BMW adopts ethical code for the use of artificial intelligence

October 13, 2020 //By Christoph Hammerschmidt
BMW adopts ethical code for the use of artificial intelligence
Within the scope of the digital transformation of the company, automobile manufacturer BMW uses more than 50 applications of artificial intelligence (AI). The company has now developed a code of ethics for the use of this technology.

Building on the basic requirements formulated by the EU on trustworthy AI, BMW has formulated seven basic principles for the use of AI in the company. These are to be continuously concretised and developed further in line with the wide range of applications across all areas of the company.

BMW's seven principles:

  • Priority of human action and human supervision of AI
  • Technical robustness and safety
  • Privacy protection and data quality management
  • Transparency: Explanability of decisions through AI applications
  • Diversity, non-discrimination and fairness
  • Social and environmental well-being
  • Accountability

As early as 2018, BMW launched the "Project AI" to ensure that the AI technologies are applied in an ethical and efficient manner. This project has now grown into a competence centre at Group level for data analytics and machine learning. As a hub for knowledge and technology exchange within the BMW Group, the project plays a key role in the digitisation of the company. Among other things, a portfolio tool was developed to create transparency in the application of technologies that make data-driven decisions.

Examples of applications from BMW's AI portfolio:

AI-supported energy management in the vehicle:  There are a large number of electrical consumers in the vehicle, such as the seat heating system, the entertainment system, the air conditioning system and many more. In many cases, the driver is not aware that the use of these consumers also has an impact on CO2 emissions or the range of the vehicle. AI experts from the BMW Group are researching and developing AI-based software for energy management in the vehicle. Based on user behaviour and available information about the route, the system learns to optimally adjust the energy consumption in the vehicle to the driver's needs and energy efficiency.

Acoustic Analytics: Sensory enhancement in the sensor model for automated driving functions. In connection with the topic of environment detection, the company is researching how AI sensor fusion can be extended to include acoustic signal


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