5 research outputs found
The Ethical Need for Watermarks in Machine-Generated Language
Watermarks should be introduced in the natural language outputs of AI systems
in order to maintain the distinction between human and machine-generated text.
The ethical imperative to not blur this distinction arises from the asemantic
nature of large language models and from human projections of emotional and
cognitive states on machines, possibly leading to manipulation, spreading
falsehoods or emotional distress. Enforcing this distinction requires
unintrusive, yet easily accessible marks of the machine origin. We propose to
implement a code based on equidistant letter sequences. While no such code
exists in human-written texts, its appearance in machine-generated ones would
prove helpful for ethical reasons
The Users' Perspective on the Privacy-Utility Trade-offs in Health Recommender Systems
Privacy is a major good for users of personalized services such as
recommender systems. When applied to the field of health informatics, privacy
concerns of users may be amplified, but the possible utility of such services
is also high. Despite availability of technologies such as k-anonymity,
differential privacy, privacy-aware recommendation, and personalized privacy
trade-offs, little research has been conducted on the users' willingness to
share health data for usage in such systems. In two conjoint-decision studies
(sample size n=521), we investigate importance and utility of
privacy-preserving techniques related to sharing of personal health data for
k-anonymity and differential privacy. Users were asked to pick a preferred
sharing scenario depending on the recipient of the data, the benefit of sharing
data, the type of data, and the parameterized privacy. Users disagreed with
sharing data for commercial purposes regarding mental illnesses and with high
de-anonymization risks but showed little concern when data is used for
scientific purposes and is related to physical illnesses. Suggestions for
health recommender system development are derived from the findings.Comment: 32 pages, 12 figure