Tag: ethics

An ABC of Artificial Intelligence Concepts

An ABC of Artificial Intelligence Concepts

Yet another great resource by one of the teams at Google in collaboration with Oxford:

An ABC of Artificial Intelligence-related concepts!

The G is for GANs: Generative Adverserial Networks.

Want to know what GANs are all about?

Just read along with Google’s laymen explanation! Here’s an excerpt:

The P is for Predictions.

Currently the ABC is only available in English, but other language translations come available soon.

Check it out yourself!

Think Like a Coder – TEDEd learning series

Think Like a Coder – TEDEd learning series

I stumbled across this TED Ed YouTube playlist called Think Like A Coder. It’s an amusing 10-episode video introduction for those new to programming and coding.

The series follows Ethic, a girl who wakes up in a prison, struck by amnesia, and thus without a clue how she got there. She meets Hedge, a robot she can program to help her escape and, later, save the world. However, she needs to learn how to code the Hedge’s instructions, and write efficient computer programs. Ethic and Hedge embark on a quest to collect three artifacts and must solve their way through a series of programming puzzles.

Episode 1 covers loops.

The adventure begins!

Episode 1: Ethic awakens in a mysterious cell. Can she and robot Hedge solve the programming puzzles blocking their escape?

2019 Shortlist for the Royal Society Prize for Science Books

2019 Shortlist for the Royal Society Prize for Science Books

Since 1988, the Royal Society has celebrated outstanding popular science writing and authors.

Each year, a panel of expert judges choose the book that they believe makes popular science writing compelling and accessible to the public.

Over the decades, the Prize has celebrated some notable winners including Bill Bryson and Stephen Hawking.

The author of the winning book receives £25,000 and £2,500 is awarded to each of the five shortlisted books. And this year’s shortlist includes some definite must-reads on data and statistics!

Infinite Powers – by Steven Strogatz

The captivating story of mathematics’ greatest ever idea: calculus. Without it, there would be no computers, no microwave ovens, no GPS, and no space travel. But before it gave modern man almost infinite powers, calculus was behind centuries of controversy, competition, and even death. 

Taking us on a thrilling journey through three millennia, Professor Steven Strogatz charts the development of this seminal achievement, from the days of Archimedes to today’s breakthroughs in chaos theory and artificial intelligence. Filled with idiosyncratic characters from Pythagoras to Fourier, Infinite Powers is a compelling human drama that reveals the legacy of calculus in nearly every aspect of modern civilisation, including science, politics, medicine, philosophy, and more.

https://royalsociety.org/grants-schemes-awards/book-prizes/science-book-prize/2019/infinite-powers/

Invisible Women – by Caroline Criado Perez

Imagine a world where your phone is too big for your hand, where your doctor prescribes a drug that is wrong for your body, where in a car accident you are 47% more likely to be seriously injured, where every week the countless hours of work you do are not recognised or valued. If any of this sounds familiar, chances are that you’re a woman.

Invisible Women shows us how, in a world largely built for and by men, we are systematically ignoring half the population. It exposes the gender data gap–a gap in our knowledge that is at the root of perpetual, systemic discrimination against women, and that has created a pervasive but invisible bias with a profound effect on women’s lives. From government policy and medical research, to technology, workplaces, urban planning and the media, Invisible Women reveals the biased data that excludes women.

https://royalsociety.org/grants-schemes-awards/book-prizes/science-book-prize/2019/invisible-women/

Six Impossible Things – by John Gribbin

This book does not deal with data or statistics specifically, but might even be more interesting, as it covers the topic of quantum physics:

Quantum physics is strange. It tells us that a particle can be in two places at once. That particle is also a wave, and everything in the quantum world can be described entirely in terms of waves, or entirely in terms of particles, whichever you prefer. 

All of this was clear by the end of the 1920s, but to the great distress of many physicists, let alone ordinary mortals, nobody has ever been able to come up with a common sense explanation of what is going on. Physicists have sought ‘quanta of solace’ in a variety of more or less convincing interpretations. 

This short guide presents us with the six theories that try to explain the wild wonders of quantum. All of them are crazy, and some are crazier than others, but in this world crazy does not necessarily mean wrong, and being crazier does not necessarily mean more wrong.

https://royalsociety.org/grants-schemes-awards/book-prizes/science-book-prize/2019/six-impossible-things/

The other shortlisted books

Awful AI: A curated list of scary usages of artificial intelligence

Awful AI: A curated list of scary usages of artificial intelligence

I found this amazingly horrifying list called Awful Artificial Intelligence:

Artificial intelligence [AI] in its current state is unfaireasily susceptible to attacks and notoriously difficult to control. Nevertheless, more and more concerning the uses of AI technology are appearing in the wild.

[Awful A.I.] aims to track all of them. We hope that Awful AI can be a platform to spur discussion for the development of possible contestational technology (to fight back!).

David Dao on the Awful A.I. github repository

The Awful A.I. list contains a few dozen applications of machine learning where the results were less than optimal for several involved parties. These AI solutions either resulted in discrimination, disinformation (fake news), mass surveillance, or severely violate privacy or ethical issues in many other ways.

