Optimizing Long-term Social Welfare in Recommender Systems
Mladenov et al. (ICML 2020)
MLRG, Winter 2021: Responsible ML
Betty Shea, 2021-11-24
*picture from AP
MLRG theme: Responsible ML
- Bias to address
- What is unfair?
- What is fair?
Introduction presentation
- ranking bias
- discrimination
- equal opportunity
MLRG theme: Responsible ML
-
Bias to addressIntroduce ranking bias -
What is unfair?Social welfare -
What is fair?Unequal opportunity
Today's paper
Classic ethics question
Individual rights
Common good
* picture from AFP
* picture from socialprotection.org
Taxation / social insurance
Main points from paper
- better algorithm for recommender systems
- unrealistic to consider individual utility in isolation
- myopic matching policies lead to suboptimal outcomes
- connection to outcomes that are `fairer in a utilitarian sense'
Notation
Setting
Notation
Process
Notation
Recommender system policy
Notation
Per user
Per provider
Provider abandons RS platform if viability threshold not met.
1-d examples
1-d examples
- Three content providers each with viability = 2
- Six users with utility based on distance to provider
1-d examples
- Myopic RS matching
1-d examples
- Result
- users 1, 2 and 3 --> provider 1
- users 4 and 5 --> provider 2
- user 6 --> provider 3
- Provider 3 goes out of business
- Maximizes total utility: 10 + 2
1-d examples
- Result of subsequent rounds
- users 1, 2 and 3 --> provider 1
- users 4, 5 and 6--> provider 2
- Per round total utility: 8 + 2
- user 6's utility goes from 2 to 0 without provider 3
1-d examples
- Non-myopic policy could be
- users 1 and 2 --> provider 1
- users 3 and 4 --> provider 2
- users 5 and 6 --> provider 3
- Per round total utility: 10 - 2
- Users 3 and 5 gives up 2 utility per round
Fairness?
- subsidize provider 3
- slightly lower rewards for users 3 and 5 at every timestep for perpetuity
- much higher reward for user 6 for perpetuity
Social welfare
- group every T timesteps into an epoch
- at epoch k under policy
- long-run social welfare
Equilibrium
- subset of providers that are viable in the long run
- many equilibria - find the one that maximizes long-run social welfare
- achieve with a single rule to apply across all epochs
Objective (for any epoch)
(expectation over user query distribution, timesteps within epoch and policy)
Objective (for any epoch)
- maximize social welfare
- ensure that all providers remain viable
- how does this translate to optimality over infinite epochs?
Solving the problem
- linear programming
- polynomial time for 1/ approximation
If utility function is additive
For non-linear utility functions
- linearize
Experiments
- linear programming solution to the welfare objective (LP-RS)
- three datasets
- synthetic data
- Movielens movie ratings
- SNAP 2010 Twitter follower
- myopic policies underperform LP-RS
Model refinements
- fairness to individual users
- incomplete information
- robustness to changes in query distributions
- full RL approach
Fairness to individual users
- long-run utility for under
- denote as the best possible policy for
- define fairness in terms of regret
Fairness to individual users
- regret of under
- maximum regret under
- add a penalty term to objective based on MR
Comments?
- constraint in objective does not take into account relative cost of subsidizing providers
- regret benchmark (optimal policy for individual user) a bit unrealistic
- penalizing maximum regret does not necessarily protect individuals and requires picking regret-tradeoff parameter values
Optimizing Long-term Social Welfare in Recommender Systems Mladenov et al. (ICML 2020) MLRG, Winter 2021: Responsible ML Betty Shea, 2021-11-24 *picture from AP
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