How Different Groups Prioritize Ethical Values for Responsible AI

Jakesch, Maurice; Buçinca, Zana; Amershi, Saleema; Olteanu, Alexandra

doi:10.1145/3531146.3533097

Cited by 49 publications

(16 citation statements)

References 29 publications

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“…Human educators remain vital in fostering high-level analysis, creativity, and interpersonal growth [57]. Additionally, sometimes there are concerns related to reliance on technology and algorithms when users experience insufficient self-trust [58][59][60], which raises concerns about the human ability to understand, manage, and responsibly control AI [61] besides limited access to educational resources, disputes over intellectual property rights, and alterations in educational values [55].…”

Section: Genai In Hementioning

confidence: 99%

Strategies for Integrating Generative AI into Higher Education: Navigating Challenges and Leveraging Opportunities

Kurtz,

Amzalag,

Shaked

et al. 2024

Education Sciences

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The recent emergence of generative AI (GenAI) tools such as ChatGPT, Midjourney, and Gemini have introduced revolutionary capabilities that are predicted to transform numerous facets of society fundamentally. In higher education (HE), the advent of GenAI presents a pivotal moment that may profoundly alter learning and teaching practices in aspects such as inaccuracy, bias, overreliance on technology and algorithms, and limited access to educational AI resources that require in-depth investigation. To evaluate the implications of adopting GenAI in HE, a team of academics and field experts have co-authored this paper, which analyzes the potential for the responsible integration of GenAI into HE and provides recommendations about this integration. This paper recommends strategies for integrating GenAI into HE to create the following positive outcomes: raise awareness about disruptive change, train faculty, change teaching and assessment practices, partner with students, impart AI learning literacies, bridge the digital divide, and conduct applied research. Finally, we propose four preliminary scale levels of a GenAI adoption for faculty. At each level, we suggest courses of action to facilitate progress to the next stage in the adoption of GenAI. This study offers a valuable set of recommendations to decision-makers and faculty, enabling them to prepare for the responsible and judicious integration of GenAI into HE.

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Section: Genai In Hementioning

confidence: 99%

Strategies for Integrating Generative AI into Higher Education: Navigating Challenges and Leveraging Opportunities

Kurtz,

Amzalag,

Shaked

et al. 2024

Education Sciences

View full text Add to dashboard Cite

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“…Wright [63] introduced a framework for assessing the ethical implications of a wide range of technologies whose comprehensiveness renders it a suitable baseline for evaluating the ethical implications of using AI in social media and health care contexts. Various tools, methods, and approaches can aid in ensuring the ethical use of AI within the health care domain on SMPs: (1) data visualization tools-these tools are designed to present complex ethical data in a clear and accessible manner, thus aiding health care professionals and other stakeholders in understanding and making informed decisions [134]; (2) sentiment analysis of social media posts related to health care interventions-this technique identifies ethical issues and concerns, such as biases or stigmatization of certain patient groups, by analyzing the sentiment of social media content [135]; (3) crowdsourcing platforms for ethical feedback-these platforms are developed to gather insights from a wide range of individuals on the ethical implications of AI systems and their recommendations, ensuring the inclusion of diverse perspectives and values (this approach highlights potential ethical concerns that development teams may otherwise overlook [136]); (4) fairness-aware ML algorithms-these algorithms are designed to address and mitigate unfairness in both the training data and the algorithmic decision-making process with the goal of promoting equity [137]; (5) privacy-preserving data analysis-this method emphasizes the protection of sensitive data from unauthorized access while enabling meaningful analysis, thus balancing privacy with utility [138,139]; (6) human-in-the-loop approaches by incorporating human oversight and decision-making into AI systems, these approaches aim to ensure that technology aligns with social values and ethical principles, thereby promoting responsible use [140]; (7) value-sensitive design-this approach focuses on identifying and integrating social values and ethical principles into the design and development of AI systems, thereby promoting their alignment with societal ethics [141].…”

