What is the impact of algorithmic decision-making on fairness and transparency in public services?

Introduction

Algorithmic decision-making (ADM) is increasingly being integrated into public services, promising more objective and efficient decision processes. However, this shift raises significant concerns about fairness and transparency. This synthesis examines the impact of ADM on these two critical aspects, drawing insights from multiple research studies.

Key Insights

Perceived Fairness and Outcome Control:
- Allowing users to interactively adjust algorithmic decisions improves perceived fairness by making the limitations of decisions more apparent and incorporating human elements into the final decision-making process.
Transparency and Accountability:
- Transparency in ADM can be enhanced by providing clear explanations of the decision-making process and outcomes, which helps in building trust and accountability .
- However, transparency alone does not always increase perceived fairness; it can have mixed effects, sometimes reducing algorithmic accountability.
Bias and Discrimination:
- ADM systems can potentially reduce human biases but also risk enhancing discrimination and creating information and power asymmetries if not properly managed .
- The use of sensitive attributes in decision-making is often identified as a primary cause of perceived unfairness.
Trust and Human Discretion:
- The integration of ADM in public services, such as job placement, affects the trust relationship between citizens and caseworkers. The lack of human discretion in ADM can lead to a "black box" perception, reducing trust.
- Trust in ADM is also influenced by the perceived appropriateness of the factors used in decision-making and the perceived fairness of the outcomes .
Impact of Algorithmic Fairness on Different Demographics:
- People's perceptions of fairness in ADM are influenced by individual differences such as education level and gender. Favorable outcomes for individuals can overshadow the recognition of biases against other demographic groups.
Procedural Fairness and Information Transparency:
- Employees perceive lower procedural fairness in ADM compared to human decision-making, primarily due to lower information transparency. An inclusive organizational climate can mitigate this effect.

Conclusion

The integration of algorithmic decision-making in public services presents a complex landscape for fairness and transparency. While ADM can potentially reduce human biases, it also risks enhancing discrimination and creating opacity. Transparency and outcome control are crucial for improving perceived fairness and trust, but they must be carefully managed to avoid mixed effects. Individual differences and the context of use significantly influence perceptions of fairness, highlighting the need for inclusive and well-explained ADM processes. Overall, a balanced approach that combines algorithmic efficiency with human oversight and clear communication is essential for fostering fairness and transparency in public services.

What is the impact of algorithmic decision-making on fairness and transparency in public services?

Summary

Introduction

Key Insights

Conclusion

Sources and full results

Procedural Justice in Algorithmic Fairness

Transparency and outcome control in algorithmic decision-making improve perceived fairness, while standards clarity alone does not, and outcome explanation has mixed effects.

Fair, Transparent, and Accountable Algorithmic Decision-making Processes

Algorithmic decision-making can potentially be fairer and more transparent when partnered with multi-disciplinary teams and involving citizens.

Perception of fairness in algorithmic decisions: Future developers' perspective

Students in fields adjacent to algorithm development perceive fairness in algorithmic decision-making as objective factors, sensitive attributes as causes of unfairness, and increased transparency as a preferred strategy for transparency.

Algorithmic Decision Making in Public Services: A CSCW-Perspective

Algorithmic decision making in public services like job placement may impact citizens' trust and transparency, affecting their relationship with caseworkers and affecting casework processes.

Fairness, Equality, and Power in Algorithmic Decision-Making

Algorithmic decision-making should consider inequality and power distribution, rather than just focusing on fairness, to address potential biases and ensure ethical decision-making.

Fairness in Algorithmic Decision Making

Fairness in algorithmic decision making is crucial for societal impact domains like banking, recruiting, judiciary, and journalism, and this tutorial introduces related literature and techniques for achieving fair outcomes.

Can AI Algorithmic Decision-making Improve Employees’ Perception of Procedural Fairness?

AI algorithmic decision-making in HR decision-making leads to lower employee perception of procedural fairness, with information transparency playing a key role, but an inclusive climate can weaken this effect.

Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences

People perceive algorithms as more fair when they predict in their favor, with factors like education level, gender, and development procedure influencing this perception.

Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems

Quantitative Input Influence (QII) measures improve transparency in machine learning decision-making systems, providing better explanations than standard associative measures and allowing for differentially private reports while preserving accuracy.

Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability?

Full transparency for oversight bodies is the only feasible option for machine learning decision-making, while making decisions more understandable.

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