Equity of Attention: Amortizing Individual Fairness in Rankings (1805.01788v1)

Abstract: Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality.

• The paper introduces a fairness metric called equity of attention, ensuring each subject’s cumulative exposure matches its relevance.
• The paper formulates amortized fairness as an online optimization problem solved through an integer linear program to mitigate position bias.
• Empirical tests on synthetic and real-world Airbnb data show that the method significantly improves fairness while maintaining ranking quality.

Overview of "Equity of Attention: Amortizing Individual Fairness in Rankings"

The paper "Equity of Attention: Amortizing Individual Fairness in Rankings," presented at SIGIR '18, addresses the pervasive issue of position bias in ranked systems, particularly within the context of platforms where rankings can translate into significant economic impact. The authors, Asia J. Biega, Krishna P. Gummadi, and Gerhard Weikum, propose a novel framework to mitigate the disparity between the attention received by subjects at lower ranks and their relevance, an issue that is critical in systems ranging from search engines to sharing economy platforms.

Core Contributions

1. Individual-Level Fairness: This paper introduces a new fairness metric called "equity of attention," which aims to ensure that each individual subject within a ranked list receives attention proportional to their relevance. This approach extends beyond traditional group fairness measures to focus on fairness at the individual level, thereby subsuming group fairness considerations.
2. Amortized Fairness: Recognizing that ensuring fairness in a single ranking is impractical due to inherent position bias, the authors propose amortizing fairness over a series of rankings. This concept involves balancing the attention subjects receive over time and multiple rankings, aligning their cumulative attention with their cumulative relevance.
3. Optimization Problem Formulation: The authors cast this amortized fairness challenge as an online optimization problem, solvable through an integer linear program (ILP). Their framework allows for the iterative adjustment of rankings to minimize the difference between deserved and received attention, subject to manageable constraints on ranking quality.
4. Empirical Evaluation: The paper provides an experimental assessment using both synthetic datasets and real-world data from Airbnb, demonstrating that significant unfairness exists in typical ranking scenarios. Their evaluations show that their approach can substantially improve fairness without considerable detriment to ranking quality, even under different attention distribution models, such as singular and geometric distributions.

Implications and Future Work

The concepts introduced in this paper have substantial implications for the design and operation of systems where rankings can affect access to resources and opportunities. By addressing the granular, individual effects of position bias, this research enhances the fairness and ethical considerations of algorithmic decision-making processes.

The proposed methodologies could be extended to a variety of applications, from online marketplaces to recommendation systems, where an equitable distribution of attention could significantly impact users' engagement and satisfaction. In particular, future work could explore the calibration of ranker scores in economically sensitive domains or explore the psychological aspects of relevance and attention.

Moreover, the paper opens potential investigations into fairness definitions beyond equity of attention, possibly examining the trade-offs between amortizing fairness and other fairness principles like equality or need-based attention distribution. Furthermore, integrating such frameworks with other machine learning fairness approaches could yield comprehensive solutions that simultaneously address multiple dimensions of fairness in algorithmic systems.

In conclusion, this paper offers a significant step toward understanding and addressing the nuanced issue of fairness in ranked systems, providing both foundational theory and practical implementations that could redefine fairness considerations in algorithmic design.