Achievement and Fragility of Long-term Equitability

by   Andrea Simonetto, et al.

Equipping current decision-making tools with notions of fairness, equitability, or other ethically motivated outcomes, is one of the top priorities in recent research efforts in machine learning, AI, and optimization. In this paper, we investigate how to allocate limited resources to locally interacting communities in a way to maximize a pertinent notion of equitability. In particular, we look at the dynamic setting where the allocation is repeated across multiple periods (e.g., yearly), the local communities evolve in the meantime (driven by the provided allocation), and the allocations are modulated by feedback coming from the communities themselves. We employ recent mathematical tools stemming from data-driven feedback online optimization, by which communities can learn their (possibly unknown) evolution, satisfaction, as well as they can share information with the deciding bodies. We design dynamic policies that converge to an allocation that maximize equitability in the long term. We further demonstrate our model and methodology with realistic examples of healthcare and education subsidies design in Sub-Saharian countries. One of the key empirical takeaways from our setting is that long-term equitability is fragile, in the sense that it can be easily lost when deciding bodies weigh in other factors (e.g., equality in allocation) in the allocation strategy. Moreover, a naive compromise, while not providing significant advantage to the communities, can promote inequality in social outcomes.


page 1

page 2

page 3

page 4


On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning

Most existing notions of algorithmic fairness are one-shot: they ensure ...

Addressing the Long-term Impact of ML Decisions via Policy Regret

Machine Learning (ML) increasingly informs the allocation of opportuniti...

Enforcing Delayed-Impact Fairness Guarantees

Recent research has shown that seemingly fair machine learning models, w...

Long-Term Mentoring for Computer Science Researchers

Early in the pandemic, we – leaders in the research areas of programming...

Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems

Long-term fairness is an important factor of consideration in designing ...

Learn to Allocate Resources in Vehicular Networks

Resource allocation has a direct and profound impact on the performance ...

Dynamic Placement in Refugee Resettlement

Employment outcomes of resettled refugees depend strongly on where they ...

Please sign up or login with your details

Forgot password? Click here to reset