pwc-archive/papers-with-abstracts huggingface dataset:
Prompting Diverse Ideas: Increasing AI Idea Variance
Authors: Lennart Meincke, Ethan R. Mollick, Christian Terwiesch
Date: 2024-01-27 00:00:00
Tasks: Diversity, Prompt Engineering

Unlike routine tasks where consistency is prized, in creativity and innovation the goal is to create a diverse set of ideas. This paper delves into the burgeoning interest in employing Artificial Intelligence (AI) to enhance the productivity and quality of the idea generation process. While previous studies have found that the average quality of AI ideas is quite high, prior research also has pointed to the inability of AI-based brainstorming to create sufficient dispersion of ideas, which limits novelty and the quality of the overall best idea. Our research investigates methods to increase the dispersion in AI-generated ideas. Using GPT-4, we explore the effect of different prompting methods on Cosine Similarity, the number of unique ideas, and the speed with which the idea space gets exhausted. We do this in the domain of developing a new product development for college students, priced under $50. In this context, we find that (1) pools of ideas generated by GPT-4 with various plausible prompts are less diverse than ideas generated by groups of human subjects (2) the diversity of AI generated ideas can be substantially improved using prompt engineering (3) Chain-of-Thought (CoT) prompting leads to the highest diversity of ideas of all prompts we evaluated and was able to come close to what is achieved by groups of human subjects. It also was capable of generating the highest number of unique ideas of any prompt we studied.
Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countries
Authors: Rebecca Umbach, Nicola Henry, Gemma Beard, Colleen Berryessa
Date: 2024-01-26 00:00:00
Tasks: Face Swapping

Deepfake technologies have become ubiquitous, "democratizing" the ability to manipulate photos and videos. One popular use of deepfake technology is the creation of sexually explicit content, which can then be posted and shared widely on the internet. Drawing on a survey of over 16,000 respondents in 10 different countries, this article examines attitudes and behaviors related to "deepfake pornography" as a specific form of non-consensual synthetic intimate imagery (NSII). Our study found that deepfake pornography behaviors were considered harmful by respondents, despite nascent societal awareness. Regarding the prevalence of deepfake porn victimization and perpetration, 2.2% of all respondents indicated personal victimization, and 1.8% all of respondents indicated perpetration behaviors. Respondents from countries with specific legislation still reported perpetration and victimization experiences, suggesting NSII laws are inadequate to deter perpetration. Approaches to prevent and reduce harms may include digital literacy education, as well as enforced platform policies, practices, and tools which better detect, prevent, and respond to NSII content.
Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums
Authors: H. Toba, Y. T. Hernita, M. Ayub, M. C. Wijanto
Date: 2024-01-26 00:00:00
Tasks: Sentence, Sentiment Analysis

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom\'s epistemic taxonomy based on textual comments in educational discussion forums. Our proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web Programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom\'s epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis.
LLM on FHIR -- Demystifying Health Records
Authors: Paul Schmiedmayer, Adrit Rao, Philipp Zagar, Vishnu Ravi, Aydin Zahedivash, Arash Fereydooni, Oliver Aalami
Date: 2024-01-25 00:00:00

Objective: To enhance health literacy and accessibility of health information for a diverse patient population by developing a patient-centered artificial intelligence (AI) solution using large language models (LLMs) and Fast Healthcare Interoperability Resources (FHIR) application programming interfaces (APIs). Materials and Methods: The research involved developing LLM on FHIR, an open-source mobile application allowing users to interact with their health records using LLMs. The app is built on Stanford's Spezi ecosystem and uses OpenAI's GPT-4. A pilot study was conducted with the SyntheticMass patient dataset and evaluated by medical experts to assess the app's effectiveness in increasing health literacy. The evaluation focused on the accuracy, relevance, and understandability of the LLM's responses to common patient questions. Results: LLM on FHIR demonstrated varying but generally high degrees of accuracy and relevance in providing understandable health information to patients. The app effectively translated medical data into patient-friendly language and was able to adapt its responses to different patient profiles. However, challenges included variability in LLM responses and the need for precise filtering of health data. Discussion and Conclusion: LLMs offer significant potential in improving health literacy and making health records more accessible. LLM on FHIR, as a pioneering application in this field, demonstrates the feasibility and challenges of integrating LLMs into patient care. While promising, the implementation and pilot also highlight risks such as inconsistent responses and the importance of replicable output. Future directions include better resource identification mechanisms and executing LLMs on-device to enhance privacy and reduce costs.
Exploring Educational Equity: A Machine Learning Approach to Unravel Achievement Disparities in Georgia
Authors: Yichen Ma, Dima Nazzal
Date: 2024-01-25 00:00:00
Tasks: Math

