Towards Operationalizing the Data Protection and Privacy Act 2020: Understanding the Draft Data Protection and Privacy Regulations, 2020

Blazevic, Alice Namuli; Mugalula, Patrick; Wandera, Andrew

doi:10.2139/ssrn.3776353

Cited by 1 publication

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) Consent for data usage [3,56,60] (2) Data protection [3,60,61] (3) Control over data / ability to restrict processing [56, 60] (4) Right to rectification [3,56,60] (5) Right to erase the data [3,56,60] (6) Right of access by data subject, data agency [56,167] • Written declaration of consent [56] • Description of what data is collected [125] • Description of how data is handled [125] • Purpose statement of data collection [125] • Statement of how long the data is kept [125] • Form and submission mechanisms to object data collection and to make complaints [27] • Obfuscation of data [3] Table 1. Illustration of how to move from values, to criteria and their manifestations for privacy.…”

Section: Value Criteria Manifestationsmentioning

confidence: 99%

“…26 The ratio of false positives and negatives has to be the same for both groups [126]. 27 For any probability score S, the probability of correctly belonging to the positive class should be the same for both the protected and unprotected group [126]. 28 It deals with the fairness of the decision-making process that leads to the outcome in question [77].…”

Section: Valuementioning

confidence: 99%

“…These signifiers are necessary for users to fully understand what sharing their data entails. Process-oriented practices include the obfuscation of data [3] and forms and submission mechanisms to object data collection and make complaints [27].…”

mentioning

confidence: 99%

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Towards a multi-stakeholder value-based assessment framework for algorithmic systems

Yurrita,

Murray-Rust,

Balayn

et al. 2022

Preprint

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In an effort to regulate Machine Learning-driven (ML) systems, current auditing processes mostly focus on detecting harmful algorithmic biases. While these strategies have proven to be impactful, some values outlined in documents dealing with ethics in ML-driven systems are still underrepresented in auditing processes. Such unaddressed values mainly deal with contextual factors that cannot be easily quantified. In this paper, we develop a value-based assessment framework that is not limited to bias auditing and that covers prominent ethical principles for algorithmic systems. Our framework presents a circular arrangement of values with two bipolar dimensions that make common motivations and potential tensions explicit. In order to operationalize these high-level principles, values are then broken down into specific criteria and their manifestations. However, some of these value-specific criteria are mutually exclusive and require negotiation. As opposed to some other auditing frameworks that merely rely on ML researchers' and practitioners' input, we argue that it is necessary to include stakeholders that present diverse standpoints to systematically negotiate and consolidate value and criteria tensions. To that end, we map stakeholders with different insight needs, and assign tailored means for communicating value manifestations to them. We, therefore, contribute to current ML auditing practices with an assessment framework that visualizes closeness and tensions between values and we give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.CCS Concepts: • General and reference → Evaluation; • Human-centered computing → Human computer interaction (HCI);• Social and professional topics → User characteristics.

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