137 research outputs found
AI Ethics Issues in Real World: Evidence from AI Incident Database
With the powerful performance of Artificial Intelligence (AI) also comes prevalent ethical issues. Though governments and corporations have curated multiple AI ethics guidelines to curb unethical behavior of AI, the effect has been limited, probably due to the vagueness of the guidelines. In this paper, we take a closer look at how AI ethics issues take place in real world, in order to have a more in-depth and nuanced understanding of different ethical issues as well as their social impact. With a content analysis of AI Incident Database, which is an effort to prevent repeated real world AI failures by cataloging incidents, we identified 13 application areas which often see unethical use of AI, with intelligent service robots, language/vision models and autonomous driving taking the lead. Ethical issues appear in 8 different forms, from inappropriate use and racial discrimination, to physical safety and unfair algorithm. With this taxonomy of AI ethics issues, we aim to provide a perspective for guideline makers to formulate more operable guidelines when trying to deploy AI applications ethically
How NFT Collectors Experience Online NFT Communities: A Case Study of Bored Ape
Non-fungible tokens (NFTs) are unique cryptographic assets representing the
ownership of digital media. NFTs have soared in popularity and trading prices.
However, there exists a large gap in the literature regarding NFTs, especially
regarding the stakeholders and online communities that have formed around NFT
projects. Bored Ape Yacht Club (BAYC) is one of the most influential NFT
projects. Through an observational study of online BAYC communities across
social media platforms and semi-structured interviews with four participants
who owned BAYC NFTs, we explored the experiences of NFT collectors within the
online NFT community. Positive community experiences, i.e., personal expression
and identity, mutual support among BAYC holders, and exclusive access to online
and offline events, were expressed. Encountered challenges included scams and
"cash grab" NFT projects as well as trolling. The results of this study point
towards the welcoming, positive nature of the NFT community, which is a
possible causation factor of the initial rise in popularity of NFTs.
Demotivators, on the other hand, countered the established trustworthiness of
NFT technology among its consumers
Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie
Large language models are quickly gaining momentum, yet are found to
demonstrate gender bias in their responses. In this paper, we conducted a
content analysis of social media discussions to gauge public perceptions of
gender bias in LLMs which are trained in different cultural contexts, i.e.,
ChatGPT, a US-based LLM, or Ernie, a China-based LLM. People shared both
observations of gender bias in their personal use and scientific findings about
gender bias in LLMs. A difference between the two LLMs was seen -- ChatGPT was
more often found to carry implicit gender bias, e.g., associating men and women
with different profession titles, while explicit gender bias was found in
Ernie's responses, e.g., overly promoting women's pursuit of marriage over
career. Based on the findings, we reflect on the impact of culture on gender
bias and propose governance recommendations to regulate gender bias in LLMs
Anonymous Expression in an Online Community for Women in China
Gender issues faced by women can range from workplace harassment to domestic violence. While publicly disclosing these issues on social media can be hard, some may incline to express themselves anonymously. We approached such an anonymous female community on Chinese social media where discussion on gender issues takes place with a qualitative content analysis. By observing anonymous experiences contributed by female users and made publicly available by an influencer, we identified 20 issues commonly discussed, with cheating-partner, controlling parents and age anxiety taking the lead. By describing the anonymously expressed social challenges faced by women in China, in the context of Chinese cultures and expectations about gender, we aim to motivate more policies and platform designs to accommodate the needs of the affected population
How People Perceive The Dynamic Zero-COVID Policy: A Retrospective Analysis From The Perspective of Appraisal Theory
The Dynamic Zero-COVID Policy in China spanned three years and diverse
emotional responses have been observed at different times. In this paper, we
retrospectively analyzed public sentiments and perceptions of the policy,
especially regarding how they evolved over time, and how they related to
people's lived experiences. Through sentiment analysis of 2,358 collected Weibo
posts, we identified four representative points, i.e., policy initialization,
sharp sentiment change, lowest sentiment score, and policy termination, for an
in-depth discourse analysis through the lens of appraisal theory. In the end,
we reflected on the evolving public sentiments toward the Dynamic Zero-COVID
Policy and proposed implications for effective epidemic prevention and control
measures for future crises
User Privacy Harms and Risks in Conversational AI: A Proposed Framework
This study presents a unique framework that applies and extends Solove
(2006)'s taxonomy to address privacy concerns in interactions with text-based
AI chatbots. As chatbot prevalence grows, concerns about user privacy have
heightened. While existing literature highlights design elements compromising
privacy, a comprehensive framework is lacking. Through semi-structured
interviews with 13 participants interacting with two AI chatbots, this study
identifies 9 privacy harms and 9 privacy risks in text-based interactions.
Using a grounded theory approach for interview and chatlog analysis, the
framework examines privacy implications at various interaction stages. The aim
is to offer developers, policymakers, and researchers a tool for responsible
and secure implementation of conversational AI, filling the existing gap in
addressing privacy issues associated with text-based AI chatbots
Understanding the concerns and choices of public when using large language models for healthcare
Large language models (LLMs) have shown their potential in biomedical fields.
However, how the public uses them for healthcare purposes such as medical Q\&A,
self-diagnosis, and daily healthcare information seeking is under-investigated.
In this paper, we adopt a mixed-methods approach, including surveys (N=167) and
interviews (N=17) to investigate how and why the public uses LLMs for
healthcare. LLMs as a healthcare tool have gained popularity, and are often
used in combination with other information channels such as search engines and
online health communities to optimize information quality. LLMs provide more
accurate information and a more convenient interaction/service model compared
to traditional channels. LLMs also do a better job of reducing misinformation,
especially in daily healthcare questions. Doctors using LLMs for diagnosis is
less acceptable than for auxiliary work such as writing medical records. Based
on the findings, we reflect on the ethical and effective use of LLMs for
healthcare and propose future research directions.Comment: 22 page
Toward Understanding the Use of Centralized Exchanges for Decentralized Cryptocurrency
Cryptocurrency has been extensively studied as a decentralized financial technology built on blockchain. However, there is a lack of understanding of user experience with cryptocurrency exchanges, the main means for novice users to interact with cryptocurrency. We conduct a qualitative study to provide a panoramic view of user experience and security perception of exchanges. All 15 Chinese participants mainly use centralized exchanges (CEX) instead of decentralized exchanges (DEX) to trade decentralized cryptocurrency, which is paradoxical. A closer examination reveals that CEXes provide better usability and charge lower transaction fee than DEXes. Country-specific security perceptions are observed. Though DEXes provide better anonymity and privacy protection, and are free of governmental regulation, these are not necessary features for many participants. Based on the findings, we propose design implications to make cryptocurrency trading more decentralized.Ope
Fake News Detection via NLP is Vulnerable to Adversarial Attacks
News plays a significant role in shaping people's beliefs and opinions. Fake
news has always been a problem, which wasn't exposed to the mass public until
the past election cycle for the 45th President of the United States. While
quite a few detection methods have been proposed to combat fake news since
2015, they focus mainly on linguistic aspects of an article without any fact
checking. In this paper, we argue that these models have the potential to
misclassify fact-tampering fake news as well as under-written real news.
Through experiments on Fakebox, a state-of-the-art fake news detector, we show
that fact tampering attacks can be effective. To address these weaknesses, we
argue that fact checking should be adopted in conjunction with linguistic
characteristics analysis, so as to truly separate fake news from real news. A
crowdsourced knowledge graph is proposed as a straw man solution to collecting
timely facts about news events.Comment: 11th International Conference on Agents and Artificial Intelligence
(ICAART 2019
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