Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

The Ethical Data Scientist by Cathy O’Neil.

From the post:

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After the financial crisis, there was a short-lived moment of opportunity to accept responsibility for mistakes with the financial community. One of the more promising pushes in this direction was when quant and writer Emanuel Derman and his colleague Paul Wilmott wrote the Modeler’s Hippocratic Oath, which nicely sums up the list of responsibilities any modeler should be aware of upon taking on the job title.

The ethical data scientist would strive to improve the world, not repeat it. That would mean deploying tools to explicitly construct fair processes. As long as our world is not perfect, and as long as data is being collected on that world, we will not be building models that are improvements on our past unless we specifically set out to do so.

At the very least it would require us to build an auditing system for algorithms. This would be not unlike the modern sociological experiment in which job applications sent to various workplaces differ only by the race of the applicant—are black job seekers unfairly turned away? That same kind of experiment can be done directly to algorithms; see the work of Latanya Sweeney, who ran experiments to look into possible racist Google ad results. It can even be done transparently and repeatedly, and in this way the algorithm itself can be tested.

The ethics around algorithms is a topic that lives only partly in a technical realm, of course. A data scientist doesn’t have to be an expert on the social impact of algorithms; instead, she should see herself as a facilitator of ethical conversations and a translator of the resulting ethical decisions into formal code. In other words, she wouldn’t make all the ethical choices herself, but rather raise the questions with a larger and hopefully receptive group.
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First, the link for the Modeler’s Hippocratic Oath takes you to a splash page at Wiley for Derman’s book: My Life as a Quant: Reflections on Physics and Finance.

The Financial Modelers’ Manifesto (PDF) and The Financial Modelers’ Manifesto (HTML), are valid links as of today.

I commend the entire text of The Financial Modelers’ Manifesto to you for repeated reading but for present purposes, let’s look at the Modelers’ Hippocratic Oath:

~ I will remember that I didn’t make the world, and it doesn’t satisfy my equations.

~ Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.

~ I will never sacrifice reality for elegance without explaining why I have done so.

~ Nor will I give the people who use my model false comfort about its accuracy. Instead, I will make explicit its assumptions and oversights.

~ I understand that my work may have enormous effects on society and the economy, many of them beyond my comprehension

It may just be me but I don’t see a charge being laid on data scientists to be the ethical voices in organizations using data science.

Do you see that charge?

To to put it more positively, aren’t other members of the organization, accountants, engineers, lawyers, managers, etc., all equally responsible for spurring “ethical conversations?” Why is this a peculiar responsibility for data scientists?

I take a legal ethics view of the employer – employee/consultant relationship. The client is the ultimate arbiter of the goal and means of a project, once advised of their options.

Their choice may or may not be mine but I haven’t ever been hired to play the role of Jiminy Cricket.

It’s heady stuff to be responsible for bringing ethical insights to the clueless but sometimes the clueless have ethical insights on their on, or not.

Data scientists can and should raise ethical concerns but no more or less than any other member of a project.

As you can tell from reading this blog, I have very strong opinions on a wide variety of subjects. That said, unless a client hires me to promote those opinions, the goals of the client, by any legal means, are my only concern.

PS: Before you ask, no, I would not work for Donald Trump. But that’s not an ethical decision. That’s simply being a good citizen of the world.

The usual ethics example for data science involves discrimination against some protected class. Discrimination on race, religion, ethnicity, etc., most if not all of which is already illegal.

That’s not a question of ethics, that’s a question of staying out of jail.

A better ethics example is to ask: Who’s lying to Hillary Clinton about back doors for encryption?

I ask because in the debate on December 19, 2015, Hillary says:

Secretary Clinton, I want to talk about a new terrorist tool used in the Paris attacks, encryption. FBI Director James Comey says terrorists can hold secret communications which law enforcement cannot get to, even with a court order.

You’ve talked a lot about bringing tech leaders and government officials together, but Apple CEO Tim Cook said removing encryption tools from our products altogether would only hurt law-abiding citizens who rely on us to protect their data. So would you force him to give law enforcement a key to encrypted technology by making it law?

