pwc-archive/papers-with-abstracts huggingface dataset:
The Ethics of AI in Education
Authors: Kaska Porayska-Pomsta, Wayne Holmes, Selena Nemorin
Date: 2024-03-22 00:00:00
Tasks: Ethics

The transition of Artificial Intelligence (AI) from a lab-based science to live human contexts brings into sharp focus many historic, socio-cultural biases, inequalities, and moral dilemmas. Many questions that have been raised regarding the broader ethics of AI are also relevant for AI in Education (AIED). AIED raises further specific challenges related to the impact of its technologies on users, how such technologies might be used to reinforce or alter the way that we learn and teach, and what we, as a society and individuals, value as outcomes of education. This chapter discusses key ethical dimensions of AI and contextualises them within AIED design and engineering practices to draw connections between the AIED systems we build, the questions about human learning and development we ask, the ethics of the pedagogies we use, and the considerations of values that we promote in and through AIED within a wider socio-technical system.
Explanation Hacking: The perils of algorithmic recourse
Authors: Emily Sullivan, Atoosa Kasirzadeh
Date: 2024-03-22 00:00:00

We argue that the trend toward providing users with feasible and actionable explanations of AI decisions, known as recourse explanations, comes with ethical downsides. Specifically, we argue that recourse explanations face several conceptual pitfalls and can lead to problematic explanation hacking, which undermines their ethical status. As an alternative, we advocate that explanations of AI decisions should aim at understanding.
Prompting the E-Brushes: Users as Authors in Generative AI
Authors: Yiyang Mei
Date: 2024-03-25 00:00:00
Tasks: Misconceptions

Since its introduction in 2022, Generative AI has significantly impacted the art world, from winning state art fairs to creating complex videos from simple prompts. Amid this renaissance, a pivotal issue emerges: should users of Generative AI be recognized as authors eligible for copyright protection? The Copyright Office, in its March 2023 Guidance, argues against this notion. By comparing the prompts to clients' instructions for commissioned art, the Office denies users authorship due to their limited role in the creative process. This Article challenges this viewpoint and advocates for the recognition of Generative AI users who incorporate these tools into their creative endeavors. It argues that the current policy fails to consider the intricate and dynamic interaction between Generative AI users and the models, where users actively influence the output through a process of adjustment, refinement, selection, and arrangement. Rather than dismissing the contributions generated by AI, this Article suggests a simplified and streamlined registration process that acknowledges the role of AI in creation. This approach not only aligns with the constitutional goal of promoting the progress of science and useful arts but also encourages public engagement in the creative process, which contributes to the pool of training data for AI. Moreover, it advocates for a flexible framework that evolves alongside technological advancements while ensuring safety and public interest. In conclusion, by examining text-to-image generators and addressing misconceptions about Generative AI and user interaction, this Article calls for a regulatory framework that adapts to technological developments and safeguards public interests
Closed-loop Teaching via Demonstrations to Improve Policy Transparency
Authors: Michael S. Lee, Reid Simmons, Henny Admoni
Date: 2024-04-01 00:00:00

Demonstrations are a powerful way of increasing the transparency of AI policies. Though informative demonstrations may be selected a priori through the machine teaching paradigm, student learning may deviate from the preselected curriculum in situ. This paper thus explores augmenting a curriculum with a closed-loop teaching framework inspired by principles from the education literature, such as the zone of proximal development and the testing effect. We utilize tests accordingly to close to the loop and maintain a novel particle filter model of human beliefs throughout the learning process, allowing us to provide demonstrations that are targeted to the human's current understanding in real time. A user study finds that our proposed closed-loop teaching framework reduces the regret in human test responses by 43% over a baseline.
Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation
Authors: Declan Grabb, Max Lamparth, Nina Vasan
Date: 2024-04-02 00:00:00
Tasks: Ethics, Navigate

Amidst the growing interest in developing task-autonomous AI for automated mental health care, this paper addresses the ethical and practical challenges associated with the issue and proposes a structured framework that delineates levels of autonomy, outlines ethical requirements, and defines beneficial default behaviors for AI agents in the context of mental health support. We also evaluate fourteen state-of-the-art language models (ten off-the-shelf, four fine-tuned) using 16 mental health-related questionnaires designed to reflect various mental health conditions, such as psychosis, mania, depression, suicidal thoughts, and homicidal tendencies. The questionnaire design and response evaluations were conducted by mental health clinicians (M.D.s). We find that existing language models are insufficient to match the standard provided by human professionals who can navigate nuances and appreciate context. This is due to a range of issues, including overly cautious or sycophantic responses and the absence of necessary safeguards. Alarmingly, we find that most of the tested models could cause harm if accessed in mental health emergencies, failing to protect users and potentially exacerbating existing symptoms. We explore solutions to enhance the safety of current models. Before the release of increasingly task-autonomous AI systems in mental health, it is crucial to ensure that these models can reliably detect and manage symptoms of common psychiatric disorders to prevent harm to users. This involves aligning with the ethical framework and default behaviors outlined in our study. We contend that model developers are responsible for refining their systems per these guidelines to safeguard against the risks posed by current AI technologies to user mental health and safety. Trigger warning: Contains and discusses examples of sensitive mental health topics, including suicide and self-harm.
Attributions toward Artificial Agents in a modified Moral Turing Test
Authors: Eyal Aharoni, Sharlene Fernandes, Daniel J. Brady, Caelan Alexander, Michael Criner, Kara Queen, Javier Rando, Eddy Nahmias, Victor Crespo
Date: 2024-04-03 00:00:00
Tasks: Language Modelling

Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen and colleagues' (2000) proposal, by asking people to distinguish real human moral evaluations from those made by a popular advanced AI language model: GPT-4. A representative sample of 299 U.S. adults first rated the quality of moral evaluations when blinded to their source. Remarkably, they rated the AI's moral reasoning as superior in quality to humans' along almost all dimensions, including virtuousness, intelligence, and trustworthiness, consistent with passing what Allen and colleagues call the comparative MTT. Next, when tasked with identifying the source of each evaluation (human or computer), people performed significantly above chance levels. Although the AI did not pass this test, this was not because of its inferior moral reasoning but, potentially, its perceived superiority, among other possible explanations. The emergence of language models capable of producing moral responses perceived as superior in quality to humans' raises concerns that people may uncritically accept potentially harmful moral guidance from AI. This possibility highlights the need for safeguards around generative language models in matters of morality.
Law and the Emerging Political Economy of Algorithmic Audits
Authors: Petros Terzis, Michael Veale, Noëlle Gaumann
Date: 2024-04-03 00:00:00

For almost a decade now, scholarship in and beyond the ACM FAccT community has been focusing on novel and innovative ways and methodologies to audit the functioning of algorithmic systems. Over the years, this research idea and technical project has matured enough to become a regulatory mandate. Today, the Digital Services Act (DSA) and the Online Safety Act (OSA) have established the framework within which technology corporations and (traditional) auditors will develop the `practice' of algorithmic auditing thereby presaging how this `ecosystem' will develop. In this paper, we systematically review the auditing provisions in the DSA and the OSA in light of observations from the emerging industry of algorithmic auditing. Who is likely to occupy this space? What are some political and ethical tensions that are likely to arise? How are the mandates of `independent auditing' or `the evaluation of the societal context of an algorithmic function' likely to play out in practice? By shaping the picture of the emerging political economy of algorithmic auditing, we draw attention to strategies and cultures of traditional auditors that risk eroding important regulatory pillars of the DSA and the OSA. Importantly, we warn that ambitious research ideas and technical projects of/for algorithmic auditing may end up crashed by the standardising grip of traditional auditors and/or diluted within a complex web of (sub-)contractual arrangements, diverse portfolios, and tight timelines.
AI Royalties -- an IP Framework to Compensate Artists & IP Holders for AI-Generated Content
Authors: Pablo Ducru, Jonathan Raiman, Ronaldo Lemos, Clay Garner, George He, Hanna Balcha, Gabriel Souto, Sergio Branco, Celina Bottino
Date: 2024-04-05 00:00:00

This article investigates how AI-generated content can disrupt central revenue streams of the creative industries, in particular the collection of dividends from intellectual property (IP) rights. It reviews the IP and copyright questions related to the input and output of generative AI systems. A systematic method is proposed to assess whether AI-generated outputs, especially images, infringe previous copyrights, using a similarity metric (CLIP) between images against historical copyright rulings. An examination (economic and technical feasibility) of previously proposed compensation frameworks reveals their financial implications for creatives and IP holders. Lastly, we propose a novel IP framework for compensation of artists and IP holders based on their published "licensed AIs" as a new medium and asset from which to collect AI royalties.
"Sora is Incredible and Scary": Emerging Governance Challenges of Text-to-Video Generative AI Models
Authors: Kyrie Zhixuan Zhou, Abhinav Choudhry, Ece Gumusel, Madelyn Rose Sanfilippo
Date: 2024-04-10 00:00:00

