4 research outputs found
Quantitative study about the estimated impact of the AI Act
With the Proposal for a Regulation laying down harmonised rules on Artificial
Intelligence (AI Act) the European Union provides the first regulatory document
that applies to the entire complex of AI systems. While some fear that the
regulation leaves too much room for interpretation and thus bring little
benefit to society, others expect that the regulation is too restrictive and,
thus, blocks progress and innovation, as well as hinders the economic success
of companies within the EU. Without a systematic approach, it is difficult to
assess how it will actually impact the AI landscape. In this paper, we suggest
a systematic approach that we applied on the initial draft of the AI Act that
has been released in April 2021. We went through several iterations of
compiling the list of AI products and projects in and from Germany, which the
Lernende Systeme platform lists, and then classified them according to the AI
Act together with experts from the fields of computer science and law. Our
study shows a need for more concrete formulation, since for some provisions it
is often unclear whether they are applicable in a specific case or not. Apart
from that, it turns out that only about 30\% of the AI systems considered would
be regulated by the AI Act, the rest would be classified as low-risk. However,
as the database is not representative, the results only provide a first
assessment. The process presented can be applied to any collections, and also
repeated when regulations are about to change. This allows fears of over- or
under-regulation to be investigated before the regulations comes into effect.Comment: The raw data and the various categorizations (including the
preprocessing steps) are submitted as wel
What did you see? A study to measure personalization in Google’s search engine
In this paper we present the results of the project “#Datenspende” where during the German election in 2017 more than 4000 people contributed their search results regarding keywords connected to the German election campaign.
Analyzing the donated result lists we prove, that the room for personalization of the search results is very small. Thus the opportunity for the effect mentioned in Eli Pariser’s filter bubble theory to occur in this data is also very small, to a degree that it is negligible. We achieved these results by applying various similarity measures to the result lists that were donated. The first approach using the number of common results as a similarity measure showed that the space for personalization is less than two results out of ten on average when searching for persons and at most four regarding the search for parties. Application of other, more specific measures show that the space is indeed smaller, so that the presence of filter bubbles is not evident.
Moreover this project is also a proof of concept, as it enables society to permanently monitor a search engine’s degree of personalization for any desired search terms. The general design can also be transferred to intermediaries, if appropriate APIs restrict selective access to contents relevant to the study in order to establish a similar degree of trustworthiness
What did you see? A study to measure personalization in Google’s search engine
In this paper we present the results of the project “#Datenspende” where during the German election in 2017 more than 4000 people contributed their search results regarding keywords connected to the German election campaign.
Analyzing the donated result lists we prove, that the room for personalization of the search results is very small. Thus the opportunity for the effect mentioned in Eli Pariser’s filter bubble theory to occur in this data is also very small, to a degree that it is negligible. We achieved these results by applying various similarity measures to the result lists that were donated. The first approach using the number of common results as a similarity measure showed that the space for personalization is less than two results out of ten on average when searching for persons and at most four regarding the search for parties. Application of other, more specific measures show that the space is indeed smaller, so that the presence of filter bubbles is not evident.
Moreover this project is also a proof of concept, as it enables society to permanently monitor a search engine’s degree of personalization for any desired search terms. The general design can also be transferred to intermediaries, if appropriate APIs restrict selective access to contents relevant to the study in order to establish a similar degree of trustworthiness