Entries Tagged "data collection"

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Companies Not Saving Your Data

There’s a new trend in Silicon Valley startups; companies are not collecting and saving data on their customers:

In Silicon Valley, there’s a new emphasis on putting up barriers to government requests for data. The Apple-FBI case and its aftermath have tech firms racing to employ a variety of tools that would place customer information beyond the reach of a government-ordered search.

The trend is a striking reversal of a long-standing article of faith in the data-hungry tech industry, where companies including Google and the latest start-ups have predicated success on the ability to hoover up as much information as possible about consumers.

Now, some large tech firms are increasingly offering services to consumers that rely far less on collecting data. The sea change is even becoming evident among early-stage companies that see holding so much data as more of a liability than an asset, given the risk that cybercriminals or government investigators might come knocking.

Start-ups that once hesitated to invest in security are now repurposing limited resources to build technical systems to shed data, even if it hinders immediate growth.

The article also talks about companies providing customers with end-to-end encryption.

I believe that all this data isn’t nearly as valuable as the big-data people are promising. Now that companies are recognizing that it is also a liability, I think we’re going to see more rational trade-offs about what to keep — and for how long — and what to discard.

Posted on May 25, 2016 at 2:37 PMView Comments

White House Report on Big Data Discrimination

The White House has released a report on big-data discrimination. From the blog post:

Using case studies on credit lending, employment, higher education, and criminal justice, the report we are releasing today illustrates how big data techniques can be used to detect bias and prevent discrimination. It also demonstrates the risks involved, particularly how technologies can deliberately or inadvertently perpetuate, exacerbate, or mask discrimination.

The purpose of the report is not to offer remedies to the issues it raises, but rather to identify these issues and prompt conversation, research­ — and action­ — among technologists, academics, policy makers, and citizens, alike.

The report includes a number of recommendations for advancing work in this nascent field of data and ethics. These include investing in research, broadening and diversifying technical leadership, cross-training, and expanded literacy on data discrimination, bolstering accountability, and creating standards for use within both the government and the private sector. It also calls on computer and data science programs and professionals to promote fairness and opportunity as part of an overall commitment to the responsible and ethical use of data.

Posted on May 6, 2016 at 6:12 AMView Comments

Helen Nissenbaum on Regulating Data Collection and Use

NYU professor Helen Nissenbaum gave an excellent lecture at Brown University last month, where she rebutted those who think that we should not regulate data collection, only data use: something she calls “big data exceptionalism.” Basically, this is the idea that collecting the “haystack” isn’t the problem; it what is done with it that is. (I discuss this same topic in Data and Goliath, on pages 197-9.)

In her talk, she makes a very strong argument that the problem is one of domination. Contemporary political philosopher Philip Pettit has written extensively about a republican conception of liberty. He defines domination as the extent one person has the ability to interfere with the affairs of another.

Under this framework, the problem with wholesale data collection is not that it is used to curtail your freedom; the problem is that the collector has the power to curtail your freedom. Whether they use it or not, the fact that they have that power over us is itself a harm.

Posted on April 20, 2016 at 6:27 AMView Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.