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Published in:

2019 | OriginalPaper | Chapter

# 10. Applied Profiling: Uses, Reliability and Ethics

Author: Rita Singh

Published in:

Publisher: Springer Singapore

## Abstract

There are many uses of profiling. Currently, as represented by this book, the science of profiling is in its nascent stages. As it becomes more accurate, more uses for it will emerge. However, there is a dichotomy associated with this progression. While its increasing accuracy is likely to give rise to more applications, its potential to severely infringe on a person’s privacy through them will also rise. In the context of practical applications, two issues therefore become extremely important: whether the information generated through profiling is accurate or not, and whether it is relevant and ethical or not.
Footnotes
1
For instance, a sample of 100 people may have 20, 30 and 50 instances of short, medium height and tall people respectively.

2
If Eq. 10.50 $$\mu$$, the true mean of P(Y), instead of $$\bar{Y}$$, there would be N degrees of freedom and the denominator would be N.

Literature
1.
Singh, R., Jimenéz, A., & Öland, A. (2017). Voice disguise by mimicry: Deriving statistical articulometric evidence to evaluate claimed impersonation. IET Biometrics, 6(4), 282–289.
2.
Menn, L. (1983). Development of articulatory, phonetic, and phonological capabilities. Language Production, 2, 3–50.
3.
Giannelli, P. C. (1993). Daubert: Interpreting the federal rules of evidence. Cardozo Law Review, 15, 1999.
4.
Lally, S. J. (2003). What tests are acceptable for use in forensic evaluations? A survey of experts. Professional Psychology: Research and Practice, 34(5), 491. CrossRef
5.
Hamilton, H. G. (1997). The movement from Frye to Daubert: Where do the states stand. Jurimetrics, 38, 201.
6.
Cisse, M. M., Adi, Y., Neverova, N., & Keshet, J. (2017). Houdini: Fooling deep structured visual and speech recognition models with adversarial examples. In Proceedings of the Thirty-First Annual Conference on Neural Information Processing Systems (NIPS) (pp. 6977–6987). Long Beach, California.
7.
Kreuk, F., Adi, Y., Cisse, M., & Keshet, J. (2018). Fooling end-to-end speaker verification with adversarial examples. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1962–1966). Calgary, Canada: IEEE.
8.
Brendel, W., Rauber, J., & Bethge, M. (2017). Decision-based adversarial attacks: Reliable attacks against black-box machine learning models. arXiv:​1712.​04248.
9.
Xia, L. (2018). China focus: Technologies at summer Davos offer a glimpse into future, News Article, XinhuaNet, China, 20 Sept 2018.
10.
Wonnacott, T. H., & Wonnacott, R. J. (1990). Introductory statistics (Vol. 5). New York: Wiley.
11.
Spiegel, M. R., Schiller, J. J., & Srinivasan, R. (2013). Probability and statistics. New York: McGraw-Hill.
12.
Wasserman, L. (2013). All of statistics: A concise course in statistical inference. Berlin: Springer Science & Business Media.
13.
Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica: Journal of the Econometric Society, 57, 307–333.
14.
Garside, G. R., & Mack, C. (1976). Actual type 1 error probabilities for various tests in the homogeneity case of the 2 $$\times$$ 2 contingency table. The American Statistician, 30(1), 18–21.
15.
Chen, P. N. (1996). General formulas for the Neyman-Pearson type-II error exponent subject to fixed and exponential type-I error bounds. IEEE Transactions on Information Theory, 42(1), 316–323.
16.
Lehmann, E. L. (1993). The Fisher, Neyman-Pearson theories of testing hypotheses: One theory or two? Journal of the American Statistical Association, 88(424), 1242–1249.
17.
Self, S. G., & Liang, K. Y. (1987). Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. Journal of the American Statistical Association, 82(398), 605–610.
18.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155.
19.
Donner, A., & Eliasziw, M. (1987). Sample size requirements for reliability studies. Statistics in Medicine, 6(4), 441–448.
20.
Olson, C. L. (1976). On choosing a test statistic in multivariate analysis of variance. Psychological Bulletin, 83(4), 579. CrossRef
21.
Pearson, K. (1896). Mathematical contributions to the theory of evolution III. Regression, heredity, and panmixia. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 187, 253–318. CrossRef
22.
Kendall, M. G. (1955). Rank correlation methods. Oxford, England: Griffin Publishers.
23.
Siegal, S. (1956). Nonparametric statistics for the behavioral sciences. New York: McGraw-Hill.
24.
Siegel, S. C., & Castellan, N. J., Jr. (1988). Nonparametric statistics for the behavioural sciences. New York: McGraw-Hill.
25.
Abramowitz, M., & Stegun, I. A. (1965). Handbook of mathematical functions: With formulas, graphs, and mathematical tables (Vol. 55). USA: Courier Corporation.
26.
