Ethics of Data Science – Page 16

Big Tech’s Half-Hearted Response To Fake News And Election Hacking

Despite big hand waves, Facebook, Google, and Twitter aren’t doing enough to stop misinformation.

From slashdot I found a story about : Big Tech’s Half-Hearted Response To Fake News And Election Hacking. This Fast Company story talks about ways that social media companies are trying to prevent the misuse of their platforms as we head into the US midterms.

For Facebook, Google, and Twitter the fight against fake news seems to be two-pronged: De-incentivize the targeted content and provide avenues to correct factual inaccuracies. These are both surface fixes, however, akin to putting caulk on the Grand Canyon.

And, despite grand hand waves, both approaches are reactive. They don’t aim at understanding how this problem became prevalent, or creating a method that attacks the systemic issue. Instead these advertising giants implement new mechanisms by which people can report one-off issues—and by which the platforms will be left playing cat-and-mouse games against fake news—all the while giving no real clear glimpse into their opaque ad platforms.

The problem is that these companies make too much money from ads and elections are a chance to get lots of ads, manipulative or not. For that matter, what political ad doesn’t try to manipulate viewers?

The slashdot story was actually about Mozilla’s Responsible Computer Science Challenge which will support initiatives to embedd ethics in computer science courses. Alas, the efficacy of ethics courses is questionable. Aristotle would say that if you don’t have the disposition to be ethical no amount of training would do any good. It just helps the unethical pretend to be ethical.

Franken-algorithms: the deadly consequences of unpredictable code

The death of a woman hit by a self-driving car highlights an unfolding technological crisis, as code piled on code creates ‘a universe no one fully understands’

The Guardian has a good essay by Andrew Smith about Franken-algorithms: the deadly consequences of unpredictable code. The essay starts with the obvious problems of biased algorithms like those documented by Cathy O’Neil in Weapons of Math Destruction. It then goes further to talk about cases where algorithms are learning on the fly or are so complex that their behaviour becomes unpredictable. An example is high-frequency trading algorithms that trade on the stock market. These algorithmic traders try to outwit each other and learn which leads to unpredictable “flash crashes” when they go rogue.

The problem, he (George Dyson) tells me, is that we’re building systems that are beyond our intellectual means to control. We believe that if a system is deterministic (acting according to fixed rules, this being the definition of an algorithm) it is predictable – and that what is predictable can be controlled. Both assumptions turn out to be wrong.

The good news is that, according to one of the experts consulted this could lead to “a golden age for philosophy” as we try to sort out the ethics of these autonomous systems.

Re-Imagining Education In An Automating World conference at George Brown

On May 25th I had a chance to attend a gem of a conference organized the Philosophy of Education (POE) committee at George Brown. They organized a conference with different modalities from conversations to formal talks to group work. The topic was Re-Imagining Education in An Automating World (see my conference notes here) and this conference is a seed for a larger one next year.

I gave a talk on Digital Citizenship at the end of the day where I tried to convince people that:

Data analytics are now a matter of citizenship (we all need to understand how we are being manipulated).
We therefore need to teach data literacy in the arts and humanities, so that
Students are prepared to contribute to and critique the ways analytics are used deployed.
This can be done by integrating data and analytical components in any course using field-appropriate data.

Duplex shows Google failing at ethical and creative AI design

Google CEO Sundar Pichai milked the woos from a clappy, home-turf developer crowd at its I/O conference in Mountain View this week with a demo of an in-the-works voice assistant feature that will e…

A number of venues, including TechCruch have discussed the recent Google demonstration of an intelligent agent Duplex who can make appointments. Many of the stories note how Duplex shows Google failing at ethical and creative AI design. The problem is that the agent didn’t (at least during the demo) identify as a robot. Instead it appeared to deceive the person it was talking to. As the TechCrunch article points out, there is really no good reason to deceive if the purpose is to make an appointment.

What I want to know is what are the ethics of dealing with a robot? Do we need to identify as human to the robot? Do we need to be polite and give them the courtesy that we would a fellow human? Would it be OK for me to hang up as I do on recorded telemarketing calls? Most of us have developed habits of courtesy when dealing with people, including strangers, that the telemarketers take advantage of in their scripts. Will the robots now take advantage of that? Or, to be more precise, will those that use the robots to save their time take advantage of us?

A second question is how Google considers the ethical implications of their research? It is easy to castigate them for this demonstration, but the demonstration tells us nothing about a line of research that has been going on for a while and what processes Google may have in place to check the ethics of what they do. As companies explore the possibilities for AI, how are they to check their ethics in the excitement of achievement?

