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Recoding black-mirror2017-irrefutable-history-of-you
- 1. The Irrefutable History of You Allan Third and John Domingue Recoding Black Mirror @ ISWC 2017
- 2. Nosedive
- 3. Nosedive
- 4. What if things were like this? • Clearly a satire on obsession with Likes, Retweets, etc., on social media • But… – Could it happen (technically)? – How could we mitigate the consequences? • We won’t look at – Could it happen (socially)? – Should it happen? (No)
- 5. Can Nosedive be implemented? • Identification of people – Connection to their social media accounts – Reliable – Automatic • A system for recording and sharing ratings – Trustworthy – Tamper-proof • All of this technology exists already
- 6. How can we do it? • Phones with accurate GPS – Broadcast user position/social media details • Biometric sensors – Avoids the “stolen phone” problem • Distributed ledgers – Secure tamper-proof record of transactions
- 7. What is a distributed ledger? • Blockchain (underpins Bitcoin) • Shared append-only data structure • Writing records has a cost • Being picked to extend the chain – Costs money but earns a reward – “Miners” compete • Everyone has a copy of everything – Tamper attempts detectable
- 8. Properties of distributed ledgers • Records are (effectively) – Immutable – Reliably timestamped – Reliably attributable • Support multiple data-writers without trust – Initial motivation e-currency, to solve “double spend” problem • DL backend fits requirements for Nosedive
- 9. How can we do it? (Reminder) • Phones with accurate GPS – Broadcast user position/social media details • Biometric sensors – Avoids the “lost/stolen phone” problem • Distributed ledgers – Secure tamper-proof record of transactions
- 10. Mitigating the consequences • Potential for abuse – (Low) ratings which are: • Malicious • Trivial • Thoughtless – Over-interpreting ratings out of context • How can we limit these?
- 11. Moderation approaches • Variations on limiting ability to give/view ratings • Consider – Choices of the rated person – Reputation of the rater – Opinions of others – Costs of ratings
- 12. Choices of the rated person • Choose whether to accept a rating? – Certainly avoids abuse by low ratings – Unlikely to be accepted as an approach • Rating inflation • Require mutual ratings? – Pressure to limit low rating abuse – Also unlikely to be accepted • Selective visibility of ratings – Similar problems as choosing to accept/reject
- 13. Reputation of the rater • Weight ratings by – Average rating of rater • Influenced by prejudice • Existing power structures replicated – Illustrated in episode – Rating history of rater • Subject to gaming by malicious raters • Doesn’t prevent multiple low ratings by crowds
- 14. Opinions of others • Meta-rating – Randomly select nearby people to “rate the rating” – Effective in online forum settings (e.g., Slashdot) • Effort from others • Likely to follow existing prejudices/power structures
- 15. Cost of ratings • Rater has to pay for each rating – Limits frivolous ratings • Guaranteed to preserve existing inequalities – Rich have more power and will maintain it
- 16. Semantic mitigation approaches • Use semantic technologies to provide richer interpretation – Represent context of ratings – Ontology and reasoning – Categorical ratings – Two-way personalisation of ratings
- 17. Rating context • Semantic annotation of rating – e.g., retail transaction • As purchaser • As retailer – Social interaction • e.g., as wedding guest • Fine-grained interpretation – Aggregate rating only across similar contexts – Link to evidence for rating
- 18. Ontology and reasoning • Ontology of rateable interactions – Manually seeded, crowd-sourced? • Reasoning on ratings using ontology – e.g., “aggregate rating of A’s interaction in a financial context with people who rate A highly financially and low socially” • Possible enabling of more abuse – Derived categories could be specific and discriminatory
- 19. Categorical ratings • Semantic categories instead of numbers – “A was helpful in this retail interaction as retailer” • Avoids over-interpretation of numbers – Non-numerical aggregation – “mostly harmless” • More flexible – Harder to model • Rating categories curated? – Could otherwise be abusive/insulting
- 20. Two-way personalisation • Expose ratings only for particular semantic contexts • Require viewers to provide semantic context of interpretation to be able to see – How to prove this? • Automatic negotiation of visibility • Unlikely to be adopted in hypothetical society
- 21. Conclusions • This is an awful idea, but – Hypothetically, we can mitigate it with • Moderation (mainly mitigates giving ratings) • Semantics (mainly mitigates interpretation) – All mitigation strategies either have flaws or go against “purpose” of rating system • It’s still a really terrible idea
- 22. The End • Any questions?
Editor's Notes
- Ubiquitous rating system 1-5. Examples of ratings. Effect on main character.
- Caricature. For the sake of argument, assume…
- For the uninitated
- In the hypothetical social context
- Notice that in the episode, main characters shown to suffer from rating system are a woman and a black man; Lacie’s brother (white male, hinted to be heterosexual) makes a point of being unconcerned about his rating Low crowd ratings: as in the episode