The Data Protection Impact Assessment as a Tool to Enforce Non-discriminatory AI

Ivanova, Yordanka

doi:10.1007/978-3-030-55196-4_1

Cited by 9 publications

(8 citation statements)

References 16 publications

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“…We observed that the methodologies did not address certain requirements; however, it is notable that the authors concentrated on specific requirements in their proposals. For example, in S10 [48], Art. 35(7)(a) is considered out of scope because the specific outcome of the systematic envisaged processing relies on the AI model and its designated use; nevertheless, they address other requirements.…”

Section: (A) a Systematic Description Of The Envisaged Processing Ope...mentioning

confidence: 99%

“…Ivanova [48] proposed a DPIA methodology that takes into context the assessment of risks that emerge from the use of AI systems. The author outlines the process and application of the methodology, specifically to AI, which involves systematically assessing data processing operations, identifying potential risks to individuals' rights and freedoms, and implementing measures to mitigate those risks.…”

Section: S10mentioning

confidence: 99%

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On the Evaluation of Privacy Impact Assessment and Privacy Risk Assessment Methodologies: A Systematic Literature Review

Wairimu,

Iwaya,

Fritsch

et al. 2024

IEEE Access

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Assessing privacy risks and incorporating privacy measures from the onset requires a comprehensive understanding of potential impacts on data subjects. Privacy Impact Assessments (PIAs) offer a systematic methodology for such purposes, which are closely related to Data Protection Impact Assessments (DPIAs), particularly outlined in Article 35 of the General Data Protection Regulation (GDPR). The core of a PIA is a Privacy Risk Assessment (PRA). PRAs can be integrated as part of full-fledged PIAs or independently developed to support PIA processes. Although these methodologies have been identified as essential enablers of privacy by design, their effectiveness has been criticized because of the lack of evidence of their rigorous and systematic evaluation. Hence, we conducted a Systematic Literature Review (SLR) to identify published PIA and PRA methodologies and assess how and to what extent they have been scientifically validated or evaluated. We found that these methodologies are rarely evaluated for their performance in practice, and most of them have only been validated in limited studies. Most validation evidence is found with PRA methodologies. Of the evaluated methodologies, PIAs were the most evaluated, where case studies were the predominant evaluation method. These evaluated methodologies can be easily transferred to an industrial setting or used by practitioners, as they provide evidence of their use in practice. In addition, the findings in this study can be used to inform researchers of the current state-of-the-art, and practitioners can understand the benefits and current limitations of the methodologies and adopt evidencebased practices.

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Section: (A) a Systematic Description Of The Envisaged Processing Ope...mentioning

confidence: 99%

Section: S10mentioning

confidence: 99%

On the Evaluation of Privacy Impact Assessment and Privacy Risk Assessment Methodologies: A Systematic Literature Review

Wairimu,

Iwaya,

Fritsch

et al. 2024

IEEE Access

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“…A number of assessment tools have been developed to evaluate new technologies, systems, or programs in regard to data protection, online safety, and algorithmic bias. Data Protection Impact Assessment (DPIA) is a process to help data controllers to identify, assess, and reduce the data protection risks of a project (Information Commisioner’s Office, 2022; Ivanova, 2020). Other tools have also been created for public agencies and developers to identify and minimise bias in algorithmic decision-making systems (Duarte, 2017; Reisman et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

TrustScapes: A Visualisation Tool to Capture Stakeholders’ Concerns and Recommendations About Data Protection, Algorithmic Bias, and Online Safety

Ito‐Jaeger

Lane

Dowthwaite

et al. 2023

International Journal of Qualitative Methods

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This paper presents a new methodological approach, TrustScapes, an open access tool designed to identify and visualise stakeholders’ concerns and policy recommendations on data protection, algorithmic bias, and online safety for a fairer and more trustworthy online world. We first describe how the tool was co-created with young people and other stakeholders through a series of workshops. We then present two sets of TrustScapes focus groups to illustrate how the tool can be used, and the data analysed. The paper then provides the methodological insights, including the strengths of the TrustScapes and the lessons for future research using TrustScapes. A key strength of this method is that it allows people to visualise their ideas and thoughts on the worksheet, using the keywords and sketches provided. The flexibility in the mode of delivery is another strength of the TrustScapes method. The TrustScapes focus groups can be conducted in a relatively short time (1.5–2 hours), either in person or online depending on the participants’ needs, geological locations, and practicality. Our experience with the TrustScapes offers some lessons (related to the data collection and analysis) for researchers who wish to use this method in the future. Finally, we describe how the outcomes from the TrustScapes focus groups should help to inform future policy decisions.

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“…The field of technology assessment [52,53] can be described as a framework for developing impact assessments independent of particular technologies or expected fields of impact. In addition, there are a number of more specific types of impact assessment with high relevance to AI, such as privacy impact assessment [35,36,64], data protection impact assessment [65] or ICT ethics impact assessment [109].…”

Section: Introductionmentioning

confidence: 99%

Assessing the ethical and social concerns of artificial intelligence in neuroinformatics research: an empirical test of the European Union Assessment List for Trustworthy AI (ALTAI)

Stahl

Leach

2022

AI Ethics

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Ethical and social concerns are a key obstacle to the adoption of artificial intelligence (AI) in the life sciences and beyond. The discussion of these issues has intensified in recent years and led to a number of approaches, tools and initiatives. Key amongst them is the idea of ex-ante impact assessments that aim to identify issues at the early stages of development. One prominent example of such ex-ante impact assessment is the European Union's (EU) Assessment list for Trustworthy AI (ALTAI). This article uses the findings of a large-scale application of the ALTAI to a large neuro-informatics project as an exemplar to demonstrate the effectiveness and limitations of the ALTAI in practice. The article shows that ex-ante impact assessment has the potential to help identify and address ethical and social issues. However, they need to be understood as part of a broader socio-technical ecosystem of AI. For ALTAI and related approaches to be useful in bio-medical research, they should be interpreted from a systems theory perspective which allows for their integration into the rich set of tools, legislation and approaches. The paper argues that ex-ante impact assessments have the best chance of being successful if seen applied in conjunction with other approaches in the context of the overall AI ecosystem.

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The Data Protection Impact Assessment as a Tool to Enforce Non-discriminatory AI

Cited by 9 publications

References 16 publications

On the Evaluation of Privacy Impact Assessment and Privacy Risk Assessment Methodologies: A Systematic Literature Review

On the Evaluation of Privacy Impact Assessment and Privacy Risk Assessment Methodologies: A Systematic Literature Review

TrustScapes: A Visualisation Tool to Capture Stakeholders’ Concerns and Recommendations About Data Protection, Algorithmic Bias, and Online Safety

Assessing the ethical and social concerns of artificial intelligence in neuroinformatics research: an empirical test of the European Union Assessment List for Trustworthy AI (ALTAI)

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