Artificial Intelligence and Administrative Decisions Under Uncertainty

Hermstrüwer, Yoan

doi:10.1007/978-3-030-32361-5_9

Cited by 10 publications

(6 citation statements)

References 34 publications

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“…They allow practitioners to examine the weight of each factor in the produced prediction (Kigerl et al, 2022) and help avoid the "black box" effect (i.e., the opacity that occurs when you are unsure of how you received the outcome classification based on the inputs) that often accompanies machine learning techniques (Burrell, 2016). While practitioners do not have to understand all the mathematical procedures, they must be able to communicate what is behind the risk prediction score (Hermstrüwer, 2019). This awareness can help them inform people under correctional supervision about what affects the algorithm and how decision-makers will interpret and utilize results.…”

Section: Increasing Awareness and Understandingmentioning

confidence: 99%

“…The focus group participants maintained there are costs associated with both error types, and preemptive measures should be taken to address them. Traditionally, developers will overweigh false negatives to reduce their occurrence (Hermstrüwer, 2019; Latessa & Lovins, 2010). Participants argued greater efforts should also be devoted to lowering false-positive rates.…”

Section: Key Takeaways From Focus Groupsmentioning

confidence: 99%

“…Participants acknowledged that simpler models might be less accurate but are easier to explain and employ and may more clearly identify the factors most associated with recidivism. Still, a balance is likely required to provide an accurate tool within the agency’s means to develop and interpret, as reflected in the caution that models be “simpler but not too simple” (Hermstrüwer, 2019, p. 208).…”

Section: Key Takeaways From Focus Groupsmentioning

confidence: 99%

See 2 more Smart Citations

Imperfect Tools: A Research Note on Developing, Applying, and Increasing Understanding of Criminal Justice Risk Assessments

Applegarth

Lewis

Rief

2023

Criminal Justice Policy Review

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This article shares considerations for designing, implementing, and understanding risk assessments used to reduce recidivism of people under community supervision. These insights are gleaned from 27 data scientists who participated in focus groups during the National Institute of Justice’s Recidivism Challenge Winners Symposium. Analyses revealed three primary themes: design considerations, implementation, and increasing awareness and understanding of risk assessments. Critical aspects of the design phase include validating the tool, incorporating field data that account for real-time changes, and adopting strategies to address false positives/negatives and the model’s complexity. Upon the tool’s development, practitioners are recommended to devise an implementation plan, balance attention to risk with client-focused needs, and exercise modest discretion while considering algorithmic results. Recognizing the value predictive instruments bring to decision-making and identifying their limitations is needed to increase understanding for all stakeholders. Collaboration and dialogue between tool developers and practitioners are crucial at every stage.

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Section: Increasing Awareness and Understandingmentioning

confidence: 99%

Section: Key Takeaways From Focus Groupsmentioning

confidence: 99%

Section: Key Takeaways From Focus Groupsmentioning

confidence: 99%

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Imperfect Tools: A Research Note on Developing, Applying, and Increasing Understanding of Criminal Justice Risk Assessments

Applegarth

Lewis

Rief

2023

Criminal Justice Policy Review

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“…Hermstrüwer identified the imperfection of machine learning technology for law enforcement purposes in Germany from the point of view of administrative law. Among the basic problems, there is the issue of sampling administrative cases for AI and the need to form an implicit sample of cases in order to minimize information noise, the formation of unregulated zones due to the behavioral adaptation of the subjects of the offense, as well as other technical traps for AI in the analysis of administrative cases [44].…”

Section: Review Of Doctrinal Legal Studiesmentioning

confidence: 99%

Public Functions Implementation by Artificial Intelligence: Current Practices and Prospects for Common Measures within Particular Periods across Continents and Regions

Atabekov

2023

Societies

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The paper explores practices regarding the implementation by AI of public functions through the analysis of research activities, and administrative and legal regulations of AI in countries of various regions and continents. The hypothesis is that there might be some global trends regarding the AI phenomenon within international institutional vision, research, and national authorities with the goal to suggest common measures within the identification of short, medium, and long-term periods to provide public authorities with trajectories to regulate the AI in terms of its implementation of public functions regarding countries of different regions. The empirical research uses administrative and legal documents, information, and analytical materials from diverse countries. The study uses the comparative method and formal logic tools. The main findings suggest modeling measures within the identification of short, medium, and long-term periods and single out measures that are common to diverse countries, regarding the implementation by AI of public functions.

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“…AI advocates rely on its use in administrative decision-making systems to rationalize them and face the various cognitive and motivational biases of public servants (Hermstrüwer, 2020), thus reducing their margin of appreciation (Veale and Bras, 2019) and preserving a fair and non-arbitrary decision. According to EU Agency for Fundamental Rights, algorithmic data analysis may produce results that could -under certain circumstances -contribute to the reduction of biases and stereotyping and dispel prejudicial attitudes, by reducing reliance on subjective human judgements (European Union Agency for Fundamental Rights, 2020).…”

Section: Ai and The Rule Of Lawmentioning

confidence: 99%

Human Control and Discretion in AI-driven Decision-making in Government

Mitrou

Janssen

Loukis

2021

14th International Conference on Theory and Practice of Electronic Governance

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Traditionally public decision-makers have been given discretion in many of the decisions they have to make in how to comply with legislation and policies. In this way, the context and specific circumstances can be taken into account when making decisions. This enables more acceptable solutions, but at the same time, discretion might result in treating individuals differently. With the advance of AI-based decisions, the role of the decision-makers is changing. The automation might result in fully automated decisions, humansin-the-loop or AI might only be used as recommender systems in which humans have the discretion to deviate from the suggested decision. The predictability of and the accountability of the decisions might vary in these circumstances, although humans always remain accountable. Hence, there is a need for human-control and the decision-makers should be given sufficient authority to control the system and deal with undesired outcomes. In this direction this paper analyzes the degree of discretion and human control needed in AI-driven decision-making in government. Our analysis is based on the legal requirements set/posed to the administration, by the extensive legal frameworks that have been created for its operation, concerning the rule of law, the fairness -non-discrimination, the justifiability and accountability, and the certainty/ predictability.

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Artificial Intelligence and Administrative Decisions Under Uncertainty

Cited by 10 publications

References 34 publications

Imperfect Tools: A Research Note on Developing, Applying, and Increasing Understanding of Criminal Justice Risk Assessments

Imperfect Tools: A Research Note on Developing, Applying, and Increasing Understanding of Criminal Justice Risk Assessments

Public Functions Implementation by Artificial Intelligence: Current Practices and Prospects for Common Measures within Particular Periods across Continents and Regions

Human Control and Discretion in AI-driven Decision-making in Government

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