Ethics of AI: Do the Face Detection Models Act with Prejudice?

Ferreira, Marcos; Almeida, Ariel; Canário, João Paulo; Souza, Matheus Freire de Lima e; Nogueira, Tatiane; Rios, Ricardo

doi:10.1007/978-3-030-91699-2_7

Cited by 3 publications

(8 citation statements)

References 17 publications

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“…Eight papers involved applied ethical aspects. Only [Ferreira et al 2021] presents both. [Ferreira et al 2021] questions: "[...] can we work in such direction without propagating to our algorithms (and data) historical errors as structural and explicit racism?".…”

Section: Resultsmentioning

confidence: 99%

“…Only [Ferreira et al 2021] presents both. [Ferreira et al 2021] questions: "[...] can we work in such direction without propagating to our algorithms (and data) historical errors as structural and explicit racism?". There is implicit meta-research content in this sentence and much of the paper's concluding section.…”

Section: Resultsmentioning

confidence: 99%

“…can the algorithm produce more errors when black people are under analysis?" [Ferreira et al 2021]. As an explicit meta-research occurrence, the authors indicate that the resulting dataset cannot be shared in compliance with the Brazilian General Data Protection Law (Lei Geral de Protec ¸ão de Dados -LGPD) [Brasil 2018].…”

Section: Resultsmentioning

confidence: 99%

“…USP figuring in three of the nine papers [dos Santos and Paraboni 2019, Pavan et al 2020, Gonc ¸alves and Cozman 2021. UFBA is involved in two, both with explicit and provocative social critical content [Ferreira et al 2021, Moura et al 2021].…”

Section: Resultsmentioning

confidence: 99%

“…The inclusion step considered nine publications presenting relevant and pertinent ethical aspects for qualitative synthesis [Moura et al 2021, Broder and Berton 2021, Ferreira et al 2021, Gonc ¸alves and Cozman 2021, Pavan et al 2020, Viana and Alcântara 2020, dos Santos and Paraboni 2019, Carvalho et al 2014, Nunes et al 2013. Four researchers extracted data in order to answer the questions in Table 1.…”

Section: Methodology and Research Methodsmentioning

confidence: 99%

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Ethics: What is the Research Scenario in the Brazilian Conference BRACIS?

Carvalho

Murakami

Suzano

et al. 2022

Anais Do XIX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2022)

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Artificial Intelligence (AI) presents many ethical dilemmas, such as explainability, bias, military uses, surveillance capitalism, employment, and jobs. In the scientific context, AI can lead us to a crisis of reproducibility spread across several areas of knowledge and guide mathematicians to solve high complexity problems. Both companies and government forward their guidelines, recommendations, and materials combining Ethics and AI. In this paper, we investigate the involvement of the Brazilian academic-scientific community with moral or ethical aspects through its publications, covering the Brazilian Conference on Intelligent Systems (BRACIS) as the most prominent Brazilian AI conference. Through a Literature Systematic Review method, we answer the main research question: what is the panorama of the explicit occurrence of ethical aspects in the BRACIS, ENIAC, and STIL conference papers? The results indicate a low occurrence of ethical aspects and increasing behavior over the years. Ethical deliberation was fruitful, constructive, and critical among these few occurrences. Whether in the Brazilian or international context, there are spaces to be filled and open opportunities for exploration along this path.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodology and Research Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Ethics: What is the Research Scenario in the Brazilian Conference BRACIS?

Carvalho

Murakami

Suzano

et al. 2022

Anais Do XIX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2022)

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YOLO and Faster R-CNN Object Detection in Architecture, Engineering and Construction (AEC): Applications, Challenges, and Future Prospects

Rane,

Choudhary,

Rane

2023

SSRN Journal

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Detection of Gender in Crowds Using ResNet Model

Rajeev G. Vishwakarma

2024

jes

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The ResNet model is used in this investigation to suggest a gender detection solution for use in congested settings. Due to occlusions, varied stances, and various features, determining a person's gender in crowded surroundings may be a difficult and time-consuming job. The ResNet model, which is a deep convolutional neural network architecture, is used to solve these difficulties because of its capacity to capture detailed characteristics and its efficiency in managing deep network structures. The strategy that has been suggested entails preprocessing the input photos, sending those images through the ResNet model, and then extracting gender-related characteristics from those images. The ResNet model is made up of a number of residual blocks with skip connections, which makes it easier to learn complicated representations. After that, the learnt characteristics are input into fully linked layers, and then softmax activation is used to determine the subject's gender. The usefulness of the technique that was developed was shown by experimental findings on a large dataset, which achieved a high level of accuracy in gender determination. The use of the ResNet model helps the system to handle complicated scenarios and improves the system's ability to accurately recognize gender in situations with a large number of people. The method that has been developed has the potential to find applications in areas such as surveillance, crowd control, and the study of social behavior.

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Ethics of AI: Do the Face Detection Models Act with Prejudice?

Cited by 3 publications

References 17 publications

Ethics: What is the Research Scenario in the Brazilian Conference BRACIS?

Ethics: What is the Research Scenario in the Brazilian Conference BRACIS?

YOLO and Faster R-CNN Object Detection in Architecture, Engineering and Construction (AEC): Applications, Challenges, and Future Prospects

Detection of Gender in Crowds Using ResNet Model

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