VITAL-ECG: a de-bias algorithm embedded in a gender-immune device

Paviglianiti, Annunziata; Pasero, Eros

doi:10.1109/metroind4.0iot48571.2020.9138291

Cited by 14 publications

(16 citation statements)

References 11 publications

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“…Analysis bias in sensitive personal information used to train financial models [30] 15 VITAL-ECG: A de-bias algorithm embedded in a gender-immune device [31] 16…”

Section: Resultsmentioning

confidence: 99%

“…Some solutions may have hybrid behavior in terms of mitigation type, handling data in pre-processing and models in in-processing. As an example, consider [30], [31] and [35]. [30] proposed a model for a banking system that can rectify skewed data collection by assuring the removal of customer data after output without affecting the ML model.…”

Section: Mitigation Techniques and Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Bias and unfairness in machine learning models: a systematic literature review

Pagano¹,

Loureiro²,

Araujo³

et al. 2022

Preprint

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One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study aims to examine existing knowledge on bias and unfairness in Machine Learning models, identifying mitigation methods, fairness metrics, and supporting tools. A Systematic Literature Review found 40 eligible articles published between 2017 and 2022 in the Scopus, IEEE Xplore, Web of Science, and Google Scholar knowledge bases. The results show numerous bias and unfairness detection and mitigation approaches for ML technologies, with clearly defined metrics in the literature, and varied metrics can be highlighted. We recommend further research to define the techniques and metrics that should be employed in each case to standardize and ensure the impartiality of the machine learning model, thus, allowing the most appropriate metric to detect bias and unfairness in a given context.

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“…Analysis bias in sensitive personal information used to train financial models [30] 15 VITAL-ECG: A de-bias algorithm embedded in a gender-immune device [31] 16…”

Section: Resultsmentioning

confidence: 99%

Section: Mitigation Techniques and Modelsmentioning

confidence: 99%

Bias and unfairness in machine learning models: a systematic literature review

Pagano¹,

Loureiro²,

Araujo³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Several studies have also identified fairness issues that arise when machine learning methods are applied to these data. For example, the Vital-ECG, a wearable smart device that monitors ECG and chest X-ray signals, is embedded with machine learning methods to predict and monitor arterial blood pressure [73] and is found to underestimate the risk of disease in female patients. Similar issues of fairness are found in models using genetic data.…”

Section: Other Data Typesmentioning

confidence: 99%

Fair Machine Learning in Healthcare: A Review

Feng¹,

Du²,

Zou³

et al. 2022

Preprint

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Benefiting from the digitization of healthcare data and the development of computing power, machine learning methods are increasingly used in the healthcare domain. Fairness problems have been identified in machine learning for healthcare, resulting in an unfair allocation of limited healthcare resources or excessive health risks for certain groups. Therefore, addressing the fairness problems has recently attracted increasing attention from the healthcare community. However, the intersection of machine learning for healthcare and fairness in machine learning remains understudied. In this review, we build the bridge by exposing fairness problems, summarizing possible biases, sorting out mitigation methods and pointing out challenges along with opportunities for the future.

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“…Short-term fatigue measurement [16,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] Reduction of fatigue [7,15,24,[34][35][36][37][38][39][40][41][42][43] Integration of human short-term fatigue in an industrial decision system [2,[44][45][46][47][48][49][50][51][52][53][54][55]…”

Section: Short-term Fatiguementioning

confidence: 99%

“…Heartbeat is also an indicator used in the understanding of the difficulty of a task and on the fatigue the operator may experience [26,29]. A more practical solution is to use a smartphone-or smartwatch-integrated accelerometer, which monitors the movement of the body in order to obtain information about energy expenditure during an effort [22,32]. These different studies show that physical sensors are a good solution in order to grasp how the human body responds when doing industrial tasks as well as identifying risks for the operator, knowing that computer simulation is also used in some cases [23].…”

Section: Short-term Fatigue Measurementmentioning

confidence: 99%

Health-Related Parameters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review

et al. 2021

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Human factors have always been an important part of research in industry, but more recently the idea of sustainable development has attracted considerable interest for manufacturing companies and management practitioners. Incorporating human factors into a decision system is a difficult challenge for manufacturing companies because the data related to human factors are difficult to sense and integrate into the decision-making processes. Our objectives with this review are to propose an overview of the different methods to measure human factors, of the solutions to reduce the occupational strain for workers and of the technical solutions to integrate these measures and solutions into a complex industrial decision system. The Scopus database was systematically searched for works from 2014 to 2021 that describe some aspects of human factors in industry. We categorized these works into three different classes, representing the specificity of the studied human factor. This review aims to show the main differences between the approaches of short-term fatigue, long-term physical strain and psychosocial risks. Long-term physical strain is the subject that concentrates the most research efforts, mainly with physical and simulation techniques to highlight physical constraints at work. Short-term fatigue and psychosocial constraints have become a growing concern in industry due to new technologies that increase the requirements of cognitive activities of workers. Human factors are taking an important place in the sustainable development of industry, in order to ameliorate working conditions. However, vigilance is required because health-related data creation and exploitation are sensible for the integrity and privacy of workers.

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VITAL-ECG: a de-bias algorithm embedded in a gender-immune device

Cited by 14 publications

References 11 publications

Bias and unfairness in machine learning models: a systematic literature review

Bias and unfairness in machine learning models: a systematic literature review

Fair Machine Learning in Healthcare: A Review

Health-Related Parameters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review

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