How to learn to trust AI? Responsible AI builds trust and lays the foundation for successful scaling by adopting a “human first” approach – using technology to help people make better decisions while keeping them held accountable through the right governance processes and technical steps. Our AI:Built to Scale study shows that responsibility is more than “nice to have”: strategic AI scalers are significantly more likely to inform their employees about how they are handling responsible AI. You see the value of AI.. but how do you trust it? From improving efficiency and improving results, to completely rethinking industries, AI opens up huge opportunities. Against this backdrop, it's easy to forget that AI decisions also have a real impact on people's lives, raising serious questions about ethics, trust, legitimacy, and accountability.

Allowing machines to make decisions can expose a business to significant risks, including reputational, employment/human resource, data privacy, health and safety issues. Enter: Responsible AI . This topic is widely covered in the media and attracts serious attention from clients in the public and private sectors. What happens when the machine's decision turns out to be wrong or illegal? Potential fines and sanctions can jeopardize the commercial viability of a business. What about other unintended consequences? AI has already shown that it can be biased, which was not to be expected, and can damage a brand's reputation. For example, Amazon SM had to abandon its artificial intelligence-based recruiting tool, which appeared to show a bias against women. And if need be, how does a human know when to intervene in a process controlled by a machine? Build trust in how you manage AI The board of directors must know what obligations it has to its shareholders, employees and society as a whole in order to ensure the deployment of AI without unforeseen consequences.

The CEO may ask, how can I be sure that we have thought through the possible brand and PR risks associated with artificial intelligence? Meanwhile, the chief risk officer and chief information security officer should be thinking: if we deploy AI, how can we do it in a way that complies with data protection regulations? Building a solid ethical foundation for AI allows you to “engineer” legal and ethical issues to the extent possible. However, it is not only about creating appropriate governance structures. It is also important to translate these ethical and legal frameworks into statistical concepts that can be unambiguously represented in software. So where to start? First, make sure AI considerations are built into your core values ​​and robust compliance processes. You will then need to implement specific technical guidelines to ensure that AI systems are secure, transparent, and accountable to protect your employees, customers, civilians, and other organizations. Then, identify new and changing roles and arrange for proper training for technicians and your diverse team of experts so they understand their new roles and responsibilities. All of these elements are part of an innovation-friendly Responsible AI blueprint that you can apply across functions and projects, allowing you to understand and manage the ethical implications of everything you do.

Put ethics at the core to build and maintain trust Design ethically when you plan for AI. We program algorithms to give us exactly what we asked for, so we shouldn't be surprised when they do. And the problem is that simple algorithms consider all data unchanged, even data about our preferences, income and life situation. What can happen then is that algorithms can trap people in their background, history or stereotype. These "bad feedback loops" can lead to negative consequences for society. The problems mentioned are not inherent in machine learning algorithms themselves. Instead, problems arise from the way they interact with society and the unforeseen consequences that can result from those interactions.

Thus, it is vital to put ethical aspects at the heart of the development of each new algorithm. Just as data privacy and cybersecurity have moved from departmental to board level issues, the responsible management of AI should quickly become more important to all organizations that use it. .