phil 7.12.18

Stampede thinking:

  • Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning
    • Gordon Pennycook
    • David Rand
    • Why do people believe blatantly inaccurate news headlines (“fake news”)? Do we use our reasoning abilities to convince ourselves that statements that align with our ideology are true, or does reasoning allow us to effectively differentiate fake from real regardless of political ideology? Here we test these competing accounts in two studies (total N = 3446 Mechanical Turk workers) by using the Cognitive Reflection Test (CRT) as a measure of the propensity to engage in analytical reasoning. We find that CRT performance is negatively correlated with the perceived accuracy of fake news, and positively correlated with the ability to discern fake news from real news – even for headlines that align with individuals’ political ideology. Moreover, overall discernment was actually better for ideologically aligned headlines than for misaligned headlines. Finally, a headline-level analysis finds that CRT is negatively correlated with perceived accuracy of relatively implausible (primarily fake) headlines, and positively correlated with perceived accuracy of relatively plausible (primarily real) headlines. In contrast, the correlation between CRT and perceived accuracy is unrelated to how closely the headline aligns with the participant’s ideology. Thus, we conclude that analytic thinking is used to assess the plausibility of headlines, regardless of whether the stories are consistent or inconsistent with one’s political ideology. Our findings therefore suggest that susceptibility to fake news is driven more by lazy thinking than it is by partisan bias per se – a finding that opens potential avenues for fighting fake news.

From Alessandro Bozzon (Scholar):

  • I am Assistant Professor with the Web Information Systemsgroup, at the Delft University of Technology. I am Research Fellow at the AMS Amsterdam Institute for Advanced Metropolitan Solutions, and a Faculty Fellow with the IBM Benelux Center of Advanced Studies.

    My research lies at the intersection of crowdsourcing, user modeling, and web information retrieval. I study and build novel Social Data science methods and tools that combine the cognitive and reasoning abilities of individuals and crowds, with the computational powers of machines, and the value of big amounts of heterogeneous data.

    I am currently active in three investigation lines related to Social Data Science: Intelligent Cities (SocialGlass; Crowdsourced Knowledge Creation in Online Social Communities (SEALINCMedia COMMIT/StackOverflow); and Enterprise Crowdsourcing (with IBM Benelux CAS).

  • Modeling CrowdSourcing Scenarios in Socially-Enabled Human Computation Applications
    • User models have been defined since the 1980s, mainly for the purpose of building context-based, user-adaptive applications. However, the advent of social networked media, serious games, and crowdsourcing/human computation platforms calls for a more pervasive notion of user model, capable of representing the multiple facets of social users and performers, including their social ties, interests, capabilities, activity history, and topical affinities. In this paper, we define a comprehensive model able to cater for all the aspects relevant for applications involving social networks and human computation; we capitalize on existing social user models and content description models, enhancing them with novel models for human computation and gaming activities representation. Finally, we report on our experiences in adopting the proposed model in the design and implementation of three socially enabled human computation platforms.
  • Sparrows and Owls: Characterisation of Expert Behaviour in StackOverflow
    • Question Answering platforms are becoming an important repository of crowd-generated knowledge. In these systems a relatively small subset of users is responsible for the majority of the contributions, and ultimately, for the success of the Q/A system itself. However, due to built-in incentivization mechanisms, standard expert identification methods often misclassify very active users for knowledgable ones, and misjudge activeness for expertise. This paper contributes a novel metric for expert identification, which provides a better characterisation of users’ expertise by focusing on the quality of their contributions. We identify two classes of relevant users, namely sparrows and owls, and we describe several behavioural properties in the context of the StackOverflow Q/A system. Our results contribute new insights to the study of expert behaviour in Q/A platforms, that are relevant to a variety of contexts and applications.

Phil 7.8.18

Scott Klemmer Keynote 2

  • What are interesting things that we can do with computers and teaching – 2011
  • Objective truth <-> Contextual truth
  • Design is in the middle, between objective and subjective truth
  • The act of assessing work is a good way to improve understanding
  • Problem finding as opposed to problem solving
  • “A negotiation around the valuation criteria” Jeff Nicholson
  • Negotiations also happen between the creators and the users, particularly in software design. The initial design is the starting point of that journey
  • What counts as preferred shifts over time
  • Talkabout – The subway model. Pick a time that you’re going to show up, and we’ll put you in a group. Small groups discuss topics.
  • Assigning to globally diverse discussion groups increase grades by greater amounts than more local, less diverse groups. Open-ended questions
  • DSCN0348DSCN0349DSCN0350DSCN0351DSCN0352

Participated in the panel on innovation in crowds (invited). There is a video, so I can figure out who to add:

