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- The ethical guidelines ecosystem has grown extensively over the past years and includes more than 40 sets of guidelines. However, the challenge of creating a complementary balance between legislation, regulation, innovation, and the guidelines remains.
- The approach of self-regulation is not enough. There is a need for a new industry model that allows for working with data ethics, but does not pose a barrier for innovation and competitiveness. Data ethics should be a parameter on the market.
- While there are many common values in the guidelines, the base values that should be addressed are transparency and explainability. Mechanisms for providing transparency have to be layered, stakeholder-specific, and able to operate on different levels. Explainability should be defined in a multistakeholder dialogue because it includes explaining algorithms’ decisions, as well as explaining what data ethics means in a specific context.
- Not all machine-learning systems operate with the same algorithms, have the same application, or are used by the same demographics. Developing tools for the practical implementation of data ethics has to be highly context-specific and targeted.
- Data ethics standardisation through certificates and seals for business entities should be explored as an instrument of ensuring trust. Other instruments include an obligation to report data ethics policies in the annual reviews and in the corporate social responsibility policies. Sharing best practice cases is crucial.