Section: Other Ethical Considerationsmentioning

confidence: 99%

Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review

Singhal,

Neveditsin,

Tanveer

et al. 2024

JMIR Med Inform

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Background The use of social media for disseminating health care information has become increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine learning in this process both significant and inevitable. This development raises numerous ethical concerns. This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs). It critically examined these technologies from the perspectives of fairness, accountability, transparency, and ethics (FATE), emphasizing computational and methodological approaches that ensure their responsible application. Objective This study aims to identify, compare, and synthesize existing solutions that address the components of FATE in AI applications in health care on SMPs. Through an in-depth exploration of computational methods, approaches, and evaluation metrics used in various initiatives, we sought to elucidate the current state of the art and identify existing gaps. Furthermore, we assessed the strength of the evidence supporting each identified solution and discussed the implications of our findings for future research and practice. In doing so, we made a unique contribution to the field by highlighting areas that require further exploration and innovation. Methods Our research methodology involved a comprehensive literature search across PubMed, Web of Science, and Google Scholar. We used strategic searches through specific filters to identify relevant research papers published since 2012 focusing on the intersection and union of different literature sets. The inclusion criteria were centered on studies that primarily addressed FATE in health care discussions on SMPs; those presenting empirical results; and those covering definitions, computational methods, approaches, and evaluation metrics. Results Our findings present a nuanced breakdown of the FATE principles, aligning them where applicable with the American Medical Informatics Association ethical guidelines. By dividing these principles into dedicated sections, we detailed specific computational methods and conceptual approaches tailored to enforcing FATE in AI-driven health care on SMPs. This segmentation facilitated a deeper understanding of the intricate relationship among the FATE principles and highlighted the practical challenges encountered in their application. It underscored the pioneering contributions of our study to the discourse on ethical AI in health care on SMPs, emphasizing the complex interplay and the limitations faced in implementing these principles effectively. Conclusions Despite the existence of diverse approaches and metrics to address FATE issues in AI for health care on SMPs, challenges persist. The application of these approaches often intersects with additional ethical considerations, occasionally leading to conflicts. Our review highlights the lack of a unified, comprehensive solution for fully and effectively integrating FATE principles in this domain. This gap necessitates careful consideration of the ethical trade-offs involved in deploying existing methods and underscores the need for ongoing research.

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“…Juxtaposed ethical considerations between AI professionals and the public surface in Jakesch et al [133], which highlight the discrepancy in responsible AI value prioritization. The authors posit that comprehensible XAI systems are instrumental in bridging this divide, thus cultivating trust and alignment with social norms.…”

Section: Digital Ethicsmentioning

confidence: 99%

Classification of Kinematic Data Using Explainable Artificial Intelligence (XAI) for Smart Motion

Yong¹

2021

Explainable Artificial Intelligence for Smart Cities

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Given the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors in accessible terms. Unlike most reviews, which focus on algorithmic and model-centric perspectives, this work takes a "data-centric" view, examining how data collection, processing, and analysis contribute to explainable AI (XAI). We categorize existing work into three categories subject to their purposes: interpretations of deep models, referring to feature attributions and reasoning processes that correlate data points with model outputs; influences of training data, examining the impact of training data nuances, such as data valuation and sample anomalies, on decision-making processes; and insights of domain knowledge, discovering latent patterns and fostering new knowledge from data and models to advance social values and scientific discovery. Specifically, we distill XAI methodologies into data mining operations on training and testing data across modalities, such as images, text, and tabular data, as well as on training logs, checkpoints, models and other DNN behavior descriptors. In this way, our study offers a comprehensive, data-centric examination of XAI from a lens of data mining methods and applications.

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How Different Groups Prioritize Ethical Values for Responsible AI

Cited by 49 publications

References 29 publications

Strategies for Integrating Generative AI into Higher Education: Navigating Challenges and Leveraging Opportunities

Strategies for Integrating Generative AI into Higher Education: Navigating Challenges and Leveraging Opportunities

Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review

Classification of Kinematic Data Using Explainable Artificial Intelligence (XAI) for Smart Motion

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