PriBots: Conversational Privacy with Chatbots (at SOUPS 2016)
•
0 likes•182 views
Presentation at the Workshop on the Future of Privacy Notices and Indicators, at the 12th Symposium on Usable Privacy and Security (SOUPS 2016). The paper is available here: https://infoscience.epfl.ch/record/218659 Video slides are available here: https://www.youtube.com/watch?v=iHVXnWg9u0g You can find more about my work on my personal website: http://hamzaharkous.com
Recommended
Recommended
More Related Content
Similar to PriBots: Conversational Privacy with Chatbots (at SOUPS 2016)
Similar to PriBots: Conversational Privacy with Chatbots (at SOUPS 2016) (20)
Recently uploaded
Recently uploaded (20)
PriBots: Conversational Privacy with Chatbots (at SOUPS 2016)
- 3. legally binding human understandable Dual Role: 2 PrivacyNotice:CurrentState
- 4. Canwesplittheseroles? legally binding human understandable Dual Role: 2 PrivacyNotice:CurrentState
- 5. a standardized short table on the right. The comparison highlights the rows below the table. While both formats contain the legend (bottom right), it is In preparation for this study we first performed three smaller pilot tests of our survey framework. We ran our pilot studies with approximately thirty users each, across 2-3 conditions. Our pilot studies helped us to finalize remaining design de- cisions surrounding the standardized short table, refine our Standardization 3
- 6. a standardized short table on the right. The comparison highlights the rows below the table. While both formats contain the legend (bottom right), it is In preparation for this study we first performed three smaller pilot tests of our survey framework. We ran our pilot studies with approximately thirty users each, across 2-3 conditions. Our pilot studies helped us to finalize remaining design de- cisions surrounding the standardized short table, refine our Standardization 3 Summarization
- 7. a standardized short table on the right. The comparison highlights the rows below the table. While both formats contain the legend (bottom right), it is In preparation for this study we first performed three smaller pilot tests of our survey framework. We ran our pilot studies with approximately thirty users each, across 2-3 conditions. Our pilot studies helped us to finalize remaining design de- cisions surrounding the standardized short table, refine our Standardization 3 Summarization Challenges one size fits all user education
- 9. fragmented ecosystem difficult to find 4 PrivacyChoice:CurrentState
- 14. PriBots:ConversationalPrivacyBots Message| Appeal to new tech adopters 7
- 15. PriBots:ConversationalPrivacyBots Message| Appeal to new tech adopters Appeal to existing users 7
- 16. PriBots:ConversationalPrivacyBots Message| Appeal to new tech adopters Appeal to existing users Anintuitivewayto 1. communicateprivacypolicies 2. adjustprivacypreferences 7
- 18. Channel 9
- 21. Timing 10
- 24. Feedback implicit: sentiment analysis explicit: structured messages gathering users’ concerns 11
- 26. VoicingUserConcerns Providers traditionally say what they want Users’ concerns might not be covered 12
- 27. VoicingUserConcerns Providers traditionally say what they want Users’ concerns might not be covered PriBots activate the two-way channel 12
- 29. 14
- 30. Service and platform-dependent interface 14
- 31. Service and platform-dependent interface Tradeoffs for simplicity: try finding this setting on Mobile Web version 14
- 32. 15
- 33. Unique interface with all functionalities Ability to suggest adjustments to the user (combining notice and choice/preferences ) 15
- 35. User Input Analysis & Classification Query Structured Query Statement Yes No SystemArchitecture 16
- 37. User Input Analysis & Classification Query Structured Query Statement Yes No Confident? Answer Formulation in NL Fallback Answer Generation Yes No Retrieval Module Result Knowledge Base SystemArchitecture 16
- 38. PriBot Reply User Input Analysis & Classification Query Structured Query Statement Yes No Confident? Answer Formulation in NL Fallback Answer Generation Yes No Retrieval Module Result Knowledge Base SystemArchitecture 16
- 39. PriBot Reply User Input Analysis & Classification Query Structured Query Statement Yes No Confident? Answer Formulation in NL Fallback Answer Generation Yes No Retrieval Module Result Knowledge Base SystemArchitecture 16 Feedback DB Analytics Amendments/ Improvements Augment the Knowledge Base • unanswered queries • frequent questions • user sentiments Feedback DB
- 40. Challenges 17
- 41. MatureUserUnderstanding Text processing Question answering Domain-specific datasets and ontologies Graceful fallback 18
- 42. LegalChallenges Inherently error prone: are they legally binding? Accounting for false-positives and false-negatives The case of 3rd party PriBots: defamation possibilities? 19
- 43. TrustingtheMachine rule-based vs. AI-based user backlash? regulate the confidence level 20
- 44. PriBots’Personality positive tone → higher trust diversified content → reduced habituation 21
- 45. Deployment 22
- 47. provider 3rd parties Deployment 22 Suitable for Voice Assistants
- 50. Image/MediaCredits Zara Picken: slide 12 Egor Kosten: slide 24 Alex Prokhoda: slide 6 Freepik: slide 23 Geoff Keough: slide 14 Victor: slide 12