Since the inception of Generative Adversarial Networks (GANs) more than a decade ago, artists have been facing the rising impact of AI systems on their creative practice and livelihood. As a result, the imposition on creativity has led to adverse consequences for the creative community. In this work, we take a foundational approach to mitigating these impacts by proposing a rights-based framework to recognize creativity as a right. In Part 1, we argue for creativity as a human right, and in Part 2, we argue for creativity as a cultural right. We then combine these arguments to present a Co-creative Rights Enablement (CORE) Design Framework comprising rights, capabilities, and possibilities. This work advances creativity in the context of technical change and contributes a rights-based framework for developing co-creative AI systems that is applicable to co-creative system designers, policymakers, and design researchers.

Auditing autoregressive Large Language Models (LLMs) for disparities is often impeded by high token costs and limited precision.We introduce Token-Focused Disparity Probing (TFDP), a novel methodology overcoming these challenges by adapting single-token masked prediction to autoregressive architectures via targeted token querying.Disparities between minimally contrastive sentence pairs are quantified through a multi-scale semantic alignment score that integrates sentence, localcontext, and token embeddings with adaptive weighting.We propose three disparity metrics: Preference Score (PS), Prediction Set Divergence (PSD), and Weighted Final Score (WFS), for comprehensive assessment.Evaluated on our customized Proverbs Disparity Dataset (PDD) with controlled attribute toggles (e.g., gender bias, misinformation susceptibility), TFDP precisely detects disparities while achieving up to 42 times fewer output tokens than minimal n-token continuations, offering a scalable tool for responsible LLM evaluation.

Delirium is a syndrome characterized by acute and fluctuating change in attention, awareness, and cognition. Common in older adults, especially those with underlying medical conditions, delirium is associated with higher mortality rates, longer hospital stays, and increased healthcare costs. Early identification and management of delirium therefore becomes essential in order to prevent adverse outcomes, improve patient health, and reduce healthcare costs. As multiple predisposing (e.g., neurological disorders) and precipitating factors (e.g., medications) can be involved in the aetiology of delirium, detecting the syndrome early on can be challenging. In this work, we present an interpretable multimodal checklist that can aid clinicians in delirium detection. Specifically, we leverage causal decision trees to extract most relevant features for delirium detection which are then used in learning the predictive checklist. Experiments demonstrate the efficacy of the approach over existing methods in terms of both detection accuracy and interpretability.

Machine learning (ML) has become central to data-driven decision making, thereby necessitating fairness to individuals and society. Quality and quantity of labeled data play a crucial role in realizing fair ML models as inadequate and unreliable labels can result in biased outcomes. In this context, active learning methods are regarded as promising pathways for efficiently collecting labeled data. In this paper, we propose a fair active learning method that characterizes the reduction in unfairness associated with ML models by quantifying the mutual information between the label predictions and sensitive attributes using efficient Bayesian sampling methods. Extensive experiments on multiple image and text datasets demonstrate that our method yields promising fairness-accuracy tradeoff when compared to existing deep learning-based methods.

The rapid proliferation of large language models (LLMs) has brought with it both opportunities and challenges. While LLMs and more broadly generative AI technologies are capable of providing excellent improvements for various routine and autonomous tasks thereby enabling cost and performance benefit, they are also prone to personal and societal harms such as biases, stereotypes, misinformation, and hallucinations to name a few. These ethical concerns have in turn triggered stakeholders across the world to call in for regulatory measures that ensure safe and beneficial use of generative AI technologies. In parallel, there are also research efforts to alleviate these issues through the development of generative AI bias detection and mitigating strategies. Towards advancing this goal, in this paper, we propose an accessible and end-user-friendly LLM diagnostic toolkit whereby diverse stakeholders such as software engineers, business executives, and consumers can examine a suite of LLMs for uncovering a host of ethical issues including biases and misinformation embedded in LLMs. We also demonstrate that our toolkit can be used to diagnose for issues related to commonsense reasoning capabilities of LLMs. Extensive experiments on challenging tasks and datasets demonstrates the effectiveness of our diagnostic toolkit.

