Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized (2404.08592v3)

Abstract: Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by proposing stochastic procedures that more adequately account for all of the claims that individuals have to allocations of social goods or opportunities.

• The paper demonstrates that incorporating randomness improves fairness by respecting individual claims and reducing systemic inequalities.
• The paper details methodologies like weighted lotteries and bootstrapping to manage both known and uncertain claims in resource distributions.
• The paper highlights that blending randomization with deterministic strategies offers a promising approach to minimizing bias in socio-critical allocations.

Exploring the Necessity of Randomness in Resource Allocation via Machine Learning

Introduction to Randomized Allocations

In the field of machine learning applied to scarce resource allocations, traditional models often implement deterministic algorithms based on quantifiable merits or criteria. These deterministic methods may not always account adequately for individual claims or resultant systemic inequalities. Recent research argues for incorporating randomness into the allocation processes to better handle the inherent uncertainties and ethical considerations when making these decisions.

The Argument for Randomization

The paper posits that randomized resource allocation can address two primary concerns in machine learning contexts:

1. Respect for Individual Claims:
   • Randomization allows for a form of fairness by respecting the individual 'claims' people have on resources, even if they ultimately do not receive them. This method is supported by philosophical arguments suggesting that individuals with claims to a resource should be given chances proportional to the strength of their claims.
   • Deterministic models can sometimes excessively prioritize certain individuals based on possibly flawed or incomplete data, leading to unfair exclusions or repeated denials of other well-deserving individuals.
2. Handling Systemic and Predictive Uncertainties:
   • Algorithms operating in complex social contexts are often fraught with predictive uncertainties. These uncertainties arise from limitations in problem formulation, data accuracy, and representativeness.
   • Randomizing allocations can mitigate the risks of systemic errors or biases getting reinforced over time, affecting the same individuals repeatedly across different decision-making instances.

Exploration of Randomization Techniques

The paper details methods for how to implement randomization, considering scenarios where the decision-maker knows each individual's claim precisely and scenarios where these claims are uncertain.

1. When Claims are Known:
   • Utilize lotteries or similar mechanisms weighted by the strength of claims. This approach ensures that while stronger claims have better chances, those with weaker claims are not entirely excluded.
   • Systemic harms, like homogenization of outcomes leading to patterned inequality or systemic exclusion, can be reduced through randomization strategies that disrupt deterministic selection patterns.
2. When Claims are Uncertain:
   • Randomization can be particularly valuable when the claims are not directly observable or perfectly quantifiable. These scenarios are typical in real-world applications where data to establish claims wholly and accurately is often unavailable or incomplete.
   • Methods such as bootstrapping to estimate prediction variance or employing techniques like conformal prediction to gauge and adjust for uncertainties in model predictions can help in designing fairer allocation mechanisms.

Implications and Future Directions

The integration of randomness into allocation strategies extends beyond mere technical implementation. It suggests a shift towards embracing probabilistic approaches to justice and fairness in decision-making frameworks. This approach acknowledges the complex realities of the social and ethical dimensions within which these algorithms operate.

• Theoretical Implications:
  • Philosophical underpinnings, such as those informing the discussion on fairness and claims, provide a robust basis for considering randomness as a necessary element in certain allocation scenarios.
• Practical Implications:
  • Practically, these methods advocate for a cautious approach towards relying solely on deterministic algorithms, especially in socio-critical applications like healthcare, job allocations, and social welfare.
• Future Research:
  • Future developments could explore the balance between randomness and determinism, especially establishing metrics to quantify fairness and utility losses or gains. Further research might also explore hybrid models that strategically combine deterministic and randomized elements based on context-specific requirements.

By advocating for the thoughtful integration of randomization into algorithmic decision-making for resource allocation, this work prompts a reevaluation of established practices and encourages a more nuanced approach that better captures the diverse and often competing claims in societal resource distribution.