Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making (1802.01029v1)

Abstract: Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions---like taxation, justice, and child protection---are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and 'discrimination-aware' machine learning---absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the 'street-level bureaucrats' on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications.

Fairness and Accountability Design in Public Sector Algorithms

The paper "Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making" by Veale, Van Kleek, and Binns addresses the critical challenge of integrating fairness and accountability into the design of algorithmic decision-support systems deployed in the public sector. This paper explores the complexities facing machine learning practitioners as they navigate the organizational and institutional frameworks inherent in public administration contexts. Through interviews with practitioners from diverse OECD countries, the paper provides insight into the often overlooked disconnect between current research trajectories in machine learning and the applied needs of those deploying these systems in areas such as taxation, policing, and child welfare.

Main Findings and Contributions

The paper reveals several key themes impacting the effective design and implementation of algorithmic systems in the public sector:

1. Organizational Buy-In and Transparency: Practitioners emphasize the necessity for transparency in machine learning systems to gain organizational buy-in. This need often results in the selection of more interpretable models, such as logistic regression or random forests, over complex alternatives like neural networks. Transparency facilitates trust both within the organization and in interactions with external stakeholders.
2. Discretion and Reliance: The paper highlights varying degrees of discretion and reliance on algorithmic systems among users. Decision-makers sometimes exercise their judgment only when it aligns with algorithmic recommendations, which presents challenges in maintaining equitable and consistent decision-making practices.
3. Data Dynamics and Concept Drift: The issue of data evolution, where the operational reality influences data collection practices, is problematic. Models can inadvertently capture biases from shifting data inputs, especially when feedback loops internal to departmental practices are overlooked.
4. Practical Implications of Discrimination and Gaming: Informants are wary of introducing protected characteristics directly into models. Yet, inherent biases in proxy variables, such as location, underscore the complex nature of fairness in predictive modeling. Additionally, there are concerns regarding potential gaming by both decision-makers and subjects, which would necessitate robust strategies to safeguard the integrity of algorithmic systems.

Implications and Future Directions

The findings of this paper suggest several implications for both theoretical and practical advancements in machine learning research, particularly in public sector applications:

• Integration of Ethical Practices: The results call for the integration of established public administration values into algorithm development, emphasizing interpretability, accountability, and the anticipatory design of systems to address ethical challenges proactively.
• Contextual Adaptation and Collaboration: There is a pronounced need for collaboration between machine learning researchers and public sector practitioners to ensure that systems are developed with an intrinsic understanding of the operational context. This aids in addressing practical challenges and enhances the successful deployment of algorithmic solutions.
• Addressing Concept Drift and Data Feedback: Future work should explore robust methods to monitor and mitigate concept drift, incorporating domain-specific knowledge to facilitate real-time detection of significant data shifts and their potential impact on model performance.

This paper underscores the crucial role that fairness and accountability will play in shaping the future of algorithmic systems within high-stakes public administration domains. It calls for a multi-disciplinary approach that blends technical innovation with an acute awareness of ethical and societal obligations. As the reliance on such technologies increases, addressing these challenges is imperative for ensuring equitable public service delivery and maintaining public trust in algorithmic decision-making processes.