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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Big data ethics | 2/3 | https://en.wikipedia.org/wiki/Big_data_ethics | reference | science, encyclopedia | 2026-05-05T06:58:22.926497+00:00 | kb-cron |
=== Privacy === Privacy has been presented as a limitation to data usage which could also be considered unethical. For example, the sharing of healthcare data can shed light on the causes of diseases, the effects of treatments, an can allow for tailored analyses based on individuals' needs. This is of ethical significance in the big data ethics field because while many value privacy, the affordances of data sharing are also quite valuable, although they may contradict one's conception of privacy. Attitudes against data sharing may be based in a perceived loss of control over data and a fear of the exploitation of personal data. However, it is possible to extract the value of data without compromising privacy. Government surveillance of big data has the potential to undermine individual privacy by collecting and storing data on phone calls, internet activity, and geolocation, among other things. For example, the NSA's collection of metadata exposed in global surveillance disclosures raised concerns about whether privacy was adequately protected, even when the content of communications was not analyzed. The right to privacy is often complicated by legal frameworks that grant governments broad authority over data collection for "national security" purposes. In the United States, the Supreme Court has not recognized a general right to "informational privacy," or control over personal information, though legislators have addressed the issue selectively through specific statutes. From an equity perspective, government surveillance and privacy violations tend to disproportionately harm marginalized communities. Historically, activists involved in the Civil rights movement were frequently targets of government surveillance as they were perceived as subversive elements. Programs such as COINTELPRO exemplified this pattern, involving espionage against civil rights leaders. This pattern persists today, with evidence of ongoing surveillance of activists and organizations. Additionally, the use of algorithms by governments to act on data obtained without consent introduces significant concerns about algorithmic bias. Predictive policing tools, for example, utilize historical crime data to predict "risky" areas or individuals, but these tools have been shown to disproportionately target minority communities. One such tool, the COMPAS system, is a notable example; Black defendants are twice as likely to be misclassified as high risk compared to white defendants, and Hispanic defendants are similarly more likely to be classified as high risk than their white counterparts. Marginalized communities often lack the resources or education needed to challenge these privacy violations or protect their data from nonconsensual use. Furthermore, there is a psychological toll, known as the "chilling effect," where the constant awareness of being surveilled disproportionately impacts communities already facing societal discrimination. This effect can deter individuals from engaging in legal but potentially "risky" activities, such as protesting or seeking legal assistance, further limiting their freedoms and exacerbating existing inequities. Some scholars such as Jonathan H. King and Neil M. Richards are redefining the traditional meaning of privacy, and others to question whether or not privacy still exists. In a 2014 article for the Wake Forest Law Review, King and Richard argue that privacy in the digital age can be understood not in terms of secrecy but in term of regulations which govern and control the use of personal information. In the European Union, the right to be forgotten entitles EU countries to force the removal or de-linking of personal data from databases at an individual's request if the information is deemed irrelevant or out of date. According to Andrew Hoskins, this law demonstrates the moral panic of EU members over the perceived loss of privacy and the ability to govern personal data in the digital age. In the United States, citizens have the right to delete voluntarily submitted data. This is very different from the right to be forgotten because much of the data produced using big data technologies and platforms are not voluntarily submitted. While traditional notions of privacy are under scrutiny, different legal frameworks related to privacy in the EU and US demonstrate how countries are grappling with these concerns in the context of big data. For example, the "right to be forgotten" in the EU and the right to delete voluntarily submitted data in the US illustrate the varying approaches to privacy regulation in the digital age.
==== How much data is worth ==== The difference in value between the services facilitated by tech companies and the equity value of these tech companies is the difference in the exchange rate offered to the citizen and the "market rate" of the value of their data. Scientifically there are many holes in this rudimentary calculation: the financial figures of tax-evading companies are unreliable, either revenue or profit could be more appropriate, how a user is defined, a large number of individuals are needed for the data to be valuable, possible tiered prices for different people in different countries, etc. Although these calculations are crude, they serve to make the monetary value of data more tangible. Another approach is to find the data trading rates in the black market. RSA publishes a yearly cybersecurity shopping list that takes this approach. This raises the economic question of whether free tech services in exchange for personal data is a worthwhile implicit exchange for the consumer. In the personal data trading model, rather than companies selling data, an owner can sell their personal data and keep the profit.