Introduction to special issue algorithmic transparency in government: Towards a multi-level perspective

Giest, Sarah; Grimmelikhuijsen, Stephan

doi:10.3233/ip-200010

Cited by 17 publications

(11 citation statements)

References 33 publications

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“…The idea that XAI increases user trust is grounded in the reasoning that people will not trust systems they do not understand (Giest & Grimmelikhuijsen, 2020). Unexplained AI systems are opaque and not understandable to decision makers (Burrell, 2016).…”

Section: Explainable Ai and Trustworthinessmentioning

confidence: 99%

“…Street-level bureaucrats, confronted with such a dilemma, have to decide: do they follow the AI recommendation or their own intuitive professional judgment? Scholars have noted that the empirical knowledge of the impact of AI on street-level bureaucrats' behavior is limited (Giest & Grimmelikhuijsen, 2020;Peeters, 2020). Therefore, the first aim of this article is to investigate what happens when AI recommendations are congruent or incongruent with a street-level bureaucrats' intuitive professional knowledge, that is, their expertise based on training activities and on-the-ground experience (Maynard-Moody & Musheno, 2000).…”

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confidence: 99%

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‘Just like I thought’: Street‐level bureaucrats trust AI recommendations if they confirm their professional judgment

Selten

Robeer

Grimmelikhuijsen

2023

Public Administration Review

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Artificial Intelligence is increasingly used to support and improve street‐level decision‐making, but empirical evidence on how street‐level bureaucrats' work is affected by AI technologies is scarce. We investigate how AI recommendations affect street‐level bureaucrats' decision‐making and if explainable AI increases trust in such recommendations. We experimentally tested a realistic mock predictive policing system in a sample of Dutch police officers using a 2 × 2 factorial design. We found that police officers trust and follow AI recommendations that are congruent with their intuitive professional judgment. We found no effect of explanations on trust in AI recommendations. We conclude that police officers do not blindly trust AI technologies, but follow AI recommendations that confirm what they already thought. This highlights the potential of street‐level discretion in correcting faulty AI recommendations on the one hand, but, on the other hand, poses serious limits to the hope that fair AI systems can correct human biases.

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Section: Explainable Ai and Trustworthinessmentioning

confidence: 99%

mentioning

confidence: 99%

‘Just like I thought’: Street‐level bureaucrats trust AI recommendations if they confirm their professional judgment

Selten

Robeer

Grimmelikhuijsen

2023

Public Administration Review

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“…Lepri et al 2018), computer science (Miller 2019) and more recently in public administration (Busuioc 2020), have called for more transparent algorithms to render them more accountable. The literature on algorithmic transparency generally considers two elements: accessibility and explainability (Giest and Grimmelikhuijsen 2020) which are summarized in table 1.…”

Section: Algorithmic Transparency and Street‐level Bureaucracymentioning

confidence: 99%

Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making

Grimmelikhuijsen

2022

Public Administration Review

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Algorithms based on Artificial Intelligence technologies are slowly transforming street-level bureaucracies, yet a lack of algorithmic transparency may jeopardize citizen trust. Based on procedural fairness theory, this article hypothesizes that two core elements of algorithmic transparency (accessibility and explainability) are crucial to strengthening the perceived trustworthiness of street-level decisionmaking. This is tested in one experimental scenario with low discretion (a denied visa application) and one scenario with high discretion (a suspicion of welfare fraud). The results show that: (1) explainability has a more pronounced effect on trust than the accessibility of the algorithm; (2) the effect of algorithmic transparency not only pertains to trust in the algorithm itself but also-partially-to trust in the human decision-maker; (3) the effects of algorithmic transparency are not robust across decision context. These findings imply that transparency-asaccessibility is insufficient to foster citizen trust. Algorithmic explainability must be addressed to maintain and foster trustworthiness algorithmic decision-making. Evidence for Practice• Algorithmic transparency consists of accessibility and explainability.• This study finds that accessibility, though important, is not sufficient to foster trust.• Governments must address the explainability of algorithmic decision-making to earn citizen trust in algorithms and bureaucrats working with them.

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“…In addition, algorithmic governance is criticized for its lack of accountability. The ways in which algorithms function, often defy comprehension by public officials using them and citizens subject to algorithmic decisions and services (Giest and Grimmelikhuijsen, 2020). In fact, even those who have designed and trained the machine learning models may no longer be able to interpret and explain the obtained results (Burrell, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In response to these issues, research has called for more transparency and regulation of algorithms (Busuoic, 2020; Giest and Grimmelikhuijsen, 2020; Yeung and Lodge, 2019). These are useful, but ex post solutions in which the development of algorithms remains a rather autonomous process that tends to outpace governance.…”

Section: Introductionmentioning

confidence: 99%

Co-designing algorithms for governance: Ensuring responsible and accountable algorithmic management of refugee camp supplies

Dekker

Koot²,

Birbil

et al. 2022

Big Data & Society

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There is increasing criticism on the use of big data and algorithms in public governance. Studies revealed that algorithms may reinforce existing biases and defy scrutiny by public officials using them and citizens subject to algorithmic decisions and services. In response, scholars have called for more algorithmic transparency and regulation. These are useful, but ex post solutions in which the development of algorithms remains a rather autonomous process. This paper argues that co-design of algorithms with relevant stakeholders from government and society is another means to achieve responsible and accountable algorithms that is largely overlooked in the literature. We present a case study of the development of an algorithmic tool to estimate the populations of refugee camps to manage the delivery of emergency supplies. This case study demonstrates how in different stages of development of the tool—data selection and pre-processing, training of the algorithm and post-processing and adoption—inclusion of knowledge from the field led to changes to the algorithm. Co-design supported responsibility of the algorithm in the selection of big data sources and in preventing reinforcement of biases. It contributed to accountability of the algorithm by making the estimations transparent and explicable to its users. They were able to use the tool for fitting purposes and used their discretion in the interpretation of the results. It is yet unclear whether this eventually led to better servicing of refugee camps.

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Introduction to special issue algorithmic transparency in government: Towards a multi-level perspective

Abstract: ??

Cited by 17 publications

References 33 publications

‘Just like I thought’: Street‐level bureaucrats trust AI recommendations if they confirm their professional judgment

‘Just like I thought’: Street‐level bureaucrats trust AI recommendations if they confirm their professional judgment

Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making

Co-designing algorithms for governance: Ensuring responsible and accountable algorithmic management of refugee camp supplies

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