The COVID-19 pandemic has significantly exacerbated existing educational disparities in Georgia's K-12 system, particularly in terms of racial and ethnic achievement gaps. Utilizing machine learning methods, the study conducts a comprehensive analysis of student achievement rates across different demographics, regions, and subjects. The findings highlight a significant decline in proficiency in English and Math during the pandemic, with a noticeable contraction in score distribution and a greater impact on economically disadvantaged and Black students. Socio-economic status, as represented by the Directly Certified Percentage -- the percentage of students eligible for free lunch, emerges as the most crucial factor, with additional insights drawn from faculty resources such as teacher salaries and expenditure on instruction. The study also identifies disparities in achievement rates between urban and rural settings, as well as variations across counties, underscoring the influence of geographical and socio-economic factors. The data suggests that targeted interventions and resource allocation, particularly in schools with higher percentages of economically disadvantaged students, are essential for mitigating educational disparities.
Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generators
Authors: Wiebke Hutiri, Oresiti Papakyriakopoulos, Alice Xiang
Date: 2024-01-25 00:00:00
Tasks: Decision Making

The rapid and wide-scale adoption of AI to generate human speech poses a range of significant ethical and safety risks to society that need to be addressed. For example, a growing number of speech generation incidents are associated with swatting attacks in the United States, where anonymous perpetrators create synthetic voices that call police officers to close down schools and hospitals, or to violently gain access to innocent citizens' homes. Incidents like this demonstrate that multimodal generative AI risks and harms do not exist in isolation, but arise from the interactions of multiple stakeholders and technical AI systems. In this paper we analyse speech generation incidents to study how patterns of specific harms arise. We find that specific harms can be categorised according to the exposure of affected individuals, that is to say whether they are a subject of, interact with, suffer due to, or are excluded from speech generation systems. Similarly, specific harms are also a consequence of the motives of the creators and deployers of the systems. Based on these insights we propose a conceptual framework for modelling pathways to ethical and safety harms of AI, which we use to develop a taxonomy of harms of speech generators. Our relational approach captures the complexity of risks and harms in sociotechnical AI systems, and yields a taxonomy that can support appropriate policy interventions and decision making for the responsible development and release of speech generation models.
Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation
Authors: Jennifer Chien, David Danks
Date: 2024-01-25 00:00:00
Tasks: Fairness

Algorithmic harms are commonly categorized as either allocative or representational. This study specifically addresses the latter, focusing on an examination of current definitions of representational harms to discern what is included and what is not. This analysis motivates our expansion beyond behavioral definitions to encompass harms to cognitive and affective states. The paper outlines high-level requirements for measurement: identifying the necessary expertise to implement this approach and illustrating it through a case study. Our work highlights the unique vulnerabilities of large language models to perpetrating representational harms, particularly when these harms go unmeasured and unmitigated. The work concludes by presenting proposed mitigations and delineating when to employ them. The overarching aim of this research is to establish a framework for broadening the definition of representational harms and to translate insights from fairness research into practical measurement and mitigation praxis.
A Multi-Perspective Machine Learning Approach to Evaluate Police-Driver Interaction in Los Angeles
Authors: Benjamin A. T. Grahama, Lauren Brown, Georgios Chochlakis, Morteza Dehghani, Raquel Delerme, Brittany Friedman, Ellie Graeden, Preni Golazizian, Rajat Hebbar, Parsa Hejabi, Aditya Kommineni, Mayagüez Salinas, Michael Sierra-Arévalo, Jackson Trager, Nicholas Weller, Shrikanth Narayanan
Date: 2024-01-24 00:00:00

Interactions between the government officials and civilians affect public wellbeing and the state legitimacy that is necessary for the functioning of democratic society. Police officers, the most visible and contacted agents of the state, interact with the public more than 20 million times a year during traffic stops. Today, these interactions are regularly recorded by body-worn cameras (BWCs), which are lauded as a means to enhance police accountability and improve police-public interactions. However, the timely analysis of these recordings is hampered by a lack of reliable automated tools that can enable the analysis of these complex and contested police-public interactions. This article proposes an approach to developing new multi-perspective, multimodal machine learning (ML) tools to analyze the audio, video, and transcript information from this BWC footage. Our approach begins by identifying the aspects of communication most salient to different stakeholders, including both community members and police officers. We move away from modeling approaches built around the existence of a single ground truth and instead utilize new advances in soft labeling to incorporate variation in how different observers perceive the same interactions. We argue that this inclusive approach to the conceptualization and design of new ML tools is broadly applicable to the study of communication and development of analytic tools across domains of human interaction, including education, medicine, and the workplace.
Investigating Algorithm Review Boards for Organizational Responsible Artificial Intelligence Governance
Authors: Emily Hadley, Alan Blatecky, Megan Comfort
Date: 2024-01-23 00:00:00

