ETHICAL IMPLICATIONS AND BIAS IN GENERATIVE AI
DOI:
https://doi.org/10.36676/jrps.v14.i5.1541Keywords:
Ethics, Biases,, Generative AI, Data Privacy, False InformationAbstract
This has led to several opportunities, mainly because generative AI has grown quickly, mostly in the content and services sectors. Since then, there have been some improvements to face these challenges, but there are still some urgent ethical issues and biases. This paper aims to discuss the following concerns concerning ethical questions around generative AI: privacy issues, the threat of misinformation, and issues related to intellectual property ownership. Also, it further proves the existence of bias in the systems and advocates for the consideration of race, gender, and other factors in society. Regarding these issues, the paper tries to answer the questions of what risks generative AI systems have, how these threats can be eradicated, and how the equity of using AI can be enhanced, which is unknown and remains a question among scholars and practitioners.
References
Bogroff, A., & Guegan, D. (2019). Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation. https://shs.hal.science/halshs-02181597/document DOI: https://doi.org/10.2139/ssrn.3419360
Mallreddy, S. R., & Vasa, Y. (2023b). Predictive maintenance in cloud computing and DEVOPS: ML models for anticipating and preventing system failures. PREDICTIVE MAINTENANCE IN CLOUD COMPUTING AND DEVOPS: ML MODELS FOR ANTICIPATING AND PREVENTING SYSTEM FAILURES, 10(1), 213–219. https://doi.org/10.53555/nveo.v10i1.5751 DOI: https://doi.org/10.53555/nveo.v10i1.5751
Mallreddy, S. R., & Vasa, Y. (2023). Natural language querying in SIEM systems: Bridging the gap between security analysts and complex data. NATURAL LANGUAGE QUERYING IN SIEM SYSTEMS: BRIDGING THE GAP BETWEEN SECURITY ANALYSTS AND COMPLEX DATA, 10(1), 205–212. https://doi.org/10.53555/nveo.v10i1.5750 DOI: https://doi.org/10.53555/nveo.v10i1.5750
Vasa, Y., Mallreddy, S. R., & Jami, V. S. (2022). AUTOMATED MACHINE LEARNING FRAMEWORK USING LARGE LANGUAGE MODELS FOR FINANCIAL SECURITY IN CLOUD OBSERVABILITY. International Journal of Research and Analytical Reviews , 9(3), 183–190.
Vasa, Y., Singirikonda, P., & Mallreddy, S. R. (2023). AI Advancements in Finance: How Machine Learning is Revolutionizing Cyber Defense. International Journal of Innovative Research in Science, Engineering and Technology, 12(6), 9051–9060.
Vasa, Y., & Singirikonda, P. (2022). Proactive Cyber Threat Hunting With AI: Predictive And Preventive Strategies. International Journal of Computer Science and Mechatronics, 8(3), 30–36.
Vasa, Y., Mallreddy, S. R., & Jaini, S. (2023). AI And Deep Learning Synergy: Enhancing Real-Time Observability And Fraud Detection In Cloud Environments, 6(4), 36–42. https://doi.org/ 10.13140/RG.2.2.12176.83206
Katikireddi, P. M., Singirikonda, P., & Vasa, Y. (2021). Revolutionizing DEVOPS with Quantum Computing: Accelerating CI/CD pipelines through Advanced Computational Techniques. Innovative Research Thoughts, 7(2), 97–103. https://doi.org/10.36676/irt.v7.i2.1482 DOI: https://doi.org/10.36676/irt.v7.i2.1482
Vasa, Y., Cheemakurthi, S. K. M., & Kilaru, N. B. (2022). Deep Learning Models For Fraud Detection In Modernized Banking Systems Cloud Computing Paradigm. International Journal of Advances in Engineering and Management, 4(6), 2774–2783. https://doi.org/10.35629/5252-040627742783
Vasa, Y., Kilaru, N. B., & Gunnam, V. (2023). Automated Threat Hunting In Finance Next Gen Strategies For Unrivaled Cyber Defense. International Journal of Advances in Engineering and Management, 5(11). https://doi.org/10.35629/5252-0511461470
