Data Skeptic

Fake news attempts to lead readers/listeners/viewers to conclusions that are not descriptions of reality. They do this most often by presenting false premises, but sometimes by presenting flawed logic.

An argument is only sound and valid if the conclusions are drawn directly from all the state premises, and if there exists a path of logical reasoning leading from those premises to the conclusion.

While creating a theorem does feel to most mathematicians as a creative act of discovery, some theorems have been proven using nothing more than search. All the "rules" of logic (like modus ponens) can be encoded into a computer program. That program can start from the premises, applying various combinations of rules to inference new information, and check to see if the program has inference the desired conclusion or its negation. This does seem like a mechanical process when painted in this light. However, several challenges exist preventing any theorem prover from instantly solving all the open problems in mathematics. In this episode, we discuss a bit about what those challenges are.

Fake news can be responded to with fact-checking. However, it's easier to create fake news than the fact check it.

Full Fact is the UK's independent fact-checking organization. In this episode, Kyle interviews Mevan Babakar, head of automated fact-checking at Full Fact.

Our discussion talks about the process and challenges in doing fact-checking. Full Fact has been exploring ways in which machine learning can assist in automating parts of the fact-checking process. Progress in areas like this allows journalists to be more effective and rapid in responding to new information.

In mathematics, truth is universal. In data, truth lies in the where clause of the query.

As large organizations have grown to rely on their data more significantly for decision making, a common problem is not being able to agree on what the data is.

As the volume and velocity of data grow, challenges emerge in answering questions with precision. A simple question like "what was the revenue yesterday" could become mired in details. Did your query account for transactions that haven't been finalized? If I query again later, should I exclude orders that have been returned since the last query? What time zone should I use? The list goes on and on.

In any large enough organization, you are also likely to find multiple copies if the same data. Independent systems might record the same information with slight variance. Sometimes systems will import data from other systems; a process which could become out of sync for several reasons.

For any sufficiently large system, answering analytical questions with precision can become a non-trivial challenge. The business intelligence community aspires to provide a "single source of truth" - one canonical place where data consumers can go to get precise, reliable, and trusted answers to their analytical questions.

Fast radio bursts are an astrophysical phenomenon first observed in 2007. While many observations have been made, science has yet to explain the mechanism for these events. This has led some to ask: could it be a form of extra-terrestrial communication?

Probably not. Kyle asks Gerry Zhang who works at the Berkeley SETI Research Center about this possibility and more importantly, about his applications of deep learning to detect fast radio bursts.

Radio astronomy captures observations from space which can be converted to a waterfall chart or spectrogram. These data structures can be formatted in a visual way and also make great candidates for applying deep learning to the task of detecting the fast radio bursts.

This episode explores the root concept of what it is to be Bayesian: describing knowledge of a system probabilistically, having an appropriate prior probability, know how to weigh new evidence, and following Bayes's rule to compute the revised distribution.

We present this concept in a few different contexts but primarily focus on how our bird Yoshi sends signals about her food preferences.

Like many animals, Yoshi is a complex creature whose preferences cannot easily be summarized by a straightforward utility function the way they might in a textbook reinforcement learning problem. Her preferences are sequential, conditional, and evolving. We may not always know what our bird is thinking, but we have some good indicators that give us clues.

This is our interview with Dorje Brody about his recent paper with David Meier, How to model fake news. This paper uses the tools of communication theory and a sub-topic called filtering theory to describe the mathematical basis for an information channel which can contain fake news.

Thanks to our sponsor Gartner.

Without getting into definitions, we have an intuitive sense of what a "community" is. The Louvain Method for Community Detection is one of the best known mathematical techniques designed to detect communities.

This method requires typical graph data in which people are nodes and edges are their connections. It's easy to imagine this data in the context of Facebook or LinkedIn but the technique applies just as well to any other dataset like cellular phone calling records or pen-pals.

The Louvain Method provides a means of measuring the strength of any proposed community based on a concept known as Modularity. Modularity is a value in the range that measure the density of links internal to a community against links external to the community. The quite palatable assumption here is that a genuine community would have members that are strongly interconnected.

A community is not necessarily the same thing as a clique; it is not required that all community members know each other. Rather, we simply define a community as a graph structure where the nodes are more connected to each other than connected to people outside the community.

It's only natural that any person in a community has many connections to people outside that community. The more a community has internal connections over external connections, the stronger that community is considered to be. The Louvain Method elegantly captures this intuitively desirable quality.

In this episode, our guest is Dan Kahan about his research into how people consume and interpret science news.

In an era of fake news, motivated reasoning, and alternative facts, important questions need to be asked about how people understand new information.

Dan is a member of the Cultural Cognition Project at Yale University, a group of scholars interested in studying how cultural values shape public risk perceptions and related policy beliefs.

In a paper titled Cultural cognition of scientific consensus, Dan and co-authors Hank Jenkins‐Smith and Donald Braman discuss the "cultural cognition of risk" and establish experimentally that individuals tend to update their beliefs about scientific information through a context of their pre-existing cultural beliefs. In this way, topics such as climate change, nuclear power, and conceal-carry handgun permits often result in people.

The findings of this and other studies tell us that on topics such as these, even when people are given proper information about a scientific consensus, individuals still interpret those results through the lens of their pre-existing cultural beliefs.

The 'cultural cognition of risk' refers to the tendency of individuals to form risk perceptions that are congenial to their values. The study presents both correlational and experimental evidence confirming that cultural cognition shapes individuals' beliefs about the existence of scientific consensus, and the process by which they form such beliefs, relating to climate change, the disposal of nuclear wastes, and the effect of permitting concealed possession of handguns. The implications of this dynamic for science communication and public policy‐making are discussed.

A false discovery rate (FDR) is a methodology that can be useful when struggling with the problem of multiple comparisons.

In any experiment, if the experimenter checks more than one dependent variable, then they are making multiple comparisons. Naturally, if you make enough comparisons, you will eventually find some correlation.

Classically, people applied the Bonferroni Correction. In essence, this procedure dictates that you should lower your p-value (raise your standard of evidence) by a specific amount depending on the number of variables you're considering. While effective, this methodology is strict about preventing false positives (type i errors). You aren't likely to find evidence for a hypothesis that is actually false using Bonferroni. However, your exuberance to avoid type i errors may have introduced some type ii errors. There could be some hypotheses that are actually true, which you did not notice.

This episode covers an alternative known as false discovery rates. The essence of this method is to make more specific adjustments to your expectation of what p-value is sufficient evidence.

Digital videos can be described as sequences of still images and associated audio. Audio is easy to fake. What about video?

A video can easily be broken down into a sequence of still images replayed rapidly in sequence. In this context, videos are simply very high dimensional sequences of observations, ripe for input into a machine learning algorithm.

The availability of commodity hardware, clever algorithms, and well-designed software to implement those algorithms at scale make it possible to do machine learning on video, but to what end? There are many answers, one interesting approach being the technology called "DeepFakes".

The Deep of Deepfakes refers to Deep Learning, and the fake refers to the function of the software - to take a real video of a human being and digitally alter their face to match someone else's face. Here are two examples:

• Barack Obama via Jordan Peele

• The versatility of Nick Cage

This software produces curiously convincing fake videos. Yet, there's something slightly off about them. Surely machine learning can be used to determine real from fake... right? Siwei Lyu and his collaborators certainly thought so and demonstrated this idea by identifying a novel, detectable feature which was commonly missing from videos produced by the Deep Fakes software.

In this episode, we discuss this use case for deep learning, detecting fake videos, and the threat of fake videos in the future.