Responsible AI research

As soon as we develop AI-supported models for analyzing human behavior, we simultaneously influence the shaping of society. However, existing methods for analyzing social phenomena may not be able to cope with this “feedback mechanism”. It is therefore particularly important to be vigilant when artificial intelligence is used to analyze patterns in human behavior and complex social phenomena. In principle, it is important to first weigh the risk of each project against its benefit – and, if necessary, to decide against it, as Microsoft Research emphasizes in an “Update on responsible AI research“.

To responsibly promote AI-supported modeling of human behavior, they have now published five recommendations for practice:

1. When developing projects, it must be ensured that they are accompanied by transparent and participatory processes based on scientific theories and ethical considerations.
2. Data, contextual information, calculation methods and measurement results must be integrated with these scientific theories.
3. During development, theory-based analysis models should be used, the underlying assumptions and the correctness of the data should be documented and justified, and the respective algorithmic influences should be reflected.
4. Quality criteria and datasets that enable the validation of competing measurement results must be developed and justified.
5. Reflection on possible negative consequences of analysis results and description of damage limitation strategies.

Fair, transparent, comprehensible and data protection-compliant analysis methods should include socio-theoretical findings. For a reliable and thus trustworthy analysis, the assumptions on which a model is based must be precisely documented and well justified. It must also be clear who decides exactly what to analyze and how the results will be used.

To identify risks to justice in AI systems at an early stage, various measurement methods should first be modeled. This allows AI practitioners to identify discrepancies between theoretical concepts and their implementation at an early stage and avoid...