While it’s transformation is not as widely discussed as sectors such as media, finance, or retail, the automotive industry is also riding the crest of a wave of technology-fuelled disruption. Automotonous driving, electrification, and data analytics are recasting the experience of customers, while new business models like ridesharing have changed the very relationship many auto brands have with particularly younger consumers.
Artificial intelligence likewise offers huge opportunities, but as with other industries, AI comes with risks. That’s why late last year, BMW revealed the guardrails it will put around algorithmic development.
The company says the use of artificial intelligence (AI) is a central element of the digital transformation process at the BMW Group and that it already used AI throughout the value chain.
But it has gone further than many brands in identifying and responding to the many ethical issues with AI creates. As such it has developed seven principles that govern the development and application of artificial intelligence at the BMW Group:
- Human agency and oversight. The BMW Group implements appropriate human monitoring of decisions made by AI applications and considers possible ways that humans can overrule algorithmic decisions.
- Technical robustness and safety. The BMW Group aims to develop robust AI applications and observes the applicable safety standards designed to decrease the risk of unintended consequences and errors.
- Privacy and data governance. The BMW Group extends its state-of-the-art data privacy and data security measures to cover storage and processing in AI applications.
- Transparency. The BMW Group aims for explainability of AI applications and open communication where respective technologies are used.
- Diversity, non-discrimination and fairness. The BMW Group respects human dignity and therefore sets out to build fair AI applications. This includes preventing non-compliance by AI applications.
- Environmental and societal well-being. The BMW Group is committed to developing and using AI applications that promote the well-being of customers, employees and partners. This aligns with the BMW Group’s goals in the areas of human rights and sustainability, which includes climate change and environmental protection.
- Accountability. The BMW Group’s AI applications should be implemented so they work responsibly. The BMW Group will identify, assess, report and mitigate risks, in accordance with good corporate governance.
According to Michael Würtenberger, Head of “Project AI”: “Artificial intelligence is the key technology in the process of digital transformation. But for us the focus remains on people. AI supports our employees and improves the customer experience. We are proceeding purposefully and with caution in the expansion of AI applications within the company.”
He said, “The seven principles for AI at the BMW Group provide the basis for our approach.”
The BMW Group says it continues to follow global developments in terms of both technological innovations and regulatory and ethical issues. Together with other companies and organisations, for instance, the BMW Group is involved in shaping and developing a set of rules for working with AI, and the company has taken an active role in the European Commission’s ongoing consultation process.
BMW also says it will continuously refine and adapt its approach, allowing it to extend the use of AI and increase awareness among its employees of the need for sensitivity when working with AI technologies.
“Project AI” was launched in 2018 to ensure that AI technologies are used ethically and efficiently. As the BMW Group’s centre of competence for data analytics and machine learning, Project AI is designed to ensure rapid knowledge and technology sharing across the company and to play a key role in the ongoing process of digital transformation at the BMW Group.
One of the developments to come out of Project AI is a portfolio tool which creates transparency in the company-wide application of technologies making data-driven decisions. This D³ (Data-Driven Decisions) portfolio currently spans 400 use cases, of which more than 50 are available for regular operation.