I love the @TheLeadDev crowd so much. They just gave @carlaprvieira applause and whooping for explaining it’s her first time in the US and her first international conference talk here at #LeadDevSanFrancisco 🥰🥰🥰💖💖💖
“Bias is like a virus that travels and is replicated by machine learning models.”
@carlaprvieira #LeadDevSanFrancisco
Potential Harms Caused by AI Systems:
1) Bias & discrimination
2) Denial of individual autonomic rights
3) Non-transparent, unexplainable or unjustifiable outcomes
4) Invasions of privacy
5) Unreliable, unsafe or poor quality outcomes
@carlaprvieira #LeadDevSanFrancisco
Bias sneaks in to machine learning in a number of places
@carlaprvieira #LeadDevSanFrancisco
Types of Bias in ML:
- historical
- representation
- measurement
- learning
- aggregation
- evaluation
- deployment
@carlaprvieira #LeadDevSanFrancisco
“Deployment bias arises when there is a mismatch between the problem a model is intended to solve and the way in which it is actually used”
@carlaprvieira #LeadDevSanFrancisco
“Algorithms cause the illusion of neutrality”
“This is called Mathwashing. When power and bias hide behind the facade of “neutral” math” — @fredbenenson
@carlaprvieira #LeadDevSanFrancisco
Possible solutions:
- fairness (statistical analysis)
- explainable and interpretable AI (explainability issues growing with advent of deep learning)
- product thinking approach (it’s gonna be a product at some point)
- ethical to proceed?
@carlaprvieira #LeadDevSanFrancisco
“Data science is a team sport that is highly interdisciplinary.
Diversity of perspective matters!
1) Technology is not free of humans
2) Math can obscure the human element & give illusion of objectivity
3) Every single human is biased”
@carlaprvieira #LeadDevSanFrancisco
Share this Scrolly Tale with your friends.
A Scrolly Tale is a new way to read Twitter threads with a more visually immersive experience.
Discover more beautiful Scrolly Tales like this.