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Information and examples on data mining and ethics

 

In this excerpt from Data Mining: Know it All find out about the data mining and ethics, including the ethical issues that data mining presents and find other relevant resources and data mining examples from the book.

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Table of contents:

An introduction to data mining
Simple data mining examples and datasets
Fielded applications of data mining and machine learning
The difference between machine learning and statistics in data mining
Information and examples on data mining and ethics
Data acquisition and integration techniques
What is a data rollup?
Calculating mode in data mining projects
Using data merging and concatenation techniques to integrate data

 

1.6 Data Mining and Ethics

The use of data — particularly data about people — for data mining has serious ethical implications, and practitioners of data mining techniques must act responsibly by making themselves aware of the ethical issues that surround their particular application.

When applied to people, data mining is frequently used to discriminate — who gets the loan, who gets the special offer, and so on. Certain kinds of discrimination — racial, sexual, religious, and so on — are not only unethical but also illegal. However, the situation is complex: everything depends on the application. Using sexual and racial information for medical diagnosis is certainly ethical, but using the same information when mining loan payment behavior is not. Even when sensitive information is discarded, there is a risk that models will be built that rely on variables that can be shown to substitute for racial or sexual characteristics. For example, people frequently live in areas that are associated with particular ethnic identities, so using an area code in a data mining study runs the risk of building models that are based on race — even though racial information has been explicitly excluded from the data.

It is widely accepted that before people make a decision to provide personal information they need to know how it will be used and what it will be used for, what steps will be taken to protect its confidentiality and integrity, what the consequences of supplying or withholding the information are, and any rights of redress they may have. Whenever such information is collected, individuals should be told these things — not in legalistic small print but straightforwardly in plain language they can understand.

The potential use of data mining techniques means that the ways in which a repository of data can be used may stretch far beyond what was conceived when the data was originally collected. This creates a serious problem: it is necessary to determine the conditions under which the data was collected and for what purposes it may be used. Does the ownership of data bestow the right to use it in ways other than those purported when it was originally recorded? Clearly in the case of explicitly collected personal data it does not. But in general the situation is complex.

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Printed with permission from Morgan Kaufmann, a division of Elsevier. Copyright 2009. Data Mining: Know It All by Chakrabarti et all. For more information about this title and other similar books, please visit www.elsevierdirect.com.

 

 Surprising results emerge from data mining. For example, it has been reported that one of the leading consumer groups in France has found that people with red cars are more likely to default on their car loans. What is the status of such a "discovery"? What information is it based on? Under what conditions was that information collected? In what ways is it ethical to use it? Clearly, insurance companies are in the business of discriminating among people based on stereotypes — young males pay heavily for automobile insurance — but such stereotypes are not based solely on statistical correlations; they also involve commonsense knowledge about the world. Whether the preceding finding says something about the kind of person who chooses a red car, or whether it should be discarded as an irrelevancy is a matter for human judgment based on knowledge of the world, rather than on purely statistical criteria.

When presented with data, you need to ask who is permitted to have access to it, for what purpose it was collected, and what kind of conclusions is it legitimate to draw from it. The ethical dimension raises tough questions for those involved in practical data mining. It is necessary to consider the norms of the community that is used to dealing with the kind of data involved, standards that may have evolved over decades or centuries but ones that the information specialist may not know. For example, did you know that in the library community, it is taken for granted that the privacy of readers is a right that is jealously protected? If you call your university library and ask who has such-and-such a textbook out on loan, they will not tell you. This prevents a student from being subjected to pressure from an irate professor to yield access to a book that she desperately needs for her latest grant application. It also prohibits inquiry into the dubious recreational reading tastes of the university ethics committee chairperson. Those who build, say, digital libraries may not be aware of these sensitivities and might incorporate data mining systems that analyze and compare individuals ' reading habits to recommend new books — perhaps even selling the results to publishers!

In addition to community standards for the use of data, logical and scientific standards must be adhered to when drawing conclusions from it. If you do come up with conclusions (such as red car owners being greater credit risks), you need to attach caveats to them and back them up with arguments other than purely statistical ones. The point is that data mining is just a tool in the whole process: It is people who take the results, along with other knowledge, and decide what action to apply.

Data mining prompts another question, which is really a political one: To what use are society's resources being put? We mentioned previously the application of data mining to basket analysis, where supermarket checkout records are analyzed to detect associations among items that people purchase. What use should be made of the resulting information? Should the supermarket manager place the beer and chips together, to make it easier for shoppers, or farther apart, making it less convenient for them, maximizing their time in the store, and therefore increasing their likelihood of being drawn into unplanned further purchases? Should the manager move the most expensive, most profitable diapers near the beer, increasing sales to harried fathers of a high-margin item and add further luxury baby products nearby?

Of course, those who use advanced technologies should consider the wisdom of what they are doing. If data is characterized as recorded facts, then information is the set of patterns, or expectations, that underlie the data. You could go on to define knowledge as the accumulation of your set of expectations and wisdom as the value attached to knowledge. Although we will not pursue it further here, this issue is worth pondering.

