Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms

Zhou, Nengfeng; Zhang, Zach; Nair, Vijayan N.; Singhal, Harsh; Chen, Jie

doi:10.1111/insr.12492

Cited by 24 publications

(10 citation statements)

References 24 publications

(49 reference statements)

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“…Inconsistencies in data quality and biases in the datasets when using diverse data sources can result in skewed AI models that eventually become unreliable tools for diagnosing HF [26,188,189].…”

Section: Heartlogic™ Algorithmmentioning

confidence: 99%

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Medhi,

Kamidi,

Mamatha Sree

et al. 2024

Cureus

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Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.

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Section: Heartlogic™ Algorithmmentioning

confidence: 99%

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Medhi,

Kamidi,

Mamatha Sree

et al. 2024

Cureus

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“…The analysis of bias and fairness in AI algorithms is a critical aspect of ethical AI development. Zhou et al (2021) provide an overview of the challenges associated with bias and fairness in machine learning (ML) algorithms. They discuss the types and sources of data bias and the nature of algorithmic unfairness.…”

Section: Analysis Of Bias and Fairness In Ai Algorithmsmentioning

confidence: 99%

Ethical AI in practice: Balancing technological advancements with human values

Benjamin Samson Ayinla,

Olukunle Oladipupo Amoo,

Akoh Atadoga

et al. 2024

Int. J. Sci. Res. Arch.

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In an era where artificial intelligence (AI) increasingly intersects with every facet of human life, the imperative for ethical AI has never been more pronounced. This paper delves into the complex interplay between technological advancements in AI and the overarching human values that guide societal norms. The background of the study establishes the urgency of addressing ethical challenges inherent in AI, such as privacy, bias, and accountability, within the broader context of regulatory and policy frameworks. Aiming to critically evaluate the integration and effectiveness of ethical principles in AI applications, the paper navigates through a qualitative analysis, employing theoretical frameworks to dissect the ethical dimensions of AI. The scope encompasses a diverse range of topics, including global trends in ethical AI development, the impact of AI on human rights and personal freedoms, and the analysis of bias and fairness in AI algorithms. Real-world case studies provide insights into the successes and failures of ethical AI implementation, while the role of public perception and trust in AI adoption is scrutinized. The main conclusions reveal a dynamic global landscape of ethical AI, emphasizing the need for robust ethical frameworks and proactive strategies to mitigate biases and ensure equitable outcomes. Recommendations advocate for clear ethical guidelines, integration of ethics in AI development, transparency, accountability, multi-stakeholder collaboration, public engagement, and continuous ethical evaluation. The study concludes that balancing technological innovation with ethical constraints is crucial for the responsible development of AI. It underscores the importance of ethical vigilance, ensuring AI aligns with societal values and individual rights.

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“…There have been increasing concerns over AI-related data bias and ethical issues [155,156]. Fundamentally, AI models should facilitate but not replace human judgment and decision-making [157,158].…”

Section: Human-in-the-loopmentioning

confidence: 99%

Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies

An¹,

Shen²,

Xiao³

2022

J Med Internet Res

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Background Obesity is a leading cause of preventable death worldwide. Artificial intelligence (AI), characterized by machine learning (ML) and deep learning (DL), has become an indispensable tool in obesity research. Objective This scoping review aimed to provide researchers and practitioners with an overview of the AI applications to obesity research, familiarize them with popular ML and DL models, and facilitate the adoption of AI applications. Methods We conducted a scoping review in PubMed and Web of Science on the applications of AI to measure, predict, and treat obesity. We summarized and categorized the AI methodologies used in the hope of identifying synergies, patterns, and trends to inform future investigations. We also provided a high-level, beginner-friendly introduction to the core methodologies to facilitate the dissemination and adoption of various AI techniques. Results We identified 46 studies that used diverse ML and DL models to assess obesity-related outcomes. The studies found AI models helpful in detecting clinically meaningful patterns of obesity or relationships between specific covariates and weight outcomes. The majority (18/22, 82%) of the studies comparing AI models with conventional statistical approaches found that the AI models achieved higher prediction accuracy on test data. Some (5/46, 11%) of the studies comparing the performances of different AI models revealed mixed results, indicating the high contingency of model performance on the data set and task it was applied to. An accelerating trend of adopting state-of-the-art DL models over standard ML models was observed to address challenging computer vision and natural language processing tasks. We concisely introduced the popular ML and DL models and summarized their specific applications in the studies included in the review. Conclusions This study reviewed AI-related methodologies adopted in the obesity literature, particularly ML and DL models applied to tabular, image, and text data. The review also discussed emerging trends such as multimodal or multitask AI models, synthetic data generation, and human-in-the-loop that may witness increasing applications in obesity research.

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Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms

Cited by 24 publications

References 24 publications

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Ethical AI in practice: Balancing technological advancements with human values

Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies

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