440 research outputs found
Minimax quantum state discrimination
We derive the optimal measurement for quantum state discrimination without a
priori probabilities, i.e. in a minimax strategy instead of the usually
considered Bayesian one. We consider both minimal-error and unambiguous
discrimination problems, and provide the relation between the optimal
measurements according to the two schemes. We show that there are instances in
which the minimum risk cannot be achieved by an orthogonal measurement, and
this is a common feature of the minimax estimation strategy.Comment: 5 pages, no figures; the title is changed. Some improvements.
Corollary 1 has been corrected (now Remark 1). Phys. Rev. A in pres
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When users control the algorithms: Values expressed in practices on the twitter platform
Recent interest in ethical AI has brought a slew of values, including fairness, into conversations about technology design. Research in the area of algorithmic fairness tends to be rooted in questions of distribution that can be subject to precise formalism and technical implementation. We seek to expand this conversation to include the experiences of people subject to algorithmic classification and decision-making. By examining tweets about the “Twitter algorithm” we consider the wide range of concerns and desires Twitter users express. We find a concern with fairness (narrowly construed) is present, particularly in the ways users complain that the platform enacts a political bias against conservatives. However, we find another important category of concern, evident in attempts to exert control over the algorithm. Twitter users who seek control do so for a variety of reasons, many well justified. We argue for the need for better and clearer definitions of what constitutes legitimate and illegitimate control over algorithmic processes and to consider support for users who wish to enact their own collective choices
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