Organizations including companies, nonprofits, governments, and academic institutions are increasingly developing, deploying, and utilizing artificial intelligence (AI) tools. Responsible AI (RAI) governance approaches at organizations have emerged as important mechanisms to address potential AI risks and harms. In this work, we interviewed 17 technical contributors across organization types (Academic, Government, Industry, Nonprofit) and sectors (Finance, Health, Tech, Other) about their experiences with internal RAI governance. Our findings illuminated the variety of organizational definitions of RAI and accompanying internal governance approaches. We summarized the first detailed findings on algorithm review boards (ARBs) and similar review committees in practice, including their membership, scope, and measures of success. We confirmed known robust model governance in finance sectors and revealed extensive algorithm and AI governance with ARB-like review boards in health sectors. Our findings contradict the idea that Institutional Review Boards alone are sufficient for algorithm governance and posit that ARBs are among the more impactful internal RAI governance approaches. Our results suggest that integration with existing internal regulatory approaches and leadership buy-in are among the most important attributes for success and that financial tensions are the greatest challenge to effective organizational RAI. We make a variety of suggestions for how organizational partners can learn from these findings when building their own internal RAI frameworks. We outline future directions for developing and measuring effectiveness of ARBs and other internal RAI governance approaches.
An Online Hierarchical Energy Management System for Energy Communities, Complying with the Current Technical Legislation Framework
Authors: Antonino Capillo, Enrico De Santis, Fabio Massimo Frattale Mascioli, Antonello Rizzi
Date: 2024-01-22 00:00:00
Tasks: energy management, Management

Efforts in the fight against Climate Change are increasingly oriented towards new energy efficiency strategies in Smart Grids (SGs). In 2018, with proper legislation, the European Union (EU) defined the Renewable Energy Community (REC) as a local electrical grid whose participants share their self-produced renewable energy, aiming at reducing bill costs by taking advantage of proper incentives. That action aspires to accelerate the spread of local renewable energy exploitation, whose costs could not be within everyone's reach. Since a REC is technically an SG, the strategies above can be applied, and specifically, practical Energy Management Systems (EMSs) are required. Therefore, in this work, an online Hierarchical EMS (HEMS) is synthesized for REC cost minimization to evaluate its superiority over a local self-consumption approach. EU technical indications (as inherited from Italy) are diligently followed, aiming for results that are as realistic as possible. Power flows between REC nodes, or Microgrids (MGs) are optimized by taking Energy Storage Systems (ESSs) and PV plant costs, energy purchase costs, and REC incentives. A hybrid Fuzzy Inference System - Genetic Algorithm (FIS-GA) model is implemented with the GA encoding the FIS parameters. Power generation and consumption, which are the overall system input, are predicted by a LSTM trained on historical data. The proposed hierarchical model achieves good precision in short computation times and outperforms the self-consumption approach, leading to about 20% savings compared to the latter. In addition, the Explainable AI (XAI), which characterizes the model through the FIS, makes results more reliable thanks to an excellent human interpretation level. To finish, the HEMS is parametrized so that it is straightforward to switch to another Country's technical legislation framework.
"Which LLM should I use?": Evaluating LLMs for tasks performed by Undergraduate Computer Science Students
Authors: Vibhor Agarwal, Madhav Krishan Garg, Sahiti Dharmavaram, Dhruv Kumar
Date: 2024-01-22 00:00:00
Tasks: Code Generation

