"The Irrefutable History of You", a presentation at the Re-coding Black Mirror workshop (https://kmitd.github.io/recoding-black-mirror/) at ISWC 2017 (http://iswc2017.semanticweb.org)

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?