Spam User Detection Through Deceptive Images in Big Data

Zafar, Shareena; Irum, Nawal; Arshad, Sidra; Nawaz, Tahir

doi:10.1007/978-3-319-99966-1_28

Cited by 1 publication

(1 citation statement)

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“…There are many methods being developed to make collecting and annotating data in an automatic way possible, including data mining of web-based images (Zafar et al, 2019 ), and active learning (AL) for semi-automatic labeling (Wang et al, 2019c ). For data tagging by autonomous agents, some have shown concerns that making agents responsible for this, may lead to incorrect tagging caused by an initial human error.…”

Section: Discussion: Representation and Infrastructurementioning

confidence: 99%

Considerations for a More Ethical Approach to Data in AI: On Data Representation and Infrastructure

Baird

Schuller

2020

Front. Big Data

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Data shapes the development of Artificial Intelligence (AI) as we currently know it, and for many years centralized networking infrastructures have dominated both the sourcing and subsequent use of such data. Research suggests that centralized approaches result in poor representation, and as AI is now integrated more in daily life, there is a need for efforts to improve on this. The AI research community has begun to explore managing data infrastructures more democratically, finding that decentralized networking allows for more transparency which can alleviate core ethical concerns, such as selection-bias. With this in mind, herein, we present a mini-survey framed around data representation and data infrastructures in AI. We outline four key considerations (auditing, benchmarking, confidence and trust, explainability and interpretability) as they pertain to data-driven AI, and propose that reflection of them, along with improved interdisciplinary discussion may aid the mitigation of data-based AI ethical concerns, and ultimately improve individual wellbeing when interacting with AI.

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Section: Discussion: Representation and Infrastructurementioning

confidence: 99%