Abstract
Artificial intelligence systems have seamlessly integrated into every aspect of life, including purchases, entertainment, and physicians. In today’s advanced technology, where many benefits have been realized in technology, particularly AI system control, the research shows that these systems are widely associated with ethnic and gender bias in automated decision-making, such as biased recommendations and wrong facial recognition, which may perpetuate gender stereotypes in society. The literature offers techniques to identify, decrease, and prevent these biases and reviews contemporary AI research on algorithmic recruitment, natural language processing, and facial recognition. The literature review addresses AI fairness issues with theoretical knowledge and real-world applications. It encourages AI-fairness research and gives academia and industry useful insights. Despite of the biases embedded in AI systems, it is the responsibility of developers to develop strategies to curb the issues and ensure AI systems remain transparent, fair, and unbiased in all their recommendations. This paper emphasizes the need for continual research and development to keep AI systems transparent, fair, and unbiased in their decision-making. Developers can create AI systems that promote fairness and diversity by following certain guidelines.
Exploring the Impact of Ethnicity and Gender in AI Algorithms
The widespread use of artificial intelligence (AI) has raised a lot of concerns regarding the persistence of biases, notably gender- and ethnicity-based ones. Due to their extensive use and complexity, AI-assisted systems’ automatic assessments are difficult to understand and prove their fairness. Though AI biases have recently emerged as a new issue, bias has long existed in human culture. Whether intentionally or unintentionally, the underlying research reveals a wide range of gender and ethnic biases with the AI-enabled systems, prompting the need to develop mitigation measures such as training on generating fair results in whatever context. AI systems trained on these data may discover implicit biases that were previously impossible to spot. The purpose of this critical literature review is to explores data, algorithms, and social aspects that cause AI biases, with the main aim of attaining fair and inclusive AI systems.
Statement of The Problem
The problem at hand revolves around gender- and ethnicity-based artificial intelligence systems. The widespread use of artificial intelligence (AI) systems has raised concerns about gender and race biases. AI may automate decision-making and boost efficiency, but there is mounting evidence that these systems have biases that can lead to discriminatory consequences, including biased recommendations and faulty facial recognition. Biases impede AI fairness and reinforce social prejudices and inequality. Thus, to ensure that AI systems operate transparently, fairly, and without gender or ethnicity prejudices, we must investigate these biases, understand their effects, and design effective mitigation techniques.
Literature Review
Shrestha and Das (2022) examined gender bias in machine learning and AI applications. NLP algorithms like Word Embeddings, Coreference Resolution, and GloVe have these biases. While AI-enabled recruiting may increase hiring standards, productivity, and transactional labor, it is also responsible for gender and ethnic biases during the recruitment process (Shrestha and Das, 2022). Algorithmic bias causes gender, race, color, and personality-based hiring practices. This was perpetuated through wrong recommendations and false face recognition. The study found that biased algorithm designers and limited raw data sets sometimes create algorithmic biases, even though sometimes these biases occur unwillingly. This article explains biased sources, stressing historical assumptions in automated system training data (Shrestha and Das, 2022). According to the research, biased historical data in AI training sets may extend unconscious biases and create undiscovered discrimination.
The study also illustrates how recommender systems, search engines, and ranking algorithms propagate gender prejudice. Shrestha and Das examine gender-based job discrimination and gender stereotype reinforcement to demonstrate gender biases’ societal roots. They found gender inequality in AI and robotics in judicial systems, medical robots, and self-driving cars (Shrestha and Das, 2022). Women are far less likely to encounter high-paying job ads on Google than men. This suggests that governance needs automated decision-making technologies and that policymakers should investigate these critical areas and overcome gender biases.
Algorithmic bias mitigation measures involve updating algorithms and conditions to achieve optimal predictions. In order to eliminate biases from machine learning (ML) and artificial intelligence (AI) models, post-processing debiasing techniques are often used. Adversarial learning, which tries to improve prediction accuracy while decreasing the adversary’s capacity to forecast protected variables, is a useful technique for eliminating biases from machine learning models (Shrestha & Das, 2022). The objective, for instance, in a credit-worthiness algorithm where gender is a protected variable is to reduce the amount of data that the encoder extracts and that a parameterized model or discriminator can recover. This method of learning preserves facial gesture recognition and classification while eliminating sensitive information from photos, such as gender and race.
