I found it very interesting that the SBA includes the topic of artificial intelligence, AI. This is a branch of computer science that tries to teach computers intelligent behavior in an automated way. Many companies are daring to take their first steps with corresponding use cases, and some are already using AI-based solutions productively. If the focus is placed on the benefit, the risks are often forgotten, starting at the drawing board through to established solutions.
We have already been able to review some AI solutions and have developed a framework for this purpose. We pay special attention to the following:
- Data governance should be established, especially in the area of data quality. A human operator can filter out erroneous data using common sense; this is not (automatically) possible using AI.
- Regulatory aspects, which may be regulated by organizational measures, must necessarily be technically intercepted in the AI environment.
- A transparent purpose of use is the prerequisite for the purpose limitation of the data, which is a key topic in data protection. At the latest for revoking consent – the right to be forgotten – it is central to know for what purpose the data was processed. It is also foreseeable that regulations in the AI environment will adopt such directories.
- Questions about controllability examine whether decisions are made directly from AI algorithms and to what extent these are reversible. Ideally, the algorithms and models will be regularly checked for biases and possible discriminations.
- Transparency is important to make decisions comprehensible. The extent to which those affected (e.g., applicants or customers) are and must be informed about the use and background of AI should also always be discussed here.
With the six use cases in its guide, the SBA provides some insights into which topics are currently possible. However, I often see a more straightforward starting point not in the customer-oriented topics but in the analysis of data in internal processes. Again, this raises several questions about risks, but they are easier to control.
In a future post, I will discuss how an organization can identify and prioritize possible use cases and what framework conditions should be met for data to be used.
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