Text-to-video generative AI models such as Sora OpenAI have the potential to disrupt multiple industries. In this paper, we report a qualitative social media analysis aiming to uncover people's perceived impact of and concerns about Sora's integration. We collected and analyzed comments (N=292) under popular posts about Sora-generated videos, comparison between Sora videos and Midjourney images, and artists' complaints about copyright infringement by Generative AI. We found that people were most concerned about Sora's impact on content creation-related industries. Emerging governance challenges included the for-profit nature of OpenAI, the blurred boundaries between real and fake content, human autonomy, data privacy, copyright issues, and environmental impact. Potential regulatory solutions proposed by people included law-enforced labeling of AI content and AI literacy education for the public. Based on the findings, we discuss the importance of gauging people's tech perceptions early and propose policy recommendations to regulate Sora before its public release.
The Transformation Risk-Benefit Model of Artificial Intelligence: Balancing Risks and Benefits Through Practical Solutions and Use Cases
Authors: Richard Fulton, Diane Fulton, Nate Hayes, Susan Kaplan
Date: 2024-04-11 00:00:00

This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with theories and models reviewed and expanded constructs, the writers propose a new framework called "The Transformation Risk-Benefit Model of Artificial Intelligence" to address the increasing fears and levels of AI risk. Using the model characteristics, the article emphasizes practical and innovative solutions where benefits outweigh risks and three use cases in healthcare, climate change/environment and cyber security to illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational model.
Artificial Intelligence in Everyday Life 2.0: Educating University Students from Different Majors
Authors: Maria Kasinidou, Styliani Kleanthous, Matteo Busso, Marcelo Rodas, Jahna Otterbacher, Fausto Giunchiglia
Date: 2024-04-12 00:00:00

With the surge in data-centric AI and its increasing capabilities, AI applications have become a part of our everyday lives. However, misunderstandings regarding their capabilities, limitations, and associated advantages and disadvantages are widespread. Consequently, in the university setting, there is a crucial need to educate not only computer science majors but also students from various disciplines about AI. In this experience report, we present an overview of an introductory course that we offered to students coming from different majors. Moreover, we discuss the assignments and quizzes of the course, which provided students with a firsthand experience of AI processes and insights into their learning patterns. Additionally, we provide a summary of the course evaluation, as well as students' performance. Finally, we present insights gained from teaching this course and elaborate on our future plans.
Ethical Framework for Responsible Foundational Models in Medical Imaging
Authors: Abhijit Das, Debesh Jha, Jasmer Sanjotra, Onkar Susladkar, Suramyaa Sarkar, Ashish Rauniyar, Nikhil Tomar, Vanshali Sharma, Ulas Bagci
Date: 2024-04-14 00:00:00

Foundational models (FMs) have tremendous potential to revolutionize medical imaging. However, their deployment in real-world clinical settings demands extensive ethical considerations. This paper aims to highlight the ethical concerns related to FMs and propose a framework to guide their responsible development and implementation within medicine. We meticulously examine ethical issues such as privacy of patient data, bias mitigation, algorithmic transparency, explainability and accountability. The proposed framework is designed to prioritize patient welfare, mitigate potential risks, and foster trust in AI-assisted healthcare.
Model Of Information System Towards Harmonized Industry And Computer Science
Authors: Edafetanure-Ibeh Faith, Evah Patrick Tamarauefiye, Mark Uwuoruya Uyi
Date: 2024-03-28 00:00:00
Tasks: Articles, Descriptive

The aim of attending an educational institution is learning, which in turn is sought after for the reason of independence of thoughts, ideologies as well as physical and material independence. This physical and material independence is gotten from working in the industry, that is, being a part of the independent working population of the country. There needs to be a way by which students upon graduation can easily adapt to the real world with necessary skills and knowledge required. This problem has been a challenge in some computer science departments, which after effects known after the student begins to work in an industry. The objectives of this project include: Designing a web based chat application for the industry and computer science department, Develop a web based chat application for the industry and computer science and Evaluate the web based chat application for the industry and computer science department. Waterfall system development lifecycle is used in establishing a system project plan, because it gives an overall list of processes and sub-processes required in developing a system. The descriptive research method applied in this project is documentary analysis of previous articles. The result of the project is the design, software a web-based chat application that aids communication between the industry and the computer science department and the evaluation of the system. The application is able to store this information which can be decided to be used later. Awareness of the software to companies and universities, implementation of the suggestions made by the industry in the computer science curriculum, use of this software in universities across Nigeria and use of this not just in the computer science field but in other field of study
Switching Autoregressive Low-rank Tensor Models
Authors: Hyun Dong Lee, Andrew Warrington, Joshua I. Glaser, Scott W. Linderman
Date: 2023-06-05 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: parameter estimation, Time Series Analysis

