Partial commitment – "Try before you buy" and "Buyer’s remorse" for personal data in Big Data & Machine learning

• The concept of partialcommitment is discussed in the context of personal privacy management in datascience. Uncommitted, promiscuous or partially committed user’s data may eitherhave a negative impact on model or data quality, or it may impose higherprivacy compliance cost on data service providers. Many Big Data (BD) andMachine Learning (ML) scenarios involve the collection and processing of largevolumes of person-related data. Data is gathered about many individuals as wellas about many parameters in individuals. ML and BD both spend considerable resourceson model building, learning, and data handling. It is therefore important toany BD/ML system that the input data trained and processed is of high quality,represents the use case, and is legally processes in the system. Additionalcost is imposed by data protection regulation with transparency, revocation andcorrection rights for data subjects. Data subjects may, for several reasons, only partially accept a privacypolicy, and chose to opt out, request data deletion or revoke their consent fordata processing. This article discusses the concept of partial commitment andits possible applications from both the data subject and the data controllerperspective in Big Data and Machine Learning.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Nyckelord

Big Data

Machine learning

data sharing

personal information

information privacy

commitment

consent

data processing

user interface

interaction

Computer Science

Datavetenskap

Publikations- och innehållstyp

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