Creative ML tools are collaborative systems that afford artistic creativity through their myriad interactive relationships. We propose using "assemblage thinking" to support analyses of creative ML by approaching it as a system in which the elements of people, organizations, culture, practices, and technology constantly influence each other. We model these interactions as "coordinating elements" that give rise to the social and political characteristics of a particular creative ML context, and call attention to three dynamic elements of creative ML whose interactions provide unique context for the social impact a particular system has: people, creative processes, and products. As creative assemblages are highly contextual, we present these as analytical concepts that computing researchers can adapt to better understand the functioning of a particular system or phenomena and identify intervention points to foster desired change. This paper contributes to theorizing interactions with AI in the context of art, and how these interactions shape the production of algorithmic art.

In recent years, artificial intelligence (AI) generated art has become very popular. From generating art works in the style of famous artists like Paul Cezanne and Claude Monet to simulating styles of art movements like Ukiyo-e, a variety of creative applications have been explored using AI. However, there has been very little focus concerning the ethical implications of AI generated art. Can AI model artists' styles without stereotyping them? Does AI do justice to the socio-cultural nuances of art movements? In this work, we take a first step towards analyzing these issues. Leveraging directed acyclic graphs to represent potential processes of art creation, we propose a simple metric to quantify confounding bias due to the lack of modeling the influence of art movements in learning artists' styles. As a case study, we consider the popular cycleGAN model and analyze confounding bias across various genres. The proposed metric is more effective than state-of-the-art outlier detection method in understanding the influence of art movements in artworks. We also highlight how the proposed metric can aid in determining authenticity of artworks. We hope our work triggers discussions related to ethical implications of AI generated art.

With growing adoption of AI across fields such as healthcare, finance, and the justice system, explaining an AI decision has become more important than ever before. Development of human-centric explainable AI (XAI) systems necessitates an understanding of the requirements of the human-in-the-loop seeking the explanation. This includes the cognitive behavioral purpose that the explanation serves for its recipients, and the structure that the explanation uses to reach those ends. An understanding of the psychological foundations of explanations is thus vital for the development of effective human-centric XAI systems. Towards this end, we survey papers from the cognitive science literature that address the following broad questions: (1) what is an explanation, (2) what are explanations for, and 3) what are the characteristics of good and bad explanations. We organize the insights gained therein by means of highlighting the advantages and shortcomings of various explanation structures and theories, discuss their applicability across different domains, and analyze their utility to various types of humans-in-the-loop. We summarize the key takeaways for human-centric design of XAI systems, and recommend strategies to bridge the existing gap between XAI research and practical needs. We hope this work will spark the development of novel human-centric XAI systems.

Automatic face recognition is prevalent in a wide range of systems these days and it is critical to explore new techniques in order to enhance the state of the art. In this paper, we analyze the Region Covariance Matrix (RCM) and its enhancement based on Sigma sets as a feature extraction procedure for face images. The RCM features encode the covariance of various low level features, e.g., pixel intensities and gradients. Sigma sets, on the other hand, reduce the computational complexity of comparing two RCMs. Based on our experiments on the Labeled Faces in the Wild (LFW) dataset, we show that the proposed technique outperforms the popular Local Binary Patterns (LBP) technique and is on par with other better performing techniques that use complex classifiers.

We present a work that explores the feasibility of automated face recognition technologies for analyzing identities in works of portraiture, and in the process provide additional evidence to settle some long-standing questions in art history. Works of portrait art bear the mark of visual interpretation of the artist. Moreover, the number of samples available to model these effects is often limited. From a set of portraiture of the Renaissance and Baroque periods, where the identities of subjects are known, we derive appropriate features that are based on domain knowledge of artistic renderings, and learn and validate statistical models for the distribution of the match and non-match scores, which we refer to as portrait feature space (PFS). Thereafter, we use this PFS on a number of cases that have been "open questions" to art historians. They are usually in the form of validating two portraits as belonging to the same person. Using statistical hypothesis tests on the PFS, we provide quantitative measures of similarity for each of these questions. It is, to the best of our knowledge, the first study that applies automated face recognition technologies to the analysis of portraits of multiple subjects in various forms - paintings, death masks, sculptures.

Organizing large video databases is a pressing need and a challenging problem. Social attitudes in the form of users' beliefs and evaluations can benefit classification. For instance, news videos do not gather as much user attention as music videos while sports videos trigger interest mainly during the time of event. In this paper, we provide an extensive analysis of the role of usage statistics in aiding classification. Towards this, we propose a novel framework motivated by evolutionary biology to characterize growth, persistence and decline of contents in online environments. We then incorporate this information in a nearest neighbor classifier to establish categories. The effectiveness of the approach is demonstrated by comparing against results obtained using principal component analysis followed by nearest neighbor based classification.