Johnson, N. L., Kotz, S., & Balakrishnan, N. (1994). Chi-squared distributions including chi and Rayleigh. Continuous univariate distributions (2nd ed., Vol. 1, pp. 415–493). New York: Wiley.
27.
Mood, A. M., Graybill, F. A., & Boes, D. (1974). Introduction to the theory of statistics (3rd ed., pp. 241–246). USA: McGraw-Hill.
28.
Beyer, W. H. (2018). Handbook of mathematical science. USA: CRC Press.
29.
Kres, H. (2012). Statistical tables for multivariate analysis: A handbook with references to applications. Berlin: Springer Science & Business Media.
30.
Krishnamoorthy, K. (2006). Handbook of statistical distributions with applications. USA: Chapman and Hall/CRC Press.
31.
Simon, M. K. (2007). Probability distributions involving Gaussian random variables: A handbook for engineers and scientists. Berlin: Springer Science & Business Media.
32.
Patnaik, P. B. (1949). The non-central $$\chi ^2$$ and F-distribution and their applications. Biometrika, 36(1/2), 202–232.
33.
Rohlf, F. J., & Sokal, R. R. (1995). Statistical tables. USA: Macmillan.
34.
O’brien, R. G., & Kaiser, M. K. (1985). MANOVA method for analyzing repeated measures designs: An extensive primer. Psychological Bulletin, 97(2), 316.
35.
Huberty, C. J., & Olejnik, S. (2006). Applied MANOVA and discriminant analysis (Vol. 498). Hoboken, New Jersey: Wiley.
36.
Davis, C. S. (2002). Statistical methods for the analysis of repeated measurements. Berlin: Springer Science & Business Media.
37.
Vonesh, E., & Chinchilli, V. M. (1996). Linear and nonlinear models for the analysis of repeated measurements. Boca Raton, Florida: CRC Press.
38.
Jones, B., & Kenward, M. G. (2014). Design and analysis of cross-over trials. Monographs on statistics and applied probability. USA: Chapman & Hall, CRC Press.
39.
Tufekci, Z. (2015). Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency. Colorado Technology Law Journal, 13, 203.
40.
Kreuk, F., Adi, Y., Cisse, M., & Keshet, J. (2018). Fooling end-to-end speaker verification by adversarial examples. arXiv:​1801.​03339.
41.
Adi, Y., Baum, C., Cisse, M., Pinkas, B., & Keshet, J. (2018). Turning your weakness into a strength: Watermarking deep neural networks by backdooring. In Proceedings of the Twenty-Seventh Security Symposium (USENIX) (pp. 1615–1631). Baltimore, USA.
42.
Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. The Cambridge handbook of artificial intelligence, 1, 316–334.
43.
Rabin, M. (1993). Incorporating fairness into game theory and economics. The American Economic Review, 83, 1281–1302.
44.
Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. The Quarterly Journal of Economics, 114(3), 817–868.
45.
Haussler, D. (1988). Quantifying inductive bias: AI learning algorithms and Valiant’s learning framework. Artificial Intelligence, 36(2), 177–221.
46.
Bozdag, E. (2013). Bias in algorithmic filtering and personalization. Ethics and Information Technology, 15(3), 209–227. CrossRef
47.
Edelman, B. (2011). Bias in search results: Diagnosis and response. Indian Journal of Technology, 7, 16.
48.
Tommasi, T., Patricia, N., Caputo, B., & Tuytelaars, T. (2017). A deeper look at dataset bias. Domain adaptation in computer vision applications (pp. 37–55). Cham: Springer.
49.
Ananny, M. (2016). Toward an ethics of algorithms: Convening, observation, probability, and timeliness. Science, Technology, & Human Values, 41(1), 93–117. CrossRef
50.
Kraemer, F., Van Overveld, K., & Peterson, M. (2011). Is there an ethics of algorithms? Ethics and Information Technology, 13(3), 251–260.
51.
Huang, J., Gretton, A., Borgwardt, K., Schölkopf, B., & Smola, A. J. (2007). Correcting sample selection bias by unlabeled data. In Advances in Neural Information Processing Systems (pp. 601–608).
52.
Jiménez, A., & Raj, B. (2017). Privacy preserving distance computation using somewhat-trusted third parties. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6399–6403). New Orleans, Louisiana, USA: IEEE.
53.
Pathak, M. A., Raj, B., Rane, S. D., & Smaragdis, P. (2013). Privacy-preserving speech processing: Cryptographic and string-matching frameworks show promise. IEEE Signal Processing Magazine, 30(2), 62–74.
54.
Pathak, M. A., & Raj, B. (2011). Privacy preserving speaker verification using adapted GMMs. In Proceedings of the Twelfth Annual Conference of the International Speech Communication Association, Florence, Italy.
55.
Buchanan, E. A. (1999). An overview of information ethics issues in a world-wide context. Ethics and Information Technology, 1(3), 193–201. CrossRef
56.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. Management Information Systems (MIS) Quarterly, 36, 1165–1188.
57.
Lipinski, T. A., & Britz, J. (2000). Rethinking the ownership of information in the 21st century: Ethical implications. Ethics and Information Technology, 2(1), 49–71.
58.
Galton, F. (1888). Co-relations and their measurement, chiefly from anthropometric data. Proceedings of the Royal Society of London, 45(273–279), 135–145.