Algorithmic Impact Assessments under the GDPR: Producing Multi-layered Explanations

Kaminski, Margot E.; Malgieri, Gianclaudio

doi:10.2139/ssrn.3456224

Cited by 27 publications

(19 citation statements)

References 31 publications

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“…As the above paragraph illustrates, artificial intelligence models have the potential to yield not only quicker but more accurate predictions based on large amounts of information, including multiple parameters, than manual estimates attempted at a comparable scale. But the development of such models will not be a guaranteed public good without individual and 'systemic' transparency [35], whereby regulators, expert communities, and patient representatives have enough information and involvement to address potential safety and ethical concerns around the processing.…”

Section: Discussion: Automating Healthcare Rationingmentioning

confidence: 99%

‘Leading by Science’ through Covid-19: the GDPR & Automated Decision-Making

Mourby

2021

IJPDS

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The UK government announced in March 2020 that it would create an NHS Covid-19 ‘Data Store’ from information routinely collected as part of the health service. This ‘Store’ would use a number of sources of population data to provide a ‘single source of truth’ about the spread of the coronavirus in England. The initiative illustrates the difficulty of relying on automated processing when making healthcare decisions under the General Data Protection Regulation (GDPR). The end-product of the store, a number of ‘dashboards’ for decision-makers, was intended to include models and simulations developed through artificial intelligence. Decisions made on the basis of these dashboards would be significant, even (it was suggested) to the point of diverting patients and critical resources between hospitals based on their predictions. How these models will be developed, and externally validated, remains unclear. This is an issue if they are intended to be used for decisions which will affect patients so directly and acutely. We have (by default) a right under the GDPR not to be subject to significant decisions based solely on automated decision-making. It is not obvious, at present, whether resource allocation within the NHS could take place in reliance on this automated modelling. The recent A Level debacle illustrates, in the context of education, the risks of basing life-changing decisions on the national application of a single equation. It is worth considering the potential consequences for the health service if the NHS Data Store is used for resource planning as part of the Covid-19 response.

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Section: Discussion: Automating Healthcare Rationingmentioning

confidence: 99%

‘Leading by Science’ through Covid-19: the GDPR & Automated Decision-Making

Mourby

2021

IJPDS

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“…However, it has been argued that the principle of transparency (Article 12 GDPR), as interpreted by the EDPB, is flexible enough to introduce and take advantages of behavioural insights. The latter can support the adopwithout Opening the Black Box: Automated Decisions and the GPDR'; Kaminski , Margot E. and Malgieri, Gianclaudio, Algorithmic Impact Assessments under the GDPR: Producing Multi-layered Explanations (September 18, 2019…”

Section: Discussionmentioning

confidence: 99%

Data Protection, Scientific Research, and the Role of Information

Ducato

2020

SSRN Journal

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This paper aims to critically assess the information duties set out in the General Data Protection Regulation (GDPR) and national adaptations when the purpose of processing is scientific research. Due to the peculiarities of the legal regime applicable to the research context information about the processing plays a crucial role for data subjects. However, the analysis points out that the information obligations, or mandated disclosures, introduced in the GDPR are not entirely satisfying and present some flaws.In addition, the GDPR information duties risk suffering from the same shortcomings usually addressed in the literature about mandated disclosures. The paper argues that the principle of transparency, developed as a "user-centric" concept, can support the adoption of solutions that embed behavioural insights to support the rationale of the information provision better.

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“…Understandings of how the production and distribution of risk through algorithmic technologies leads to sociotechnical impacts of machine learning are still in their infancy, and such technologies are only just beginning to be thought of as capable of being brought under any sort of governance regime. 56 , 57 , 58 , 59 Professional organizations have attempted to mitigate harmful impacts from applying data-driven and machine learning solutions to the problems of the COVID-19 pandemic. Some of these are quite robust, if non-binding, sets of recommendations, 60 while others attend to a limited definition of privacy rights without attending to the range of sociotechnical impacts discussed above.…”

Section: Managing Risk In the Tech Industrymentioning

confidence: 99%

High Tech, High Risk: Tech Ethics Lessons for the COVID-19 Pandemic Response

Moss¹,

Metcalf²

2020

Patterns

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The COVID-19 pandemic has, in a matter of a few short months, drastically reshaped society around the world. Because of the growing perception of machine learning as a technology capable of addressing large problems at scale, machine learning applications have been seen as desirable interventions in mitigating the risks of the pandemic disease. However, machine learning, like many tools of technocratic governance, is deeply implicated in the social production and distribution of risk and the role of machine learning in the production of risk must be considered as engineers and other technologists develop tools for the current crisis. This paper describes the coupling of machine learning and the social production of risk, generally, and in pandemic responses specifically. It goes on to describe the role of risk management in the effort to institutionalize ethics in the technology industry and how such efforts can benefit from a deeper understanding of the social production of risk through machine learning.

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Algorithmic Impact Assessments under the GDPR: Producing Multi-layered Explanations

Cited by 27 publications

References 31 publications

‘Leading by Science’ through Covid-19: the GDPR & Automated Decision-Making

‘Leading by Science’ through Covid-19: the GDPR & Automated Decision-Making

Data Protection, Scientific Research, and the Role of Information

High Tech, High Risk: Tech Ethics Lessons for the COVID-19 Pandemic Response

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