Fairness and Accountability in Machine Learning Algorithms: Addressing Bias and Ethical Challenges

Raj Kala

Abstract


This abstract examines the critical issue of fairness and accountability within machine learning algorithms, focusing on the imperative task of addressing bias and ethical challenges. Machine learning models wield immense power in decision-making processes across various domains, yet they often harbor biases, inadvertently perpetuating societal inequities. This paper scrutinizes the complexities surrounding bias detection and mitigation techniques while navigating the ethical considerations inherent in algorithmic decision-making. It explores strategies aimed at enhancing transparency, accountability, and fairness in machine learning, emphasizing the need for interdisciplinary collaborations among researchers, policymakers, and industry stakeholders to engender responsible AI systems that uphold fairness, mitigate biases, and uphold ethical standards.

References


Mallikarjunaradhya, V., & Pothukuchi, A. S. (2015). The Future of SAAS Startups: How AI Accelerates Market Research and Product Development. Asian Journal of Multidisciplinary Research & Review, 2(4), 444-450.

Chaitanya Krishna Suryadevara. (2020). GENERATING FREE IMAGES WITH OPENAI’S GENERATIVE MODELS. International Journal of Innovations in Engineering Research and Technology, 7(3), 49–56. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3653

Chaitanya Krishna Suryadevara. (2020). REAL-TIME FACE MASK DETECTION WITH COMPUTER VISION AND DEEP LEARNING: English. International Journal of Innovations in Engineering Research and Technology, 7(12), 254–259. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3184

Chaitanya Krishna Suryadevara. (2021). ENHANCING SAFETY: FACE MASK DETECTION USING COMPUTER VISION AND DEEP LEARNING. International Journal of Innovations in Engineering Research and Technology, 8(08), 224–229. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3672

Mallikarjunaradhya, V., & Pothukuchi, A. S. (2020). Leveraging AI for Predictive Migration Planning and Automated Data Transfer: Ensuring Optimal Cloud Resource Allocation and Data Integrity. Asian Journal of Multidisciplinary Research & Review, 1(2), 77-89.

Chaitanya Krishna Suryadevara, “TOWARDS PERSONALIZED HEALTHCARE - AN INTELLIGENT MEDICATION RECOMMENDATION SYSTEM”, IEJRD - International Multidisciplinary Journal, vol. 5, no. 9, p. 16, Dec. 2020.

Suryadevara, Chaitanya Krishna, Predictive Modeling for Student Performance: Harnessing Machine Learning to Forecast Academic Marks (December 22, 2018). International Journal of Research in Engineering and Applied Sciences (IJREAS), Vol. 8 Issue 12, December-2018, Available at SSRN: https://ssrn.com/abstract=4591990

Suryadevara, Chaitanya Krishna, Unveiling Urban Mobility Patterns: A Comprehensive Analysis of Uber (December 21, 2019). International Journal of Engineering, Science and Mathematics, Vol. 8 Issue 12, December 2019, Available at SSRN: https://ssrn.com/abstract=4591998

Chaitanya Krishna Suryadevara. (2019). A NEW WAY OF PREDICTING THE LOAN APPROVAL PROCESS USING ML TECHNIQUES. International Journal of Innovations in Engineering Research and Technology, 6(12), 38–48. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3654

Whig, P., & Ahmad, S. N. (2014). Simulation of linear dynamic macro model of photo catalytic sensor in SPICE. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 33(1/2), 611-629.


Refbacks

  • There are currently no refbacks.