs on par with human nurses on aggregate across dimensions such as medical safety, clinical readiness, conversational quality, and bedside manner. Additionally, we conduct a challenging task-based evaluation of the individual specialist support agents, where we demonstrate our LLM agents significantly outperform a much larger general-purpose LLM (GPT-4) as well as from its own medium-size class (LLaMA-2 70B).

Previous work in phonologically and phonetically grounded language generation has mainly focused on domains such as puns and poetry. In this article, we present new work on the generation of tongue-twisters - a form of language that is required to be conditioned on a phoneme level to maximize sound overlap, whilst maintaining semantic consistency with an input topic and still being grammatically correct. We present TwisterLister, a pipeline for generating phonologically informed tongue-twisters from Large Language Models (LLMs) that we use to generate TwistList 2.0, the largest annotated dataset of tongue-twisters to date, consisting of 17K+ examples from a combination of human and LLM authors. Our generation pipeline involves the use of a phonologically constrained vocabulary alongside LLM prompting to generate novel, non-derivative tongue-twister examples. We additionally present the results of automatic and human evaluation of smaller models trained on our generated dataset to demonstrate the extent to which phonologically motivated language types can be generated without explicit injection of phonological knowledge. Additionally, we introduce a Phoneme-Aware Constrained Decoding module (PACD) that can be integrated into any causal language model and demonstrate that this method generates good quality tongue-twisters both with and without fine-tuning the underlying language model. We also design and implement a range of automatic metrics for the task of tongue-twister generation that is phonologically motivated and captures the unique essence of tongue-twisters based on Phonemic Edit Distance (PED).