Data Skeptic15 Tammi 2016

Detecting Pseudo-profound BS

A recent paper in the journal of Judgment and Decision Making titled On the reception and detection of pseudo-profound bullshit explores empirical questions around a reader's ability to detect statements which may sound profound but are actually a collection of buzzwords that fail to contain adequate meaning or truth. These statements are definitively different from lies and nonesense, as we discuss in the episode.

This paper proposes the Bullshit Receptivity scale (BSR) and empirically demonstrates that it correlates with existing metrics like the Cognitive Reflection Test, building confidence that this can be a useful, repeatable, empirical measure of a person's ability to detect pseudo-profound statements as being different from genuinely profound statements. Additionally, the correlative results provide some insight into possible root causes for why individuals might find great profundity in these statements based on other beliefs or cognitive measures.

The paper's lead author Gordon Pennycook joins me to discuss this study's results.

If you'd like some examples of pseudo-profound bullshit, you can randomly generate some based on Deepak Chopra's twitter feed.

To read other work from Gordon, check out his Google Scholar page and find him on twitter via @GordonPennycook.

And just for fun, if you think you've dreamed up a Data Skeptic related pseudo-profound bullshit statement, tweet it with hashtag #pseudoprofound. If I see an especially clever or humorous one, I might want to send you a free Data Skeptic sticker.

Tämä jakso on lisätty Podme-palveluun avoimen RSS-syötteen kautta eikä se ole Podmen omaa tuotantoa. Siksi jakso saattaa sisältää mainontaa.

Jaksot(601)

Sustainable Recommender Systems for Tourism

In this episode, we speak with Ashmi Banerjee, a doctoral candidate at the Technical University of Munich, about her pioneering research on AI-powered recommender systems in tourism. Ashmi illuminates...

9 Loka 202538min

Interpretable Real Estate Recommendations

In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich interviews Dr. Kunal Mukherjee, a postdoctoral research associate at Virginia Tech, about the paper "Z-REx: Human-Interpr...

22 Syys 202532min

Why Am I Seeing This?

In this episode of Data Skeptic, we explore the challenges of studying social media recommender systems when exposure data isn't accessible. Our guests Sabrina Guidotti, Gregor Donabauer, and Dimitri ...

8 Syys 202549min

Eco-aware GNN Recommenders

In this episode of Data Skeptic, we dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza University of Rome. Antonio discusses his research on "EcoAware Graph Neural Networks...

30 Elo 202544min

Networks and Recommender Systems

Kyle reveals the next season's topic will be "Recommender Systems". Asaf shares insights on how network science contributes to the recommender system field.

17 Elo 202517min

Network of Past Guests Collaborations

Kyle and Asaf discuss a project in which we link former guests of the podcast based on their co-authorship of academic papers.

21 Heinä 202534min

The Network Diversion Problem

In this episode, Professor Pål Grønås Drange from the University of Bergen, introduces the field of Parameterized Complexity - a powerful framework for tackling hard computational problems by focusing...

6 Heinä 202546min

Complex Dynamic in Networks

In this episode, we learn why simply analyzing the structure of a network is not enough, and how the dynamics - the actual mechanisms of interaction between components - can drastically change how inf...

28 Kesä 202556min