AI ethical biases: normative and information systems development conceptual framework

Chowdhury, Tanay; Oredo, John

doi:10.1080/12460125.2022.2062849

Cited by 7 publications

(2 citation statements)

References 62 publications

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“…Furthermore, our model awaits validation against expansive language models, such as GPT-4 and ChatGPT, which are capable of generating synthetic text dynamically. The cautionary standpoint underscored by [86] regarding the uncritical adoption of artificial intelligence without vigilant scrutiny for inherent biases is salient. To build a more resilient model, we intend to scrutinize our data for potential biases, a step aimed at enhancing its robustness in the face of inherent complexities.…”

Section: Future Workmentioning

confidence: 99%

Leveraging Stacking Framework for Fake Review Detection in the Hospitality Sector

Ashraf,

Javed,

Bellary

et al. 2024

JTAER

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Driven by motives of profit and competition, fake reviews are increasingly used to manipulate product ratings. This trend has caught the attention of academic researchers and international regulatory bodies. Current methods for spotting fake reviews suffer from scalability and interpretability issues. This study focuses on identifying suspected fake reviews in the hospitality sector using a review aggregator platform. By combining features and leveraging various classifiers through a stacking architecture, we improve training outcomes. User-centric traits emerge as crucial in spotting fake reviews. Incorporating SHAP (Shapley Additive Explanations) enhances model interpretability. Our model consistently outperforms existing methods across diverse dataset sizes, proving its adaptable, explainable, and scalable nature. These findings hold implications for review platforms, decision-makers, and users, promoting transparency and reliability in reviews and decisions.

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Section: Future Workmentioning

confidence: 99%

Leveraging Stacking Framework for Fake Review Detection in the Hospitality Sector

Ashraf,

Javed,

Bellary

et al. 2024

JTAER

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“…So, in a way, 'quality' of the data understood as their 'objectivity' or a perfect mapping of reality can never really be achieved. This unavoidable normativity is undoubtedly worthy of its own discussion, and there is an emerging literature on this problem (see, for example, Chowdhury and Oredo, 2022).…”

mentioning

confidence: 99%

Epigenetics and Responsibility

2024

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EPDF and EPUB available Open Access under CC-BY-NC-ND licence. This volume brings together an international team of contributors to provide a much-needed examination of climate litigation in Africa. The book outlines how climate litigation in Africa is distinct as well as pinpointing where it connects with the global conversation. Chapters engage with crucial themes such as human rights approaches to climate governance, corporate liability and the role of gender in climate litigation.

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