We’ve all heard of Cambridge Analytica, but there are many more on this Awful A.I. list:

Deep Fakes – Deep Fakes is an artificial intelligence-based human image synthesis technique. It is used to combine and superimpose existing images and videos onto source images or videos. Deepfakes may be used to create fake celebrity pornographic videos or revenge porn. [AI assisted fake porn][CNN Interactive Report]

David Dao on the Awful A.I. github repository

Social Credit System – Using a secret algorithm, Sesame credit constantly scores people from 350 to 950, and its ratings are based on factors including considerations of “interpersonal relationships” and consumer habits. [summary][Foreign Correspondent (video)][travel ban]

David Dao on the Awful A.I. github repository

SenseTime & Megvii– Based on Face Recognition technology powered by deep learning algorithm, SenseFace and Megvii provides integrated solutions of intelligent video analysis, which functions in target surveillance, trajectory analysis, population management. [summary][forbes][The Economist (video)]

David Dao on the Awful A.I. github repository

Check out the full list here.

David Dao is a PhD student at DS3Lab — the computer science dpt. of Zurich — and maintains the awful AI list. The cover photo was created by LargeStupidity on Drawception

Uber: Translating Behavioural Science to the Work Floor with Gamification and Experimentation

Uber: Translating Behavioural Science to the Work Floor with Gamification and Experimentation

Yesterday, I read the most interesting article on how Uber uses academic research from the field of behavioral psychology to persuade their drivers to display desired behaviors. The tone of the article is quite negative and I most definitely agree there are several ethical issues at hand here. However, as a data scientist, I was fascinated by the way in which Uber has translated academic insights and statistical methodology into applications within their own organization that actually seem to pay off. Well, at least in the short term, as this does not seem a viable long-term strategy.

The full article is quite a long read (~20 min), and although I definitely recommend you read it yourself, here are my summary notes, for convenience quoted from the original article:

  • “Employing hundreds of social scientists and data scientists, Uber has experimented with video game techniques, graphics and noncash rewards of little value that can prod drivers into working longer and harder — and sometimes at hours and locations that are less lucrative for them.”
  • “To keep drivers on the road, the company has exploited some people’s tendency to set earnings goals — alerting them that they are ever so close to hitting a precious target when they try to log off.”
  • “Uber exists in a kind of legal and ethical purgatory […] because its drivers are independent contractors, they lack most of the protections associated with employment.”
  • “[…] much of Uber’s communication with drivers over the years has aimed at combating shortages by advising drivers to move to areas where they exist, or where they might arise. Uber encouraged its local managers to experiment with ways of achieving this.[…] Some local managers who were men went so far as to adopt a female persona for texting drivers, having found that the uptake was higher when they did.”
  • “[…] Uber was increasingly concerned that many new drivers were leaving the platform before completing the 25 rides that would earn them a signing bonus. To stem that tide, Uber officials in some cities began experimenting with simple encouragement: You’re almost halfway there, congratulations! While the experiment seemed warm and innocuous, it had in fact been exquisitely calibrated. The company’s data scientists had previously discovered that once drivers reached the 25-ride threshold, their rate of attrition fell sharply.”

  • “For months, when drivers tried to log out, the app would frequently tell them they were only a certain amount away from making a seemingly arbitrary sum for the day, or from matching their earnings from that point one week earlier.The messages were intended to exploit another relatively widespread behavioral tic — people’s preoccupation with goals — to nudge them into driving longer. […] Are you sure you want to go offline?” Below were two prompts: “Go offline” and “Keep driving.” The latter was already highlighted.”

  • “Sometimes the so-called gamification is quite literal. Like players on video game platforms such as Xbox, PlayStation and Pogo, Uber drivers can earn badges for achievements like Above and Beyond (denoted on the app by a cartoon of a rocket blasting off), Excellent Service (marked by a picture of a sparkling diamond) and Entertaining Drive (a pair of Groucho Marx glasses with nose and eyebrows).”
  • “More important, some of the psychological levers that Uber pulls to increase the supply of drivers have quite powerful effects. Consider an algorithm called forward dispatch […] that dispatches a new ride to a driver before the current one ends. Forward dispatch shortens waiting times for passengers, who may no longer have to wait for a driver 10 minutes away when a second driver is dropping off a passenger two minutes away. Perhaps no less important, forward dispatch causes drivers to stay on the road substantially longer during busy periods […]
    [But] there is another way to think of the logic of forward dispatch: It overrides self-control. Perhaps the most prominent example is that such automatic queuing appears to have fostered the rise of binge-watching on Netflix. “When one program is nearing the end of its running time, Netflix will automatically cue up the next episode in that series for you,” wrote the scholars Matthew Pittman and Kim Sheehan in a 2015 study of the phenomenon. “It requires very little effort to binge on Netflix; in fact, it takes more effort to stop than to keep going.””
  • “Kevin Werbach, a business professor who has written extensively on the subject, said that while gamification could be a force for good in the gig economy — for example, by creating bonds among workers who do not share a physical space — there was a danger of abuse.”
  • “There is also the possibility that as the online gig economy matures, companies like Uber may adopt a set of norms that limit their ability to manipulate workers through cleverly designed apps. For example, the company has access to a variety of metrics, like braking and acceleration speed, that indicate whether someone is driving erratically and may need to rest. “The next step may be individualized targeting and nudging in the moment,” Ms. Peters said. “‘Hey, you just got three passengers in a row who said they felt unsafe. Go home.’” Uber has already rolled out efforts in this vein in numerous cities.”
  • “That moment of maturity does not appear to have arrived yet, however. Consider a prompt that Uber rolled out this year, inviting drivers to press a large box if they want the app to navigate them to an area where they have a “higher chance” of finding passengers. The accompanying graphic resembles the one that indicates that an area’s fares are “surging,” except in this case fares are not necessarily higher.”

Click the below for the full article.