This study evaluates the effectiveness of various large language models (LLMs) in performing tasks common among undergraduate computer science students. Although a number of research studies in the computing education community have explored the possibility of using LLMs for a variety of tasks, there is a lack of comprehensive research comparing different LLMs and evaluating which LLMs are most effective for different tasks. Our research systematically assesses some of the publicly available LLMs such as Google Bard, ChatGPT(3.5), GitHub Copilot Chat, and Microsoft Copilot across diverse tasks commonly encountered by undergraduate computer science students in India. These tasks include code explanation and documentation, solving class assignments, technical interview preparation, learning new concepts and frameworks, and email writing. Evaluation for these tasks was carried out by pre-final year and final year undergraduate computer science students and provides insights into the models' strengths and limitations. This study aims to guide students as well as instructors in selecting suitable LLMs for any specific task and offers valuable insights on how LLMs can be used constructively by students and instructors.
A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation
Authors: Liliana Marie Prikler, Franz Wotawa
Date: 2024-01-22 00:00:00
Tasks: Diagnostic, Fault Detection

In recent years, digital twins have been proposed and implemented in various fields with potential applications ranging from prototyping to maintenance. Going forward, they are to enable numerous efficient and sustainable technologies, among them autonomous cars. However, despite a large body of research in many fields, academics have yet to agree on what exactly a digital twin is -- and as a result, what its capabilities and limitations might be. To further our understanding, we explore the capabilities of digital twins concerning diagnosis in the field of transportation. We conduct a systematic mapping study including digital twins of vehicles and their components, as well as transportation infrastructure. We discovered that few papers on digital twins describe any diagnostic process. Furthermore, most existing approaches appear limited to system monitoring or fault detection. These findings suggest that we need more research for diagnostic reasoning utilizing digital twins.
Community-based Behavioral Understanding of Crisis Activity Concerns using Social Media Data: A Study on the 2023 Canadian Wildfires in New York City
Authors: Khondhaker Al Momin, Md Sami Hasnine, Arif Mohaimin Sadri
Date: 2024-01-22 00:00:00
Tasks: text-classification, Text Classification

New York City (NYC) topped the global chart for the worst air pollution in June 2023, owing to the wildfire smoke drifting in from Canada. This unprecedented situation caused significant travel disruptions and shifts in traditional activity patterns of NYC residents. This study utilized large-scale social media data to study different crisis activity concerns (i.e., evacuation, staying indoors, shopping, and recreational activities among others) in the emergence of the 2023 Canadian wildfire smoke in NYC. In this regard, one week (June 02 through June 09, 2023) geotagged Twitter data from NYC were retrieved and used in the analysis. The tweets were processed using advanced text classification techniques and later integrated with national databases such as Social Security Administration data, Census, and American Community Survey. Finally, a model has been developed to make community inferences of different activity concerns in a major wildfire. The findings suggest, during wildfires, females are less likely to engage in discussions about evacuation, trips for medical, social, or recreational purposes, and commuting for work, likely influenced by workplaces maintaining operations despite poor air quality. There were also racial disparities in these discussions, with Asians being more likely than Hispanics to discuss evacuation and work commute, and African Americans being less likely to discuss social and recreational activities. Additionally, individuals from low-income neighborhoods and non-higher education students expressed fewer concerns about evacuation. This study provides valuable insights for policymakers, emergency planners, and public health officials, aiding them in formulating targeted communication strategies and equitable emergency response plans.
Leveraging Social Media Data to Identify Factors Influencing Public Attitude Towards Accessibility, Socioeconomic Disparity and Public Transportation
Authors: Khondhaker Al Momin, Arif Mohaimin Sadri, Md Sami Hasnine
Date: 2024-01-22 00:00:00

This study proposes a novel method to understand the factors affecting individuals' perception of transport accessibility, socioeconomic disparity, and public infrastructure. As opposed to the time consuming and expensive survey-based approach, this method can generate organic large-scale responses from social media and develop statistical models to understand individuals' perceptions of various transportation issues. This study retrieved and analyzed 36,098 tweets from New York City from March 19, 2020, to May 15, 2022. A state-of-the-art natural language processing algorithm is used for text mining and classification. A data fusion technique has been adopted to generate a series of socioeconomic traits that are used as explanatory variables in the model. The model results show that females and individuals of Asian origin tend to discuss transportation accessibility more than their counterparts, with those experiencing high neighborhood traffic also being more vocal. However, disadvantaged individuals, including the unemployed and those living in low-income neighborhoods or in areas with high natural hazard risks, tend to communicate less about such issues. As for socioeconomic disparity, individuals of Asian origin and those experiencing various types of air pollution are more likely to discuss these topics on Twitter, often with a negative sentiment. However, unemployed, or disadvantaged individuals, as well as those living in areas with high natural hazard risks or expected losses, are less inclined to tweet about this subject. Lack of internet accessibility could be a reason why many disadvantaged individuals do not tweet about transport accessibility and subsidized internet could be a possible solution.