FairHash: A Fair and Memory/Time-efficient Hashmap that Guarantees Equal Collision Probability Across Demographic Groups

Основные понятия

FairHash is a data-dependent hashmap that guarantees uniform distribution of data across hash buckets, ensuring equal collision probability (false positive rate) for different demographic groups.

Аннотация

The paper introduces FairHash, a novel hashmap data structure that satisfies group fairness in addition to collision probability and single fairness.
Key highlights:

Existing hashmaps, including data-informed CDF-based hashmaps, do not guarantee group fairness, which can lead to disparate treatment of minority groups.
FairHash satisfies three notions of fairness simultaneously: collision probability, single fairness, and pairwise fairness. Pairwise fairness ensures equal ratio of demographic groups across all hash buckets.
The authors propose three families of algorithms to design fair hashmaps: ranking-based, cut-based, and discrepancy-based. These algorithms offer different trade-offs between fairness, memory efficiency, and time efficiency.
Ranking-based algorithms maintain the same time and memory efficiency as CDF-based hashing, while minimizing unfairness. Cut-based algorithms guarantee zero unfairness but incur extra memory overhead. Discrepancy-based algorithms enable balancing pairwise and single fairness.
Extensive experiments on real datasets demonstrate the superiority of FairHash in achieving fairness with negligible performance impact compared to other baselines.

Статистика

The collision probability of a hashmap is ∑︀m
j=1 (nj/n)2, where nj is the number of items in bucket j.
The single fairness for group gi is ∑︀m
j=1 αi,j/(|gi| * nj/n), where αi,j is the number of items from group i in bucket j.
The pairwise fairness for group gi is ∑︀m
j=1 (αi,j/|gi|)2.

Цитаты

"To the best of our knowledge, none of the existing technique guarantees group fairness among different groups of items stored in the hashmap."
"FairHash satisfies all three of them simultaneously."
"Our ranking-based algorithms reduce the unfairness of data-dependant hashmaps without any memory-overhead."

Ключевые выводы из

A Fair and Memory/Time-efficient Hashmap

by Abolfazl Asu... в arxiv.org 04-16-2024

https://arxiv.org/pdf/2307.11355.pdf

Дополнительные вопросы

How can the fairness notions defined in FairHash be extended to other data structures beyond hashmaps

The fairness notions defined in FairHash can be extended to other data structures beyond hashmaps by adapting the principles of group fairness and statistical parity to suit the specific characteristics of those data structures. For instance, in a tree-based data structure like a binary search tree, fairness could be defined in terms of the distribution of nodes across different levels or branches. The concept of equal collision probability and group-wise fairness can be translated into ensuring an equitable distribution of data points or nodes based on certain attributes or characteristics. By customizing the fairness criteria to align with the inherent properties of different data structures, similar notions of fairness can be achieved in a broader range of contexts.

What are the potential challenges in applying FairHash to dynamic datasets where the data distribution changes over time

Applying FairHash to dynamic datasets where the data distribution changes over time poses several potential challenges. One major challenge is maintaining fairness in the face of evolving data patterns. As the distribution of data points shifts, the existing hashmap boundaries may no longer be optimal for ensuring group fairness. This could lead to increased unfairness and a degradation in performance. Additionally, updating the hashmap to accommodate changing data distributions in real-time can be computationally intensive and may impact query latency. Ensuring that the fairness criteria are consistently met despite fluctuations in the dataset requires robust algorithms that can adapt to dynamic changes efficiently without compromising fairness.

How can the trade-off between fairness and other performance metrics like query latency be further optimized in FairHash

To optimize the trade-off between fairness and other performance metrics like query latency in FairHash, several strategies can be employed. One approach is to fine-tune the ranking-based algorithms to minimize unfairness while also optimizing query processing speed. This could involve optimizing the selection of ranking functions or refining the boundary placement strategies to reduce unfairness without significantly impacting query performance. Additionally, incorporating dynamic adjustments based on the workload characteristics and query patterns can help strike a balance between fairness and efficiency. By continuously monitoring and adjusting the hashmap based on real-time data trends, FairHash can adapt to changing conditions while maintaining fairness and optimizing performance metrics.