I should note that Google’s parent Alphabet has apparently dropped the “Don’t be evil” motto from their code of conduct. There has also been news about how a number of employees quit over a Google program to apply machine learning to drone footage for the military. This is after over 3000 Google employees signed a letter taking issue with the project. See also the Open Letter in Support of Google Employees and Tech Workers that researchers signed. As they say:

We are also deeply concerned about the possible integration of Google’s data on people’s everyday lives with military surveillance data, and its combined application to targeted killing. Google has moved into military work without subjecting itself to public debate or deliberation, either domestically or internationally. While Google regularly decides the future of technology without democratic public engagement, its entry into military technologies casts the problems of private control of information infrastructure into high relief.

The Ethics of Datafiction

Information Wants to Be Free, Or Does It? The Ethics of Datafication has just come out in the Electronic Book Review. This article was written with Bettina Berendt at KU Leuven and is about thinking about the ethics of digitization. The article first looks at the cliche phrase “information wants to be free” and then moves on to survey a number of arguments why some things should be digitized.

The Aggregate IQ Files, Part One: How a Political Engineering Firm Exposed Their Code Base

The Research Director for UpGuard, Chris Vickery (@VickerySec) has uncovered code repositories from AggregateIQ, the Canadian company that was building tools for/with SCL and Cambridge Analytica. See The Aggregate IQ Files, Part One: How a Political Engineering Firm Exposed Their Code Base and AggregateIQ Created Cambridge Analytica’s Election Software, and Here’s the Proof from Gizmodo.

The screenshots from the repository show on project called ephemeral with a description “Because there is no such thing as THE TRUTH”. The “Primary Data Storage” of Ephemeral is called “Mamba Jamba”, presumably a joke on “mumbo jumbo” which isn’t a good sign. What is mort interesting is the description (see image above) of the data storage system as “The Database of Truth”. Here is a selection of that description:

The Database of Truth is a database system that integrates, obtains, and normalizes data from disparate sources including starting with the RNC data trust. … This system will be created to make decisions based upon the data source and quality as to which data constitutes the accepted truth and connect via integrations or API to the source systems to acquire and update this data on a regular basis.

A robust front-end system will be built that allows an authrized user to query the Database of Truth to find data for a particular upcoming project, to see how current the data is, and to take a segment of that data and move it to the Escrow Database System. …

The Database of Truth is the Core source of data for the entire system. …

One wonders if there is a philosophical theory, of sorts, in Ephemeral. A theory where no truth is built on the mumbo jumbo of a database of truth(s).

Ephemeral would seem to be part of Project Ripon, the system that Cambridge Analytica never really delivered to the Cruz campaign. Perhaps the system was so ephemeral that it never worked and therefore the Database of Truth never held THE TRUTH. Ripon might be better called Ripoff.

Research Team Security

One of the researchers in the GamerGate Reactions team has created a fabulous set of recommendations for team members doing dangerous research. See Security_Recommendations_2018_v2.0. This document brings together in one place a lot of information and links on how to secure your identity and research. The researcher put this together in support of a panel that I am chairing this afternoon on Risky Research that is part of a day of panels/workshops following the Edward Snowden talk yesterday evening. (You can see my blog entry on Snowden’s talk here.) The key topics covered include:

Basic Security Measures
Use End-to-End Encryption for Communications  Encrypt Your Computer
Destroy All Information
Secure Browsing
Encrypt all Web Traffic
Avoiding Attacks
On Preventing Doxing
Dealing with Harassment

How Trump Consultants Exploited the Facebook Data of Millions

Cambridge Analytica harvested personal information from a huge swath of the electorate to develop techniques that were later used in the Trump campaign.

The New York Times has just published a story about How Trump Consultants Exploited the Facebook Data of Millions. The story is about how Cambridge Analytica, the US arm of SCL, a UK company, gathered a massive dataset from Facebook with which to do “psychometric modelling” in order to benefit Trump.

The Guardian has been reporting on Cambridge Analytica for some time – see their Cambridge Analytica Files. The service they are supposed to have provided with this massive dataset was to model types of people and their needs/desires/politics and then help political campaigns, like Trump’s, through microtargeting to influence voters. Using the models a campaign can create content tailored to these psychometrically modelled micro-groups to shift their opinions. (See articles by Paul-Olivier Dehaye about what Cambridge Analytica does and has.)

What is new is that there is a (Canadian) whistleblower from Cambridge Analytica, Christopher Wylie who was willing to talk to the Guardian and others. He is “the data nerd who came in from the cold” and he has a trove of documents that contradict what other said.

The Intercept has a earlier and related story about how Facebook Failed to Protect 30 Million Users From Having Their Data Harvested By Trump Campaign Affiliate. This tells how people were convinced to download a Facebook app that then took your data and that of their friends.

It is difficult to tell how effective the psychometric profiling with data is and if can really be used to sway voters. What is clear, however, is that Facebook is not really protecting their users’ data. To some extent their set up to monetize such psychometric data by convincing those who buy access to the data that you can use it to sway people. The problem is not that it can be done, but that Facebook didn’t get paid for this and are now getting bad press.