  • Christopher Tucci,
  • Gianluigi Viscusi (GG)
  • Rosy Mondardini
  • Thomas Malone
  • Joel Chan
  • Philip Feldman

Eszter Hargitti – U of Zurich

  • Awareness of what is possible
  • The ability to create and share content
  • Wikigroan?
  • DSCN0353DSCN0354DSCN0355

When Ties Bind And When Ties Divide: The Effects Of Communication Networks On Group Processes And Performance DSCN0356.JPG_1DSCN0357.JPG_2

  • Network structural variance

Enhancing Collective Intelligence of Human-Machine Teams DSCN0358DSCN0359

  • Cognitive and ethnic diversity predict collective intelligence
  • Group structure, high level communication and equality of communication
  • It’s the quality of the individuals and the quality of the connections
  • Coordination technologies – connect humans

Implicit Coordination in Peer Production Networks DSCN0360DSCN0361DSCN0362DSCN0363

Collective Intelligence Systems for Analogical Search (must read! Joel Chan is at UMD)

  • Really interesting, worth reading. Purpose and mechanism may be related to belief spaces. Definitely trainable using NN to find purpose mechanism

Rational Collective Learning in the Laboratory

  • Groupthink. as a failure of design
  • Randomy constructed groups can make good design choices given failing parts with a history.

Phil 7.7.18

8:00 – 9:00 ASRC MKT

  • At CI 2018. Hell of a time setting up eduroam. Nice venue, though. Winston Churchill called for the unification of Europe from that podium. Probably without PowerPoint DSCN0310
  • Patrick Meier – keynote – Digital humanitarian efforts
    • Mission is to pioneer the next generation of humanitarian technology
    • DSCN0313
    • DSCN0315
  • Poster pitches
    • Multiple barriers to crowdsourcing, ranging from operational to strategic
    • Anita Wollie – trust in AI Embedded agency, Virtual agency, Physical Agency
    • Croudoscope – qualitative and quantitative surveys – open coments. Not lists, but graphs
    • Market volitility with High-Frequency trading an hmans
    • How many people constitutes a ‘crowd’
    • Is novelty an advantage in crowdfunding
    • QUEST – annotating questions on stackoverflow-style probles’
    • Cyber-physical systems – e.g. smart transportation systems
  • Papers
  • Keynote 2
    • Optimizing the Human-Machine Partnership with Zooniverse DSCN0321 DSCN0322
      • Lucy Fortson
      • Galaxy Zoo
      • Zooniverse is on its third iteration and now supports project building
      • Can also point to a project
  • Session 2
    • Collective Intelligence for Deep Reinforcement Learning (MIT, mostly)
      • Evolutionary strategies (Salimans 2017) DSCN0327
    • Social learning strategies for matters of taste (This is a must-read!)
      • DSCN0326DSCN0325DSCN0324
    • Photo Sleuth: Combining Collective Intelligence and Computer Vision to
      Identify Historical Portraits

      • Good discussion of how to blend human and ML person identification
    • Toward Safer Crowdsourced Content Moderation
    • How Intermittent Breaks in Interaction Improve Collective

Phil 7.1.18

On vacation, but oddly enough, I’m back on my morning schedule, so here I am in Bormio, Italy at 4:30 am.

I forgot my HDMI adaptor for the laptop. Need to order one and have it delivered to Zurich – Hmmm. Can’t seem to get it delivered from Amazon to a hotel. Will have to buy in Zurich

Need to add Gamerfate to the lit review timeline to show where I started to get interested in the problem – tried it but didn’t like it. I’d have to redo the timeline and I’m not sure I have the excel file

Add vacation pictures to slides – done!

Some random thoughts

  • When using the belief space example of the table, note that if we sum up all the discussions about tables, we would be able to build a pretty god map of what matters to people with regards to tables
  • Manifold learning is what intelligent systems do as a way of determining relationships between things (see curse of dimensionality). As groups of individuals, we need to coordinate our manifold learning activities so that we can us the power of group cognition. When looking at how manifold learning schemes like t-sne and particularly embedding systems such as word2vec create their own unique embeddings, it becomes clear that our machines are not yet engaged in group cognition, except in the simplest way of re-using trained networks and copied hyperparameters. This is very prone to stampedes
  • In conversation at dinner, Mike M mentioned that he’d like a language app that is able to indicate the centrality of a term an order that list so that it’s possible to learn a language in a “prioritized” way that can be context-dependent. I think that LMN with a few tweaks could do that.

Continuing the Evolution of Cooperation. A thing that strikes me is that once a TIT FOR TAT successfully takes over, then it becomes computationally easier to ALWAYS COOPERATE. That could evolve to become dominant and be completely vulnerable to ALWAYS DEFECT