Research Papers

Artificial intelligence (AI), although not able to currently capture the many complexities of humans, are slowly adapting to have certain capabilities of humans, many of which can revolutionize our world. AI systems, such as ChatGPT and others utilized within various industries for specific processes, have been transforming rapidly. However, this transformation can occur in an extremely concerning way if certain measures are not taken. This article touches on some of the current issues within the artificial intelligence ethical crisis, such as the concerns of discrimination within AI and false information that is becoming readily available with AI. Within this article, plausible solutions involving regulation are discussed and how they would mitigate ethical concerns. These include the self-regulation of businesses along with government regulation, and the effects these possible solutions can both have on current AI concerns.

Image captioning models are widely used to describe recent and archived pictures with the objective of improving their accessibility and retrieval. Yet, these approaches tend to be inefficient and biased at retrieving people's names. In this work we introduce AstroCaptions, a dataset for the image captioning task. This dataset specifically contains thousands of public fig-ures that are complex to identify for a traditional model. We also propose a novel post-processing method to insert identified people's names inside the caption using explainable AI tools and the grounding capabilities of vi-sion-language models. The results obtained with this method show signifi-cant improvements of captions quality and a potential of reducing halluci-nations. Up to 93.2% of the persons detected can be inserted in the image captions leading to improvements in the BLEU, ROUGE, CIDEr and METEOR scores of each captioning model.

Recently, and under the umbrella of Responsible AI, efforts have been made to develop gender-ambiguous synthetic speech to represent with a single voice all individuals in the gender spectrum. However, research efforts have completely overlooked the speaking style despite differences found among binary and non-binary populations. In this work, we synthesise gender-ambiguous speech by combining the timbre of a male speaker with the manner of speech of a female speaker using voice morphing and pitch shifting towards the male-female boundary. Subjective evaluations indicate that the ambiguity of the morphed samples that convey the female speech style is higher than those that undergo plain pitch transformations suggesting that the speaking style can be a contributing factor in creating gender-ambiguous speech. To our knowledge, this is the first study that explicitly uses the transfer of the speaking style to create gender-ambiguous voices.

We study bias and discrimination in the context of Bumble, an online dating platform in India. Drawing on research in AI fairness and inclusion studies we analyze algorithmic bias and their propensity to reproduce bias. We conducted an experiment to identify and address the presence of bias in the matching algorithms Bumble pushes to its users in the form of profiles for potential dates in the real world. Dating apps like Bumble utilize algorithms that learn from user data to make recommendations. Even if the algorithm does not have intentions or consciousness, it is a system created and maintained by humans. We attribute moral agency of such systems to be compositely derived from algorithmic mediations, the design and utilization of these platforms. Developers, designers, and operators of dating platforms thus have a moral obligation to mitigate biases in the algorithms to create inclusive platforms that affirm diverse social identities.

Explainable Artificial Intelligence is a concept aimed at making complex algorithms transparent to users through a uniform solution. Researchers have highlighted the importance of integrating domain specific contexts to develop explanations tailored to end users. In this study, we focus on the Schufa housing scoring system in Germany and investigate how users information needs and expectations for explanations vary based on their roles. Using the speculative design approach, we asked business information students to imagine user interfaces that provide housing credit score explanations from the perspectives of both tenants and landlords. Our preliminary findings suggest that although there are general needs that apply to all users, there are also conflicting needs that depend on the practical realities of their roles and how credit scores affect them. We contribute to Human centered XAI research by proposing future research directions that examine users explanatory needs considering their roles and agencies.

In an era of rapidly expanding software usage, catering to the diverse needs of users from various backgrounds has become a critical challenge. Inclusiveness, representing a core human value, is frequently overlooked during software development, leading to user dissatisfaction. Users often engage in discourse on online platforms where they indicate their concerns. In this study, we leverage user feedback from three popular online sources Reddit, Google Play Store, and X, for 50 of the most popular apps in the world. Using a Socio-Technical Grounded Theory approach, we analyzed 22,000 posts across the three sources. We organize our empirical results in a taxonomy for inclusiveness comprising 5 major categories: Algorithmic Bias, Technology, Demography, Accessibility, and Other Human Values. To explore automated support for identifying inclusiveness-related posts, we experimented with a large language model (GPT4o-mini) and found that it is capable of identifying inclusiveness-related user feedback. We provide implications and recommendations that can help software practitioners to better identify inclusiveness issues to support a wider range of users

Several recent works have studied the societal effects of AI; these include issues such as fairness, robustness, and safety. In many of these objectives, a learner seeks to minimize its worst-case loss over a set of predefined distributions (known as uncertainty sets), with usual examples being perturbed versions of the empirical distribution. In other words, aforementioned problems can be written as min-max problems over these uncertainty sets. In this work, we provide a general framework for studying these problems, which we refer to as Responsible AI (RAI) games. We provide two classes of algorithms for solving these games: (a) game-play based algorithms, and (b) greedy stagewise estimation algorithms. The former class is motivated by online learning and game theory, whereas the latter class is motivated by the classical statistical literature on boosting, and regression. We empirically demonstrate the applicability and competitive performance of our techniques for solving several RAI problems, particularly around subpopulation shift.

