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Screening for Sexually Transmitted Infections in Adolescents with Genitourinary Complaints: Is There a Still Role for Endocervical Gram Stains?
Subah Nanda, Amanda Schoonover, Jasman Kaur, Annie Vu, Erica Tavares, Angela Zamarripa, Christian Kolacki, Lindsey Ouellette and Jeffrey Jones
Abstract

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We strive to bring experts together from different areas to enhance research and its impacts.

Engineering Group Review Article Article ID: igmin250

Artificial Intelligence & the Capacity for Discrimination: The Imperative Need for Frameworks, Diverse Teams & Human Accountability

Educational Technology Machine LearningArtificial IntelligenceEducational Science DOI10.61927/igmin250 Affiliation

Affiliation

    Destiny J Hunter, National University, Kuinua Tech LLC, USA, Email: [email protected]; [email protected]

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Abstract

The increasing integration of Artificial Intelligence (AI) in various industries has led to concerns about how these systems can perpetuate discrimination, particularly in fields like employment, healthcare, and public policy. Multiple academic and business perspectives on AI discrimination, focusing on the need for global policy coordination and ethical oversight to mitigate biased outcomes, ask for our technical innovators to create contingencies that will better protect humanity’s experience with AI’s ever-expanding reach. Central to the key constructs such as biased datasets, algorithmic transparency, and the global governance of AI systems can function as a harmful drawback to these systems. Without adequate data governance and transparency, AI systems can perpetuate discrimination. 
AI's ability to discriminate stems primarily from biased data and the opacity of machine learning models, necessitating proactive research and policy implementation on a global scale. These frameworks must transcend the limitations of the experiences or perspectives of their programmers to ensure that AI innovations are ethically sound and that their use in global organizations adheres to principles of fairness and accountability. This synthesis will explore how these articles advocate for comprehensive, continuous monitoring of AI systems and policies that address both local and international concerns, offering a roadmap for organizations to innovate responsibly while mitigating the risks of AI-driven discrimination.

References

    1. Ajunwa I. Artificial intelligence and the challenges of workplace discrimination. SSRN Electron J. 2020.
    2. Binns R, Veale M, Van Kleek M. Integrating ethics in AI development: A qualitative study. BMC Med Ethics. 2022;23:100.
    3. Westerman G. How to implement digital transformation successfully. Harv Bus Rev. 2020.
    4. Floridi L, Cowls J. The global impact of artificial intelligence on public policy. Sustainability. 2020;12(17):7076.
    5. S. Equal Employment Opportunity Commission. ITutorGroup to pay $365,000 to settle EEOC discriminatory hiring suit. US EEOC. 2023 Sep 11. Available from: https://www.eeoc.gov/newsroom/itutorgroup-pay-365000-settle-eeoc-discriminatory-hiring-suit
    6. Ribeiro MT, Singh S, Guestrin C. "Why should I trust you?" Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016 Aug 13-17; San Francisco, CA. p. 1135-44. Available from: https://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf
    7. Mitchell M, Wu S, Zaldivar A, Barnes P, Vasserman L, Hutchinson B, et al. Model cards for model reporting. In: Proceedings of the Conference on Fairness, Accountability, and Transparency; 2019 Jan 29-Feb 1; Atlanta, GA. p. 220-9. Available from: https://dl.acm.org/doi/10.1145/3287560.3287596
    8. Zanzotto FM. Human-in-the-loop artificial intelligence. J Artif Intell Res. 2019;64:243-52.
    9. National Institute of Standards and Technology (NIST). Artificial Intelligence Risk Management Framework (AI RMF) 1.0. 2023. Available from: https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1270.pdf
    10. Wójcik MA. Algorithmic discrimination in health care: an EU law perspective. PubMed Central (PMC). 2022 Jun 1. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9212826/
    11. Raji ID, Buolamwini J. Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society; 2019 Jan 27-28; Honolulu, HI. Available from: https://dl.acm.org/doi/10.1145/3306618.3314244

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