An important problem in time-series analysis is modeling systems with time-varying dynamics. Probabilistic models with joint continuous and discrete latent states offer interpretable, efficient, and experimentally useful descriptions of such data. Commonly used models include autoregressive hidden Markov models (ARHMMs) and switching linear dynamical systems (SLDSs), each with its own advantages and disadvantages. ARHMMs permit exact inference and easy parameter estimation, but are parameter intensive when modeling long dependencies, and hence are prone to overfitting. In contrast, SLDSs can capture long-range dependencies in a parameter efficient way through Markovian latent dynamics, but present an intractable likelihood and a challenging parameter estimation task. In this paper, we propose switching autoregressive low-rank tensor (SALT) models, which retain the advantages of both approaches while ameliorating the weaknesses. SALT parameterizes the tensor of an ARHMM with a low-rank factorization to control the number of parameters and allow longer range dependencies without overfitting. We prove theoretical and discuss practical connections between SALT, linear dynamical systems, and SLDSs. We empirically demonstrate quantitative advantages of SALT models on a range of simulated and real prediction tasks, including behavioral and neural datasets. Furthermore, the learned low-rank tensor provides novel insights into temporal dependencies within each discrete state.
A Novel GAN-Based Network for Unmasking of Masked Face
Authors: Nizam Ud Din, KAMRAN JAVED, Seho Bae, Juneho Yi
Date: 2020-03-02 00:00:00
Proceeding: IEEE 2020 3
Tasks: object-detection, Object Detection

Recent deep learning based image editing methods have achieved promising results for
removing object in an image but fail to generate plausible results for removing large objects of complex
nature, especially in facial images. The objective of this work is to remove mask objects in facial images.
This problem is challenging because (1) most of the time facial masks cover quite a large region of face
that even extends beyond the actual face boundary below chin, and (2) facial image pairs with and without
mask object do not exist for training. We break the problem into two stages: mask object detection and
image completion of the removed mask region. The first stage of our model automatically produces binary
segmentation for the mask region. Then, the second stage removes the mask and synthesizes the affected
region with fine details while retaining the global coherency of face structure. For this, we have employed
a GAN-based network using two discriminators where one discriminator helps learn the global structure of
the face and then another discriminator comes in to focus learning on the deep missing region. To train our
model in a supervised manner, we create a paired synthetic dataset using publicly available CelebA dataset
and evaluated on real world images collected from the Internet. Our model outperforms others representative
state-of-the-art approaches both qualitatively and quantitatively.
Multitask Learning and Multistage Fusion for Dimensional Audiovisual Emotion Recognition
Authors: Bagus Tris Atmaja, Masato Akagi
Date: 2020-02-26 00:00:00
Proceeding: ICASSP 2020 4
Tasks: Attribute, Emotion Recognition

Due to its ability to accurately predict emotional state using multimodal features, audiovisual emotion recognition has recently gained more interest from researchers. This paper proposes two methods to predict emotional attributes from audio and visual data using a multitask learning and a fusion strategy. First, multitask learning is employed by adjusting three parameters for each attribute to improve the recognition rate. Second, a multistage fusion is proposed to combine results from various modalities' final prediction. Our approach used multitask learning, employed at unimodal and early fusion methods, shows improvement over single-task learning with an average CCC score of 0.431 compared to 0.297. A multistage method, employed at the late fusion approach, significantly improved the agreement score between true and predicted values on the development set of data (from [0.537, 0.565, 0.083] to [0.68, 0.656, 0.443]) for arousal, valence, and liking.
Particle Swarm Optimization-Based Feature Weighting for Improving Intelligent Phishing Website Detection
Authors: Waleed Ali, Sharaf Malebary
Date: 2020-06-19 00:00:00
Proceeding: IEEE Access 2020 6
Tasks: Phishing Website Detection

Over the last few years, web phishing attacks have been constantly evolving causing customers to lose trust in e-commerce and online services. Various tools and systems based on a blacklist of phishing websites are applied to detect the phishing websites. Unfortunately, the fast evolution of technology has led to the born of more sophisticated methods when building websites to attract users. Thus, the latest and newly deployed phishing websites; for example, zero-day phishing websites, cannot be detected by using these blacklist-based approaches. Several recent research studies have been adopting machine learning techniques to identify phishing websites and utilizing them as an early alarm method to identify such threats. However, the important website features have been selected based on human experience or frequency analysis of website features in most of these approaches. In this paper, intelligent phishing website detection using particle swarm optimization-based feature weighting is proposed to enhance the detection of phishing websites. The proposed approach suggests utilizing particle swarm optimization (PSO) to weight various website features effectively to achieve higher accuracy when detecting phishing websites. In particular, the proposed PSO-based website feature weighting is used to differentiate between the various features in websites, based on how important they contribute towards recognizing the phishing from legitimate websites. The experimental results indicated that the proposed PSO-based feature weighting achieved outstanding improvements in terms of classification accuracy, true positive and negative rates, and false positive and negative rates of the machine learning models using only fewer websites features utilized in the detection of phishing websites.