Hunton Privacy
FEBRUARY 23, 2024
On February 21, 2024, the Centre for Information Policy Leadership at Hunton Andrews Kurth LLP (“CIPL”) published a white paper on Building Accountable AI Programs: Mapping Emerging Best Practices to the CIPL Accountability Framework. Read the white paper on accountable AI programs.
Hunton Privacy
FEBRUARY 6, 2024
On February 6, 2024, the UK government published a response to the consultation on its AI Regulation White Paper, which the UK government originally published in March 2023. A 12-week consultation on the White Paper was then held and this response summarizes the feedback and proposed next steps.
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Hunton Privacy
FEBRUARY 28, 2024
The paper will contribute to the constructive momentum that has been building among multiple stakeholders and countries, to create pragmatic and long-term solutions for accountable, trusted and sustainable international data transfers. CIPL has worked on projects promoting solutions for safe cross-border data transfers.
Krebs on Security
DECEMBER 29, 2023
Nor do I wish to hold forth about whatever cyber horrors may await us in 2024. as a young teen, I inherited a largish paper route handed down from my elder siblings). ” I was 23 years old, and I had no clue what to say except to tell him that paper route story, and that I’d already been working for him for half my life.
Data Protection Report
MARCH 4, 2024
The authors acknowledge the assistance of Salma Khatab, paralegal, in researching and preparing some aspects of this blog The UK Department for Science, Innovation, and Technology (DSIT) has published its response to its consultation on its white paper, ‘A pro innovation approach to AI regulation ’ (the Response). Contestability and redress.
Schneier on Security
JANUARY 24, 2024
The result is that when the prompt indicated “2023,” the model wrote secure code. For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024.
Schneier on Security
FEBRUARY 7, 2024
For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024. To study this question, we construct proof-of-concept examples of deceptive behavior in large language models (LLMs).
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