Reinforcement Learning: Evidence from Stepwise Fairness
This paper explores how reinforcement learning can be made fairer by ensuring that decisions do not discriminate against certain groups at every step of the decision-making process.
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- 1 In this case, our optimization objective, for a particular kind of fairness constraint, is:.
- 2 For any policy π, objective m, transition probabilty p, and underlying objectives m * , p * , it holds that.
- 3 However, those decision systems may demonstrate discrimination against disadvantaged groups due to the biases in the data .
- 4 In particular, they study and show the drawback of myopic optimization together with requiring fairness at each time step, which we refer to as stepwise fairness constraints.
Introduction
Decision making systems trained with real-world data are deployed ubiquitously in our daily life, for example, in regard to credit, education, and medical care. In order to mitigate this issue, many have proposed to impose fairness constraints on the decision, such that certain statistical parity properties are achieved.
Despite the fact that fair learning has been extensively studied, most of this work is in the static setting without considering the sequential feedback effects of decisions.
When there exist sequential feedback effects, even ignoring one-step feedback effects can harm minority groups .
However, our techniques can also be extended in future work to additional types of fairness criteria.
This will involve using a more advanced version of concentration inequality for Markov chain, and we leave this to future work.
Research Question
In this case, our optimization objective, for a particular kind of fairness constraint, is:. For any policy π, objective m, transition probabilty p, and underlying objectives m * , p * , it holds that.
Methodology
We consider a binary decision setting, with training examples that consist of triplets (x, y, ϑ), where x ∈ X is a feature vector, ϑ ∈ Λ is a protected group attribute such as race or gender, and the label y ∈ {0, 1}.For simplicity, we only consider binary sensitive attributes Λ = {α, β}, but our method can also be generalized to deal with multiple sensitive attributes (see Appendix.
Study Design
For the purpose of analysis, we treat p ϑ ‘s as known constants for simplicity; for example, perhaps these proportions are provided by census.
We estimate transition probabilities and rewards using the counting method outlined above.
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Results & Findings
However, those decision systems may demonstrate discrimination against disadvantaged groups due to the biases in the data . In response, advocates to study a discrete-time sequential decision process, where responses to the decisions made at each time step are accompanied by changes in the features and qualifications of the population in the next time step.
- However, those decision systems may demonstrate discrimination against disadvantaged groups due to the biases in the data .
- In response, advocates to study a discrete-time sequential decision process, where responses to the decisions made at each time step are accompanied by changes in the.
- In particular, they study and show the drawback of myopic optimization together with requiring fairness at each time step, which we refer to as stepwise fairness.
- We take a model-based learning approach, and provide practical optimization algorithms that enjoy strong theoretical guarantees in regard to policy optimality and fairness violations as the.
- We summarize our contributions as below:.
Despite the fact that we consider stepwise fairness constraints, our techniques can also be extended in future work to aggregate fairness notions that consider the entire episodic process.
However, those decision systems may demonstrate discrimination against disadvantaged groups due to the biases in the data .
Practical Applications
At the same time, in many scenarios, algorithmic decisions may incur changes in the underlying features or qualification status of individuals, which further feeds back to the decision making process; for example, banks’ decision may induce borrowers to react, for example changing their FICO score by closing credit cards. On one hand, our work could be viewed as a Fair Partially Observable Markov Decision Process (F-POMDP) framework to promote fair.
These are illustrative of other stepwise fairness constraints that could be adopted.
After a decision is made, a possibly group-dependent reward, which may be stochastic, r ϑ : (s, a) → R is obtained by the decision maker.
Preliminaries
The preliminaries outline the binary decision-making framework used in the study, defining the components such as feature vectors, protected group attributes, and reward functions. It sets the stage for the formalization of the decision-making process under fairness constraints.
Source Paper Figures and Captions
![(a). For all (s, a, s ) ∈ S × A × S, there exists a universal constant C > 0, such that p * ϑ (s |s, a) ≥ C for ϑ = {α, β}. (b). For all (x, a) ∈ X × A, there exists a universal constant C, such that π * ϑ (a|x) ≥ C. Assumption 4.1 implies irreducibility of the Markov process and ensures feasibility of our optimization problems (see Appendix C). Recall that at episode k ∈ [K], for each group ϑ, we have n ϑ k ≥ 1 individuals drawn for each group ϑ. And as a concrete exemplified choice of η k and k , we take η k = k -1 3 and k = (kHS) -1 .](https://pdfdigest.com/wp-content/uploads/nala/output/paper_LvSZ_X-b-WnAhAH5_figure_04.png)
Frequently Asked Questions
In this case, our optimization objective, for a particular kind of fairness constraint, is:. For any policy π, objective m, transition probabilty p, and underlying objectives m * , p * , it holds that.
We consider a binary decision setting, with training examples that consist of triplets (x, y, ϑ), where x ∈ X is a feature vector, ϑ ∈ Λ is a protected group attribute such as race or gender, and the label y ∈.
However, those decision systems may demonstrate discrimination against disadvantaged groups due to the biases in the data . In particular, they study and show the drawback of myopic optimization together with requiring fairness at each time step, which we refer to as.
These are illustrative of other stepwise fairness constraints that could be adopted. Our reward function is chosen based on the following considerations: in real life, the decision maker may give a higher loan amount to a candidate with a higher credit score.
However, our techniques can also be extended in future work to additional types of fairness criteria. Despite the fact that we consider stepwise fairness constraints, our techniques can also be extended in future work to aggregate fairness notions that consider the entire.
This paper explores how reinforcement learning can be made fairer by ensuring that decisions do not discriminate against certain groups at every step of the decision-making process.