Ethical Issues in Data Mining

Ethical Issues in Data Mining

Mandana Mir Moftakhari

PhD Student at Hacettepe University, Department of Information Management. Email: [email protected]

Güleda Doğan

PhD Student & Research Assistant of Hacettepe University Department of Information Management. Email: [email protected]

We will discuss about:

 Big Data

 _{Knowledge Discovery}

 Data mining

Big Data !

 Data overload is a serious problem that has

been grown by technical advances.

 Human beings have to cope with such

overwhelming amounts of data and manage it in

order to obtain relevant information and

Big Data !

Organizations have to overcome with massive data volume to achieve opportunities for:

 _{better decision-making}

knowledge Discovery in Databases

as a Solution

knowledge discovery in databases (KDD) is “the nontrivial process of identifying

 valid

 novel

 potentially useful and

 ultimately understandable patterns in data”

KDD Involves Different Steps

What Is Data Mining?

Data mining as the center process of KDD is

“the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner.”

Data mining Using Areas

 Customer service support

 Prediction

 Estimation

 Forecasting

 Decision support

Data Mining Process

 Identifying the aim areas

 _{Determining sources of data}

 Gathering and cleaning the data into a data

warehouse

 Choosing proper analyzing tools

 Finding new patterns

 Prepare reports and implementing the

Ethical issues in Data mining

 _{Individuals not only expect qualified services,}

but also they require high level privacy and security of their personal details.

 These issues cannot be overlooked because of

their consequences and effects on consumers, individuals and society.

Ethical issues

 _Privacy

 Data accuracy

 _{Database security}

Privacy Threats

Many consumers feel that their privacy is violated by information-gathering practices.

 _{Secondary use of the personal information}

 Handling misinformation

Secondary Use of the Personal

Information

Recent surveys on privacy show a great concern about the use of personal data for purposes other than the one for which data has been collected.

Handling Misinformation

Misinformation can cause serious and long-term damage, so individuals should be able challenge the correctness of data about themselves.

Granulated Access to Personal

Information

The access to personal data should be on a need-to-know basis, and limited to relevant information only.

Type of data

Some types of personal information are seen as being more sensitive than others.

What complicates this issue is that sensitivity level varies according to the individual.

Database security

Database security inhibits the unauthorized dissemination of personal data.

Data accuracy

Collected data have originated from many diverse, possibly external, sources.

 _{Might be noisy, obsolete, inaccurate, or}

incomplete

 Not enough new

 Different from the present situation of

Consent

The purpose of data mining is to discover new insights and new uses for the information that companies already have.

This makes it nearly impossible to allow the consumer to have the right of giving informed consent for each use of his data.

Conclusion and Recommendations

Data mining is the process of searching in order to discover relationships between data sets and find useful information. Ethical issues should be observed in all steps of the process.

Conclusion and Recommendations

 Consider the expectations of the customers

 _{Develop a customer-oriented privacy policy}

 Research and understand all laws that may

have jurisdiction over sensitive data

 Control access to data warehouses

 Give customers more control over their data

References

 American Library Association. (1995). Code of ethics of the American Library Association.

 Bhambri, V. &Gagandeep, (2012).Coexistence of data mining and privacy of data.International Journal of

Research in IT & Management,2(2).

 Brankovic, L., &Estivill-Castro, V. (1999, July). Privacy issues in knowledge discovery and data mining. In Australian institute of computer ethics conference. 89-99.

 Cary, C., Wen, H.J. &Mahatanankoon, P. (2003).Data mining: consumer privacy, ethical policy, and systems development practices. Human Systems Management, 22(4), 157–168.

 Cavoukian, A. (1998). Data mining: staking a claim on your privacy.

 Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P. &Uthurusamy, R.. (1996). Advances in knowledge discovery

and data mining. Cambridge, Menlo Park, Calif.: AAAI Press.Retrieved on May ,10, 2014, from

http://www.amazon.ca/gp/product/0262560976

 Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases.AI Magazine, 17, 37–54.

 Nicholson, S. (2003). The bibliomining process: Data warehousing and data mining for library decision-making.

Information Technology and Libraries, 22(4), 146-151.

 Nicholson, S. & Stanton, J. (2003).Gaining strategic advantage through bibliomining: Data mining for management decisions in corporate, special, digital, and traditional libraries.In Nemati, H. &Barko, C. (Eds.).Organizational data mining: Leveraging enterprise data resources for optimal performance. Hershey, PA: Idea Group Publishing. 247-262.

 Payne, D. &. Trumbach, C. C. (2009). Data mining: proprietary rights, people and proposals, Business Ethics

Quarterly, vol.

 18(3).