Ethics and Privacy in AI and Big Data: Implementing Responsible Research and Innovation

By Bernd Carsten Stahl, David Wright. 2018

239 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Health, Toxicology and Mutagenesis, Safety Research, Privacy & Security, AI Ethics, Fairness & Society

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Citations in the corpus

Trustworthy Artificial Intelligence: A Review (2022, background): “The first step is to ensure that the data used for training is diverse and does not have any bias [55, 132, 188].”
Ethics of artificial intelligence and robotics (2020, background): “…of persons or groups to a large extent are now a standard staple in data science; they include (relative) anonymisation, access control (plus encryption), differential privacy and other models where computation is carried out without access to full non-encrypted input data (Stahl and Wright 2018).”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, background): “As such circumstances change, and historical data may be imbued with biases, systemic or otherwise, we need to ask whether the historical training data chosen is a fair and appropriate reference point for this decision [56, 86, 91, 101, 127].”
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation (2023, background): “Privacy and security by design: standards should be considered during all life cycles, also from the Responsible Research and Innovation Guidelines [8].”
From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019, methodology): “These include the responsible research and innovation methodology employed by the European Commission’s Human Brain Project (Stahl & Wright, 2018), the field guide to humancentred design,20 and Involve and DeepMind’s guidance on stimulating effective public engagement on the ethics of artificial int”
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey (2020, background): “evel accuracy. Recent interest in XAI, even from governments especially with the European General Data Protection Regulation (GDPR) [21] regulation, shows the important realization of the ethics [22]–[26], trust [27]–[29], bias [30]–[33] of AI, as well as the impact of adversarial examples [34]–[37]”