An Empirical Study on Algorithmic Bias

Sen, Sajib; Dasgupta, Dipankar; Gupta, Kishor Datta

doi:10.1109/compsac48688.2020.00-95

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…Additionally, the experimental result may imitate other current studies, reflecting the possibility of developing a COVID-19 screening system using a deep-learning approach. Further analysis includes but is not limited to-understanding deep learning models performance with highly imbalanced data, model performance with a larger dataset, Check for data bias [47], parameter tuning, and developing a decision support system.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities

Ahsan

Gupta

Islam

et al. 2020

MAKE

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The outbreak of COVID-19 has caused more than 200,000 deaths so far in the USA alone, which instigates the necessity of initial screening to control the spread of the onset of COVID-19. However, screening for the disease becomes laborious with the available testing kits as the number of patients increases rapidly. Therefore, to reduce the dependency on the limited test kits, many studies suggested a computed tomography (CT) scan or chest radiograph (X-ray) based screening system as an alternative approach. Thereby, to reinforce these approaches, models using both CT scan and chest X-ray images need to develop to conduct a large number of tests simultaneously to detect patients with COVID-19 symptoms. In this work, patients with COVID-19 symptoms have been detected using eight distinct deep learning techniques, which are VGG16, InceptionResNetV2, ResNet50, DenseNet201, VGG19, MobilenetV2, NasNetMobile, and ResNet15V2, using two datasets: one dataset includes 400 CT scan and another 400 chest X-ray images. Results show that NasNetMobile outperformed all other models by achieving an accuracy of 82.94% in CT scan and 93.94% in chest X-ray datasets. Besides, Local Interpretable Model-agnostic Explanations (LIME) is used. Results demonstrate that the proposed models can identify the infectious regions and top features; ultimately, it provides a potential opportunity to distinguish between COVID-19 patients with others.

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Section: Discussionmentioning

confidence: 99%

COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities

Ahsan

Gupta

Islam

et al. 2020

MAKE

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“…Data bias, such as survivorship bias, Simpson's paradox, and Bergson's paradox, probably occurs in any stage of a Data Science lifecycle. Explainable Arti cial Intelligence is a way to check whether there is algorithmic bias(Sen, Dasgupta, & Gupta, 2020). Besides, User authentication and consent on personnel data is critical for protecting ethics and privacy.…”

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confidence: 99%

WITHDRAWN: Data Science: Trends, Perspectives, and Prospects

Borjigin

Zhang

2021

Preprint

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Data Science is one of today’s most rapidly growing academic fields and has significant implications for all conventional scientific studies. However, most of the relevant studies so far have been limited to one or several facets of Data Science from a specific application domain perspective and fail to discuss its theoretical framework. Data Science is a novel science in that its research goals, perspectives, and body of knowledge is distinct from other sciences. The core theories of Data Science are the DIKW pyramid, data-intensive scientific discovery, data science lifecycle, data wrangling or munging, big data analytics, data management and governance, data products development, and big data visualization. Six main trends characterize the recent theoretical studies on Data Science: growing significance of DataOps, the rise of citizen data scientists, enabling augmented data science, diversity of domain-specific data science, and implementing data stories as data products. The further development of Data Science should prioritize four ways to turning challenges into opportunities: accelerating theoretical studies of data science, the trade-off between explainability and performance, achieving data ethics, privacy and trust, and aligning academic curricula to industrial needs.

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“…Although Kahneman et al ( 2016 ) support the idea that algorithms often lead to a reduction of noise and bias, they also view the application of algorithms as a radical solution since they are sometimes politically or operationally not feasible. Furthermore, a growing research suggest that AI and algorithms should be used in caution as this may lead to algorithmic/automation bias (e.g., Ferguson, 2017 ; Lyell & Coiera, 2016 ; Parasuraman & Manzey, 2010 ; Rai et al, 2019 ; Sen et al, 2020 ). For example, Rai et al ( 2019 ) assert that digital platforms are likely to align well with AI agents (such as speed, accuracy, etc.…”

Section: Introductionmentioning

confidence: 99%

Analysis of noise and bias errors in intelligence information systems

Labib

Chakhar

Hope

et al. 2022

Asso for Info Science & Tech

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An intelligence information system (IIS) is a particular kind of information systems (IS) devoted to the analysis of intelligence relevant to national security. Professional and military intelligence analysts play a key role in this, but their judgments can be inconsistent, mainly due to noise and bias. The team‐oriented aspects of the intelligence analysis process complicates the situation further. To enable analysts to achieve better judgments, the authors designed, implemented, and validated an innovative IIS for analyzing UK Military Signals Intelligence (SIGINT) data. The developed tool, the Team Information Decision Engine (TIDE), relies on an innovative preference learning method along with an aggregation procedure that permits combining scores by individual analysts into aggregated scores. This paper reports on a series of validation trials in which the performance of individual and team‐oriented analysts was accessed with respect to their effectiveness and efficiency. Results show that the use of the developed tool enhanced the effectiveness and efficiency of intelligence analysis process at both individual and team levels.

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An Empirical Study on Algorithmic Bias

Cited by 9 publications

References 5 publications

COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities

COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities

WITHDRAWN: Data Science: Trends, Perspectives, and Prospects

Analysis of noise and bias errors in intelligence information systems

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