Abstract
Learning algorithms have become the basis of decision making and the modern tool of assessment in all spares of human endeavours. Consequently, several competing arguments about the reliability of learning algorithm remain at AI global debate due to concerns about arguable algorithm biases such as data inclusiveness bias, homogeneity assumption in data structuring, coding bias etc., resulting from human imposed bias, and variance among many others. Recent pieces of evidence (computer vision - misclassification of people of colour, face recognition, among many others) have shown that there is indeed a need for concerns. Evidence suggests that algorithm bias typically can be introduced to learning algorithm during the assemblage of a dataset; such as how the data is collected, digitized, structured, adapted, and entered into a database according to human-designed cataloguing criteria. Therefore, addressing algorithm fairness, bias and variance in artificial intelligence imply addressing the training set bias. We propose a framework of data inclusiveness, participation and reciprocity.
Supervisor: Prof. Olusanya E. Olubusoye.
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References
Cawley, G., Talbot, N.: On over-fitting in model selection & subsequent selection bias in performance evaluation. J. Mach. Learn. Res. 11, 2079–2107 (2010)
Nadkarni, P.: Core technologies: machine learning and natural language processing. In: Clinical Research Computing (2016)
Alake, R.: Algorithm Bias in Artificial Intelligence Needs to be Discussed (And addressed). https://towardsdatascience.com/algorithm-bias-in-artificial-intelligence-needs-to-be-discussed-and-addressed-8d369d675a70. Accessed 20 May 2020
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn., no. 5. MIT Press, Cambridge (2009)
Weizenbaum, J.: Computer Power and Human Reason: From Judgment to Calculation. W.H. Freeman and Company, San Francisco (1976)
Diakopoulos, N.: Enabling accountability of algorithmic media: transparency as a constructive and critical lens. In: Cerquitelli, T., Quercia, D., Pasquale, F. (eds.) Transparent Data Mining for Big and Small Data. SBD, vol. 11, pp. 25–43. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54024-5_2
Gillespie, T., Boczkowski, P., Foot, K.: Media Technologies, pp. 1–30. MIT Press, Cambridge (2014)
Kohavi, R., Wolpert, D.H.: Bias Plus Variance Decomposition for Zero-One Loss Function. In: ICML (1996)
Luxburg, U.V., Schölkopf, B.: Statistical Learning Theory: Models, Concepts, and Results. Handbook of the History of Logic (2011)
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Akintande, O.J. (2021). Algorithm Fairness Through Data Inclusion, Participation, and Reciprocity. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_50
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DOI: https://doi.org/10.1007/978-3-030-73200-4_50
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