As we saw at the very beginning of this chapter, the techniques described in this book may be called on to help make some of the most profound and intimate decisions that life presents. Data mining is a technology that we need to take seriously.

1.7 Resources

This section describes papers, books, and other resources relevant to the material covered in this chapter. The human in vitro fertilization research mentioned in the opening of this chapter was undertaken by the Oxford University Computing Laboratory, and the research on cow culling was performed in the Computer Science Department at the University of Waikato, New Zealand.

The example of the weather problem is from Quinlan (1986) and has been widely used to explain machine learning schemes. The corpus of example problems mentioned in the introduction to Section 1.2 is available from Blake et al. (1998). The contact lens example is from Cendrowska (1998), who introduced the PRISM rule-learning algorithm. The iris dataset was described in a classic early paper on statistical inference (Fisher 1936). The labor negotiations data is from the Collective Bargaining Review, a publication of Labour Canada issued by the Industrial Relations Information Service (BLI 1988), and the soybean problem was first described by Michalski and Chilausky (1980).

Some of the applications in Section 1.3 are covered in an excellent paper that gives plenty of other applications of machine learning and rule induction (Langley & Simon 1995); another source of fielded applications is a special issue of the Machine Learning Journal (Kohavi & Provost 1998). The loan company application is described in more detail by Michie (1989), the oil slick detector is from Kubat et al. (1998), the electric load forecasting work is by Jabbour et al. (1988), and the application to preventative maintenance of electromechanical devices is from Saitta and Neri (1998). Fuller descriptions of some of the other projects mentioned in Section 1.3 (including the figures of dollars saved and related literature references) appear at the websites of the Alberta Ingenuity Centre for Machine Learning and MLnet, a European network for machine learning.

The book Classification and Regression Trees mentioned in Section 1.4 is by Breiman et al. (1984), and the independently derived but similar scheme of Quinlan was described in a series of papers that eventually led to a book (Quinlan 1993).

The first book on data mining appeared in 1991 (Piatetsky-Shapiro & Frawley 1991), a collection of papers presented at a workshop on knowledge discovery in databases in the late 1980s. Another book from the same stable has appeared since (Fayyad et al. 1996) from a 1994 workshop. There followed a rash of business-oriented books on data mining, focusing mainly on practical aspects of how it can be put into practice with only superficial descriptions of the technology that underlies the methods used. They are valuable sources of applications and inspiration. For example, Adriaans and Zantige (1996) from Syllogic, a European systems and database consultancy, provide an early introduction to data mining. Berry and Linoff (1997), from a Pennsylvania-based company specializing in data warehousing and data mining, give an excellent and example-studded review of data mining techniques for marketing, sales, and customer support. The work of Cabena et al. (1998), written by people from five international IBM laboratories, presents an overview the data mining process with many examples of real-world applications. Dhar and Stein (1997) give a business perspective on data mining and include broad-brush, popularized reviews of many of the technologies involved. Groth (1998), working for a provider of data mining software, gives a brief introduction to data mining and then a fairly extensive review of data mining software products; the book includes a CD containing a demo version of his company' s product. Weiss and Indurkhya (1998) look at a wide variety of statistical techniques for making predictions from what they call "big data." Han and Kamber (2001) cover data mining from a database perspective, focusing on the discovery of knowledge in large corporate databases. Finally, Hand et al. (2001) produced an interdisciplinary book on data mining from an international group of authors who are well respected in the field.

Books on machine learning, on the other hand, tend to be academic texts suited for use in university courses rather than practical guides. Mitchell (1997) wrote an excellent book that covers many techniques of machine learning, including some — notably genetic algorithms and reinforcement learning — that are not covered here. Langley (1996) offers another good text. Although the previously mentioned book by Quinlan (1993) concentrates on a particular learning algorithm, C4.5, it is a good introduction to some of the problems and techniques of machine learning. An excellent book on machine learning from a statistical perspective is from Hastie et al. (2001). This is a theoretically oriented work and is beautifully produced with apt and telling illustrations.

Pattern recognition is a topic that is closely related to machine learning, and many of the same techniques apply. Duda et al. (2001) offer the second edition of a classic and successful book on pattern recognition (Duda & Hart 1973). Ripley (1996) and Bishop (1995) describe the use of neural networks for pattern recognition. Data mining with neural networks is the subject of a book by Bigus (1996) of IBM, which features the IBM Neural Network Utility Product that he developed.

There is a great deal of current interest in support vector machines. Cristianini and Shawe-Taylor (2000) give a nice introduction, and a follow-up work generalizes this to cover additional algorithms, kernels, and solutions with applications to pattern discovery problems in fi elds such as bioinformatics, text analysis, and image analysis (Shawe-Taylor & Cristianini 2004). Schölkopf and Smola (2002) provide a comprehensive introduction to support vector machines and related kernel methods by two young researchers who did their PhD research in this rapidly developing area.

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This was first published in May 2009

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