AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications

Chen, Pu; Wu, Linna; Wang, Lei

doi:10.3390/app131810258

Cited by 16 publications

(4 citation statements)

References 245 publications

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“…Other measures include the use of Explicit AI (XAI) methods, which aim to provide interpretability and transparency to the predictions of machine algorithms. Such methods contribute to addressing concerns about bias and fairness in AI-driven approaches, providing a clearer understanding of the underlying mechanisms and assumptions guiding predictions (Chen et al, 2023).…”

Section: Limitations Of Artificial Intelligencementioning

confidence: 99%

The recent advances in the approach of artificial intelligence (AI) towards drug discovery

Khan,

Raza,

Shahbaz

et al. 2024

Front. Chem.

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Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and denovo drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery.

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Section: Limitations Of Artificial Intelligencementioning

confidence: 99%

The recent advances in the approach of artificial intelligence (AI) towards drug discovery

Khan,

Raza,

Shahbaz

et al. 2024

Front. Chem.

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“…AI enables advanced data analytics techniques, such as natural language processing (NLP) and network analysis, to extract insights from unstructured data sources, such as emails, documents, and social media, that are traditionally challenging to analyze manually. AI offers several advantages over traditional risk assessment methods, making it a valuable tool for auditors: AI algorithms can continuously learn from new data, allowing them to adapt to changing risk environments and identify emerging risks in real-time (Chen, Wu & Wang, 2023, Mitan, 2024. This dynamic learning capability enhances the accuracy and effectiveness of risk assessment processes.…”

Section: The Role Of Ai In Risk Assessmentmentioning

confidence: 99%

AI-Driven risk assessment: Revolutionizing audit planning and execution

Ebere Ruth Onwubuariri,

Beatrice Oyinkansola Adelakun,

Omolara Patricia Olaiya

et al. 2024

Financ. account. res. j.

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Artificial Intelligence (AI) is profoundly transforming risk assessment in audit planning and execution, offering unparalleled advancements in efficiency, accuracy, and strategic decision-making. This review explores the role of AI-driven risk assessment in revolutionizing the audit process, highlighting its benefits and the challenges associated with its implementation. AI technologies, particularly machine learning and advanced data analytics, are enhancing auditors' ability to identify, assess, and manage risks. Traditional risk assessment methods often rely on historical data and static models, which can be limited in their predictive power. In contrast, AI-driven approaches leverage vast datasets, continuously updating and learning from new information to provide dynamic and precise risk evaluations. One of the primary benefits of AI in risk assessment is its ability to process and analyze large volumes of data rapidly. AI algorithms can identify patterns and anomalies that may indicate potential risks, which might be missed by human auditors due to cognitive biases or data overload. This capability ensures a more comprehensive and accurate risk assessment, enabling auditors to focus on high-risk areas and allocate resources more effectively. Moreover, AI-driven risk assessment enhances the strategic planning of audits. By providing real-time insights into emerging risks, AI allows auditors to anticipate and address issues proactively. This forward-looking approach not only improves the efficiency of audit execution but also strengthens the overall risk management framework of organizations. Despite these advantages, integrating AI into risk assessment poses several challenges. Ensuring the quality and integrity of data is crucial, as AI systems rely on accurate and relevant information to produce reliable risk assessments. Additionally, the "black box" nature of some AI models can create transparency issues, making it difficult for auditors to explain how specific risk assessments were derived. Addressing algorithmic biases and ensuring compliance with regulatory standards are also critical concerns. In conclusion, AI-driven risk assessment is revolutionizing audit planning and execution by enhancing the ability to detect and manage risks with greater precision and efficiency. However, to fully realize its potential, auditors must navigate challenges related to data quality, transparency, and ethical considerations. By doing so, the audit profession can leverage AI technologies to achieve more robust and effective risk management practices, ultimately enhancing organizational resilience and accountability. Keywords: AI-Driven, Risk Assessment, Revolutionizing, Audit Planning and Execution

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“…It argues for the development of more universally applicable AI models in emotion recognition. [11] emphasizes the importance of diverse data sets for training AI in emotion recognition, pointing out that many current models are limited by the homogeneity of their training data.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of an advanced ai-based model for human psychoemotional state analysis

Ainakulov,

Koshekov,

Savostin

et al. 2023

EEJET

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The research focuses on developing a novel method for the automatic recognition of human psychoemotional states (PES) using deep learning technology. This method is centered on analyzing speech signals to classify distinct emotional states. The primary challenge addressed by this research is to accurately perform multiclass classification of seven human psychoemotional states, namely joy, fear, anger, sadness, disgust, surprise, and a neutral state. Traditional methods have struggled to accurately distinguish these complex emotional nuances in speech. The study successfully developed a model capable of extracting informative features from audio recordings, specifically mel spectrograms and mel-frequency cepstral coefficients. These features were then used to train two deep convolutional neural networks, resulting in a classifier model. The uniqueness of this research lies in its use of a dual-feature approach and the employment of deep convolutional neural networks for classification. This approach has demonstrated high accuracy in emotion recognition, with an accuracy rate of 0.93 in the validation subset. The high accuracy and effectiveness of the model can be attributed to the comprehensive and synergistic use of mel spectrograms and mel-frequency cepstral coefficients, which provide a more nuanced analysis of emotional expressions in speech. The method presented in this research has broad applicability in various domains, including enhancing human-machine interface interactions, implementation in the aviation industry, healthcare, marketing, and other fields where understanding human emotions through speech is crucial

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AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications

Cited by 16 publications

References 245 publications

The recent advances in the approach of artificial intelligence (AI) towards drug discovery

The recent advances in the approach of artificial intelligence (AI) towards drug discovery

AI-Driven risk assessment: Revolutionizing audit planning and execution

Development of an advanced ai-based model for human psychoemotional state analysis

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