Vasa, Y., & Mallreddy, S. R. (2022). Biotechnological Approaches To Software Health: Applying Bioinformatics And Machine Learning To Predict And Mitigate System Failures. Natural Volatiles & Essential Oils, 9(1), 13645–13652. https://doi.org/https://doi.org/10.53555/nveo.v9i2.5764
Mallreddy, S. R., & Vasa, Y. (2022). Autonomous Systems In Software Engineering: Reducing Human Error In Continuous Deployment Through Robotics And AI. NVEO - Natural Volatiles & Essential Oils, 9(1), 13653–13660. https://doi.org/https://doi.org/10.53555/nveo.v11i01.5765
Vasa, Y., Jaini, S., & Singirikonda, P. (2021). Design Scalable Data Pipelines For Ai Applications. NVEO - Natural Volatiles & Essential Oils, 8(1), 215–221. https://doi.org/https://doi.org/10.53555/nveo.v8i1.5772 DOI: https://doi.org/10.53555/nveo.v8i1.5772
Singirikonda, P., Jaini, S., & Vasa, Y. (2021). Develop Solutions To Detect And Mitigate Data Quality Issues In ML Models. NVEO - Natural Volatiles & Essential Oils, 8(4), 16968–16973. https://doi.org/https://doi.org/10.53555/nveo.v8i4.5771 DOI: https://doi.org/10.53555/nveo.v8i4.5771
Vasa, Y. (2021). Develop Explainable AI (XAI) Solutions For Data Engineers. NVEO - Natural Volatiles & Essential Oils, 8(3), 425–432. https://doi.org/https://doi.org/10.53555/nveo.v8i3.5769 DOI: https://doi.org/10.53555/nveo.v8i3.5769
Sukender Reddy Mallreddy. (2023). ENHANCING CLOUD DATA PRIVACY THROUGH FEDERATED LEARNING: A DECENTRALIZED APPROACH TO AI MODEL TRAINING. IJRDO -Journal of Computer Science Engineering, 9(8), 15-22. DOI: https://doi.org/10.53555/cse.v9i8.6131
Mallreddy, S.R., Nunnaguppala, L.S.C., & Padamati, J.R. (2022). Ensuring Data Privacy with CRM AI: Investigating Customer Data Handling and Privacy Regulations. ResMilitaris. Vol.12(6). 3789-3799
Nunnagupala, L. S. C. ., Mallreddy, S. R., & Padamati, J. R. . (2022). Achieving PCI Compliance with CRM Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 13(1), 529–535. DOI: https://doi.org/10.61841/turcomat.v13i1.14689
Jangampeta, S., Mallreddy, S.R., & Padamati, J.R. (2021). Anomaly Detection for Data Security in SIEM: Identifying Malicious Activity in Security Logs and User Sessions. 10(12), 295-298
Jangampeta, S., Mallreddy, S. R., & Padamati, J. R. (2021). Data Security: Safeguarding the Digital Lifeline in an Era of Growing Threats. International Journal for Innovative Engineering and Management Research, 10(4), 630-632.
Sukender Reddy Mallreddy(2020).Cloud Data Security: Identifying Challenges and Implementing Solutions.JournalforEducators,TeachersandTrainers,Vol.11(1).96 -102.
Nunnaguppala, L. S. C. , Sayyaparaju, K. K., & Padamati, J. R.. (2021). "Securing The Cloud: Automating Threat Detection with SIEM, Artificial Intelligence & Machine Learning", International Journal For Advanced Research In Science & Technology, Vol 11 No 3, 385-392
Padamati, J., Nunnaguppala, L., & Sayyaparaju, K. . (2021). "Evolving Beyond Patching: A Framework for Continuous Vulnerability Management", Journal for Educators, Teachers and Trainers, 12(2), 185-193.
Nunnaguppala, L. S. C. . (2021). "Leveraging AI In Cloud SIEM And SOAR: Real-World Applications For Enhancing SOC And IRT Effectiveness", International Journal for Innovative Engineering and Management Research,10(08), 376-393
Sayyaparaju, K. K., Nunnaguppala, L. S. C. , & Padamati, J. R.. (2021). "Building SecureAI/ML Pipelines: Cloud Data Engineeringfor Compliance and Vulnerability Management", International Journal for Innovative Engineering and Management Research,10(10), 330-340
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