CLINTON: I would not want to go to that point. I would hope that, given the extraordinary capacities that the tech community has and the legitimate needs and questions from law enforcement, that there could be a Manhattan-like project, something that would bring the government and the tech communities together to see they’re not adversaries, they’ve got to be partners.

It doesn’t do anybody any good if terrorists can move toward encrypted communication that no law enforcement agency can break into before or after. There must be some way. I don’t know enough about the technology, Martha, to be able to say what it is, but I have a lot of confidence in our tech experts.

And maybe the back door is the wrong door, and I understand what Apple and others are saying about that. But I also understand, when a law enforcement official charged with the responsibility of preventing attacks — to go back to our early questions, how do we prevent attacks — well, if we can’t know what someone is planning, we are going to have to rely on the neighbor or, you know, the member of the mosque or the teacher, somebody to see something.

CLINTON: I just think there’s got to be a way, and I would hope that our tech companies would work with government to figure that out. Otherwise, law enforcement is blind — blind before, blind during, and, unfortunately, in many instances, blind after.

So we always have to balance liberty and security, privacy and safety, but I know that law enforcement needs the tools to keep us safe. And that’s what i hope, there can be some understanding and cooperation to achieve.
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Who do you think has told Secretary Clinton there is a way to have secure encryption and at the same time enable law enforcement access to encrypted data?

That would be a data scientist or someone posing as a data scientist. Yes?

I assume you have read: Keys Under Doormats: Mandating Insecurity by Requiring Government Access to All Data and Communications by H. Abelson, R. Anderson, S. M. Bellovin, J. Benaloh, M. Blaze, W. Diffie, J. Gilmore, M. Green, S. Landau, P. G. Neumann, R. L. Rivest, J. I. Schiller, B. Schneier, M. Specter, D. J. Weitzner.

Abstract:

Twenty years ago, law enforcement organizations lobbied to require data and communication services to engineer their products to guarantee law enforcement access to all data. After lengthy debate and vigorous predictions of enforcement channels “going dark,” these attempts to regulate security technologies on the emerging Internet were abandoned. In the intervening years, innovation on the Internet flourished, and law enforcement agencies found new and more effective means of accessing vastly larger quantities of data. Today, there are again calls for regulation to mandate the provision of exceptional access mechanisms. In this article, a group of computer scientists and security experts, many of whom participated in a 1997 study of these same topics, has convened to explore the likely effects of imposing extraordinary access mandates.

We have found that the damage that could be caused by law enforcement exceptional access requirements would be even greater today than it would have been 20 years ago. In the wake of the growing economic and social cost of the fundamental insecurity of today’s Internet environment, any proposals that alter the security dynamics online should be approached with caution. Exceptional access would force Internet system developers to reverse “forward secrecy” design practices that seek to minimize the impact on user privacy when systems are breached. The complexity of today’s Internet environment, with millions of apps and globally connected services, means that new law enforcement requirements are likely to introduce unanticipated, hard to detect security flaws. Beyond these and other technical vulnerabilities, the prospect of globally deployed exceptional access systems raises difficult problems about how such an environment would be governed and how to ensure that such systems would respect human rights and the rule of law.

Whether you agree on policy grounds about back doors to encryption or not, is there any factual doubt that back doors to encryption leave users insecure?

That’s an important point because Hillary’s data science advisers should have clued her in that her position is factually false. With or without a “Manhattan Project.”

Here are the ethical questions with regard to Hillary’s position on back doors for encryption:

1. Did Hillary’s data scientist(s) tell her that access by the government to encrypted data means no security for users?
2. What ethical obligations do data scientists have to advise public office holders or candidates that their positions are at variance with known facts?
3. What ethical obligations do data scientists have to caution their clients when they persist in spreading mis-information, in this case about encryption?
4. What ethical obligations do data scientists have to expose their reports to a client outlining why the client’s public position is factually false?

Many people will differ on the policy question of access to encrypted data but that access to encrypted data weakens the protection for all users is beyond reasonable doubt.

If data scientists want to debate ethics, at least make it about an issue with consequences. Especially for the data scientists.

Questions with no risk aren’t ethics questions, they are parlor entertainment games.

PS: Is there an ethical data scientist in the Hillary Clinton campaign?