Experiments on Trial

Hannah Fry
The New Yorker
Originally posted 24 Feb 20

Here are two excerpts:

There are also times when manipulation leaves people feeling cheated. For instance, in 2018 the Wall Street Journal reported that Amazon had been inserting sponsored products in its consumers’ baby registries. “The ads look identical to the rest of the listed products in the registry, except for a small gray ‘Sponsored’ tag,” the Journal revealed. “Unsuspecting friends and family clicked on the ads and purchased the items,” assuming they’d been chosen by the expectant parents. Amazon’s explanation when confronted? “We’re constantly experimenting,” a spokesperson said. (The company has since ended the practice.)

But there are times when the experiments go further still, leaving some to question whether they should be allowed at all. There was a notorious experiment run by Facebook in 2012, in which the number of positive and negative posts in six hundred and eighty-nine thousand users’ news feeds was tweaked. The aim was to see how the unwitting participants would react. As it turned out, those who saw less negative content in their feeds went on to post more positive stuff themselves, while those who had positive posts hidden from their feeds used more negative words.

A public backlash followed; people were upset to discover that their emotions had been manipulated. Luca and Bazerman argue that this response was largely misguided. They point out that the effect was small. A person exposed to the negative news feed “ended up writing about four additional negative words out of every 10,000,” they note. Besides, they say, “advertisers and other groups manipulate consumers’ emotions all the time to suit their purposes. If you’ve ever read a Hallmark card, attended a football game or seen a commercial for the ASPCA, you’ve been exposed to the myriad ways in which products and services influence consumers’ emotions.”

(cut)

Medicine has already been through this. In the early twentieth century, without a set of ground rules on how people should be studied, medical experimentation was like the Wild West. Alongside a great deal of good work, a number of deeply unethical studies took place—including the horrifying experiments conducted by the Nazis and the appalling Tuskegee syphilis trial, in which hundreds of African-American men were denied treatment by scientists who wanted to see how the lethal disease developed. As a result, there are now clear rules about seeking informed consent whenever medical experiments use human subjects, and institutional procedures for reviewing the design of such experiments in advance. We’ve learned that researchers aren’t always best placed to assess the potential harm of their work.

The info is here.

MIT Creates World’s First Psychopath AI By Feeding It Reddit Violent Content

Navin Bondade
www.techgrabyte.com
Originally posted October 2019

The state of the psychopathic is wider and darker in human intelligence that we haven’t fully understood yet, but still, scientists have given a try and to implement Psychopathism in Artificial Intelligence.

Scientists at MIT have created the world’s First Psychopath AI called Norman. The purpose of Norman AI is to demonstrate that AI cannot be unfair and biased unless such data is fed into it.

MIT’s Scientists have created Norman by training it on violent and gruesome content like images of people dying in gruesome circumstances from an unnamed Reddit page before showing it a series of Rorschach inkblot tests.

The Scientists created a dataset from this unnamed Reddit page and trained Norman to perform image captioning. This data is dedicated to documents and observe the disturbing reality of death.

The info is here.

The Truth About Algorithms

Cathy O'Neil
a 3 minute video

Algorithms are opinions, not truth machines, and demand the application of ethics

It can be easy to simply accept algorithms as indisputable mathematic truths. After all, who wants to spend their spare time deconstructing complex equations? But make no mistake: algorithms are limited tools for understanding the world, frequently as flawed and biased as the humans who create and interpret them. In this brief animation, which was adapted from a 2017 presentation at the Royal Society of Arts (RSA) in London, the US data scientist Cathy O’Neil, author of Weapons of Math Destruction (2016), argues that algorithms can be useful tools when thoughtfully deployed. However, their newfound ubiquity and massive power calls for ethical conduct from modellers, regulation and oversight by policymakers, and a more skeptical, mathematics-literate public.


The Truth About Algorithms | Cathy O’Neil from Nice Shit Studio on Vimeo.

Data Citizens: Why We All Care About Data Ethics

Caitlin McDonald
InfoQ.com
Originally posted July 4, 2018

Key Takeaways

• Data citizens are impacted by the models, methods, and algorithms created by data scientists, but they have limited agency to affect the tools which are acting on them.

• Data science ethics can draw on the conceptual frameworks in existing fields for guidance on how to approach ethical questions--specifically, in this case, civics.

• Data scientists are also data citizens. They are acted upon by the tools of data science as well as building them. It is often where these roles collide that people have the best understanding of the importance of developing ethical systems.

• One model for ensuring the rights of data citizens could be seeking the same level of transparency for ethical practices in data science that there are for lawyers and legislators.

• As with other ethical movements before, like seeking greater environmental protection or fairer working conditions, implementing new rights and responsibilities at scale will take a great deal of lobbying and advocacy.

The information is here.

What is data ethics?

Luciano Floridi and Mariarosaria Taddeo
Philosophical Transactions Royal Society A

This theme issue has the founding ambition of landscaping data ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including artificial intelligence, artificial agents, machine learning and robots) and corresponding practices (including responsible innovation, programming, hacking and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values).  Data ethics builds on the foundation provided by computer and information ethics but, at the sametime, it refines the approach endorsed so far in this research field, by shifting the level of abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations—the interactions among hardware, software and data—rather than on the variety of digital technologies that enable them. And it emphasizes the complexity of the ethical challenges posed by data science. Because of such complexity, data ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of data science and its applications within a consistent, holistic and inclusive framework. Only as a macroethics will data ethics provide solutions that can maximize the value of data science for our societies, for all of us and for our environments.This article is part of the themed issue ‘The ethical impact of data science’.

The article is here.