Large language models (LLMs) are typically evaluated on the basis of task-based benchmarks such as MMLU. Such benchmarks do not examine responsible behaviour of LLMs in specific contexts. This is particularly true in the LGBTI+ context where social stereotypes may result in variation in LGBTI+ terminology. Therefore, domain-specific lexicons or dictionaries may be useful as a representative list of words against which the LLM's behaviour needs to be evaluated. This paper presents a methodology for evaluation of LLMs using an LGBTI+ lexicon in Indian languages. The methodology consists of four steps: formulating NLP tasks relevant to the expected behaviour, creating prompts that test LLMs, using the LLMs to obtain the output and, finally, manually evaluating the results. Our qualitative analysis shows that the three LLMs we experiment on are unable to detect underlying hateful content. Similarly, we observe limitations in using machine translation as means to evaluate natural language understanding in languages other than English. The methodology presented in this paper can be useful for LGBTI+ lexicons in other languages as well as other domain-specific lexicons. The work done in this paper opens avenues for responsible behaviour of LLMs, as demonstrated in the context of prevalent social perception of the LGBTI+ community.

Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications. It is therefore crucial to implement effective social risk management strategies at the point of care. Objective: To develop an EHR-based machine learning (ML) analytical pipeline to identify the unmet social needs associated with hospitalization risk in patients with T2D. Methods: We identified 10,192 T2D patients from the EHR data (from 2012 to 2022) from the University of Florida Health Integrated Data Repository, including contextual SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing stability). We developed an electronic health records (EHR)-based machine learning (ML) analytic pipeline, namely individualized polysocial risk score (iPsRS), to identify high social risk associated with hospitalizations in T2D patients, along with explainable AI (XAI) techniques and fairness assessment and optimization. Results: Our iPsRS achieved a C statistic of 0.72 in predicting 1-year hospitalization after fairness optimization across racial-ethnic groups. The iPsRS showed excellent utility for capturing individuals at high hospitalization risk; the actual 1-year hospitalization rate in the top 5% of iPsRS was ~13 times as high as the bottom decile. Conclusion: Our ML pipeline iPsRS can fairly and accurately screen for patients who have increased social risk leading to hospitalization in T2D patients.

Adaptive Adversarial Training Does Not Increase Recourse Costs

Ian Hardy, Jayanth Yetukuri, Yang Liu

Recent work has connected adversarial attack methods and algorithmic recourse methods: both seek minimal changes to an input instance which alter a model's classification decision. It has been shown that traditional adversarial training, which seeks to minimize a classifier's susceptibility to malicious perturbations, increases the cost of generated recourse; with larger adversarial training radii correlating with higher recourse costs. From the perspective of algorithmic recourse, however, the appropriate adversarial training radius has always been unknown. Another recent line of work has motivated adversarial training with adaptive training radii to address the issue of instance-wise variable adversarial vulnerability, showing success in domains with unknown attack radii. This work studies the effects of adaptive adversarial training on algorithmic recourse costs. We establish that the improvements in model robustness induced by adaptive adversarial training show little effect on algorithmic recourse costs, providing a potential avenue for affordable robustness in domains where recoursability is critical.

Algorithm fairness has become a central problem for the broad adoption of artificial intelligence. Although the past decade has witnessed an explosion of excellent work studying algorithm biases, achieving fairness in real-world AI production systems has remained a challenging task. Most existing works fail to excel in practical applications since either they have conflicting measurement techniques and/ or heavy assumptions, or require code-access of the production models, whereas real systems demand an easy-to-implement measurement framework and a systematic way to correct the detected sources of bias. In this paper, we leverage recent advances in causal inference and interpretable machine learning to present an algorithm-agnostic framework (MIIF) to Measure, Interpret, and Improve the Fairness of an algorithmic decision. We measure the algorithm bias using randomized experiments, which enables the simultaneous measurement of disparate treatment, disparate impact, and economic value. Furthermore, using modern interpretability techniques, we develop an explainable machine learning model which accurately interprets and distills the beliefs of a blackbox algorithm. Altogether, these techniques create a simple and powerful toolset for studying algorithm fairness, especially for understanding the cost of fairness in practical applications like e-commerce and targeted advertising, where industry A/B testing is already abundant.

Among the seven key requirements to achieve trustworthy AI proposed by the High-Level Expert Group on Artificial Intelligence (AI-HLEG) established by the European Commission (EC), the fifth requirement ("Diversity, non-discrimination and fairness") declares: "In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system's life cycle. [...] This requirement is closely linked with the principle of fairness". In this paper, we try to shed light on how closely these two distinct concepts, diversity and fairness, may be treated by focusing on information access systems and ranking literature. These concepts should not be used interchangeably because they do represent two different values, but what we argue is that they also cannot be considered totally unrelated or divergent. Having diversity does not imply fairness, but fostering diversity can effectively lead to fair outcomes, an intuition behind several methods proposed to mitigate the disparate impact of information access systems, i.e. recommender systems and search engines.