Dialogue systems, face recognition algorithms, and visual recognition algorithms have all been debiased using comparable techniques. In general, adversarial learning strategies are essential for improving the precision and confidentiality of machine learning models. The article also emphasizes gender bias ethics and the significance of individual fairness and participatory design in algorithmic system correction (Shrestha and Das, 2022). Shrestha and Das enrich the discussion on fairer and more inclusive AI systems by providing a thorough assessment of the existing literature on the subject matter.
The article “Ethics and discrimination in artificial intelligence-enabled recruitment practices” by Chen (2023) study explores algorithmic recruiting prejudice, revealing its causes and effects. The author underlines that algorithm biases are frequently unintentional and caused by machine learning (ML) faults. Chen attributes algorithmic bias to dataset generation, engineer target definition, and feature selection. The absence of diversity in databases, which favor mainstream groups, causes gender and racial disparities. This perpetuates societal prejudices, generating a “bias in and bias out” situation where algorithmic decision-making unwittingly discriminates (Chen, 2023 p.6).
Chen (2023) further examines gender, ethnicity, skin color, and personality-related algorithmic bias in recruiting. The research demonstrates that gender biases are present in NLP approaches, including Amazon’s ML-based recruiting tool. Skin color discrimination, like Google’s picture app mislabeling photographs, shows the necessity for sophisticated algorithmic bias mitigation.
To mitigate algorithmic recruitment discrimination, Chen (2023) outlines recommendations at both technical and regulatory levels. Fair algorithms need to reorganize uneven data and employ equitable sources to create more impartial datasets. Tools such as Blendoor, which removes names, photographs, and dates from applicant profiles, may be crucial in improving algorithmic transparency and reducing unconscious prejudice. There is also a need for corporate ethical governance, such as audits and various data collection methods, to address the issue of biases in AI (Chen, 2023). Third-party testing, certification, data protection, and non-discrimination compliance are suggested to assure openness and accountability.
Chen P et al. (2023) examine AI fairness concerns, methods, and applications in data management and analytics. Their research emphasizes the importance of fairness in artificial intelligence, especially in decision support systems in various contexts, such as credit applications, where biases might disproportionately harm specific sociodemographic groups (Chen et al., 2023). The research acknowledges the social, political, and economic consequences of AI systems misinterpreting tasks, including value misalignment and social difficulties.
Chen P et al. (2023) examine AI bias conceptual development, including fairness and bias measures, algorithmic bias prevention, and fair representation learning algorithms. The authors critically evaluate commonly used fairness indicators, discuss bias reduction strategies, and provide fair representation learning algorithms. In the demonstration of AI biases, Chen et al. (2023) point out real-world examples from IBM, Facebook, and Google to show how fairness tools and measurements may uncover and minimize bias in AI systems (Chen et al., 2023). For instance, algorithms have incorrectly identified the gender of black women’s faces, incorrectly estimated that a black defendant would commit another crime twice as frequently as a white defendant, and incorrectly displayed a disproportionate number of men rather than women when searching for the term “CEO” in Google images. This most recent study found that skewed picture searches can affect people’s perceptions of the proportion of men and women in various occupations. Despite these advances, the study emphasizes the need for research, tools, and frameworks in this emerging area to estimate AI fairness risk.
The study shows that AI systems have racial and gender biases. AI biases in face recognition, natural language processing, and algorithmic recruiting might damage society. According to the findings, data, algorithms, and social and institutional factors perpetuate these prejudices. To overcome algorithmic, user interaction, and data bias, fair training could play a critical role (Chen et al., 2023). AI fairness is especially crucial in healthcare, criminal justice, education, and human resource management, where search engine job suggestions have shown various biases. Regularization-based fairness, adversarial training, and equalized post-processing may reduce these biases. AI fairness is evolving and needs continual study and tool and framework development to measure and control hazards.
References
Chen, P., Wu, L., & Wang, L. (2023). AI Fairness in Data Management and Analytics: A Review of Challenges, Methodologies and Applications. Applied Sciences, 13(18), 10258. https://doi.org/10.3390/app131810258
Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10(1), 1–12. https://doi.org/10.1057/s41599-023-02079-x
Shrestha, S., & Das, S. (2022). Exploring gender biases in ML and AI academic research through a systematic literature review. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.976838
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