Hello SSN.Arvind Ram
SSN CSE 2013 Batch
Machine Learning
Why ML ?
To make RIGHT
decisions
People can make RIGHT decisions!
Correct. But...
Hard to make large number of them
Decision fatigue
Leads to WRONG decisions
Birth of ML Algorithms
Decisions need DATA
Data needs Privacy
Data reveals your identity
Identity loss can have serious consequences
Ex: Gender Discrimination
Ex: Caste Discrimination
Ex: Theft Targets
We need to protect identity
From who ?
External hackers
Internal hackers
How ?
Multi level access control for extenral hackers
Internal hackers/employees
Data Anonymization
Data Anonymization
Reference: https://trustarc.com/blog/wp-content/uploads/Hash_information_tableData-anonymization-blog.png
Case Study
Netflix Case Study
Reference: https://www.youtube.com/watch?v=gI0wk1CXlsQ
Netflix Case Study
Reference: https://www.youtube.com/watch?v=gI0wk1CXlsQ
Netflix Case Study
Reference: https://www.youtube.com/watch?v=gI0wk1CXlsQ
MA Medical Records
Reference: https://www.youtube.com/watch?v=gI0wk1CXlsQ
Unique ID
Reference: https://www.youtube.com/watch?v=gI0wk1CXlsQ
Differential Privacy
Alter data
Won't we lose truth ?
Differential Privacy
Reference: https://towardsdatascience.com/understanding-differential-privacy-85ce191e198a
Use the data after removing noise (25%)
Plausible Deniability
$$$ LEGAL $$$
Facebook Lawsuit
Reference: https://dailyillini.com/news/2020/02/20/facebook-pay-illinois-550-million/
Facebook Lawsuit
Reference: https://dailyillini.com/news/2020/02/20/facebook-pay-illinois-550-million/
Ethics
Helps Make RIGHT decisions
Be good. Do good.
What is RIGHT ?
Depends...
On ?
On ?
Result of a decision
WRONG decisions result in bias
Microsoft Tay.ai
Microsoft Tay.ai
Reference: https://twitter.com/geraldmellor/status/712880710328139776
How to avoid this ?
- Foresee the complete potential of the solution
- Blacklist sensitive topics
- Need to eliminate such traces at training data level
- Strong ethical testing rules
Google Vision
Google Vision Detection
Reference: https://algorithmwatch.org/en/story/google-vision-racism/
How to avoid this ?
- Know your users better
- Diversity in dataset
- Strong ethical testing rules
Amazon Recruitment
Amazon Recruitment Tool
Reference: https://fortune.com/2018/10/10/amazon-ai-recruitment-bias-women-sexist/
How to avoid this ?
- Never transfer human bias into dataset
- Clearly define and analyze decisions
- Strong ethical testing rules