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statworx AI Principles: Why We Started Developing Our Own AI Guidelines

  • Expert Jan Fischer
  • Date 04. August 2022
  • Topic Artificial IntelligenceHuman-centered AI
  • Format Blog
  • Category Management
statworx AI Principles: Why We Started Developing Our Own AI Guidelines

Why we need AI Principles

Artificial intelligence has already begun and will continue to fundamentally transform our world. Algorithms increasingly influence how we behave, think, and feel. Companies around the globe will continue to adapt AI technology and rethink their current processes and business models. Our social structures, how we work, and how we interact with each other will change with the advancements of digitalization, especially in AI.

Beyond its social and economic influence, AI also plays a significant role in one of the biggest challenges of our time: climate change. On the one hand, AI can provide instruments to tackle parts of this urgent challenge. On the other hand, the development and the implementation of AI applications will consume a lot of energy and emit massive amounts of greenhouse gases.

Risks of AI

With the advancement of a technology that has such a high impact on all areas of our lives come huge opportunities but also big risks. To give you an impression of the risks, we just picked a few examples:

  • AI can be used to monitor people, for example, through facial recognition systems. Some countries are already using this technology extensively for a few years.
  • AI is used in very sensitive areas where minor malfunctions could have dramatic implications. Examples are autonomous driving, robot-assisted surgery, credit scoring, recruiting candidate selection, or law enforcement.
  • The Facebook and Cambridge Analytica scandal showed that data and AI technologies can be used to build psychographic profiles. These profiles allow microtargeting of individuals with customized content to influence elections. This example shows the massive power of AI technologies and its potential for abuse and manipulation.
  • With recent advancements in computer vision technology, deep learning algorithms can now be used to create deepfakes. Deepfakes are realistic videos or images of people doing or saying something they never did or said. Obviously, this technology comes with enormous risks.
  • Artificial intelligence solutions are often developed to improve or optimize manual processes. There will be use cases where this will lead to a replacement of human work. A challenge that cannot be ignored and needs to be addressed early.
  • In the past, AI models reproduced discriminating patterns of the data they were trained on. For example, Amazon used an AI system in their recruiting process that clearly disadvantaged women.

These examples make clear that every company and every person developing AI systems should reflect very carefully on the impact the system will or might have on society, specific groups, or even individuals.

Therefore, the big challenge for us is to ensure that the AI technologies we develop help and enable people while minimizing any forms of associated risks.

Why are there no official regulations in place in 2022?

You might be asking yourself why there is no regulation in place to address this issue. The problem with new technology, especially artificial intelligence, is that it advances fast, sometimes even too fast.

Recent releases of new language models like GPT-3 or computer vision models, for example, DALLE-2, exceeded the expectations of many AI experts. The abilities and applications of AI technologies will continually advance faster than regulation can. And we are not talking about months, but years.

It is fair to say that the EU made its first attempt in this direction by proposing a regulatory framework for artificial intelligence. However, they indicate that the regulation could apply to operators in the second half of 2024 at the earliest. That is years after the above-described examples became a reality.

Our approach: statworx AI Principles

The logical consequence of this issue is that we, as a company, must address this challenge ourselves. And therefore, we are currently working on the statworx AI Principles, a set of principles that guide us when developing AI solutions.

What we have done so far and how we got here

In our task force “AI & Society”, we started to tackle this topic. First, we scanned the market and found many interesting papers but concluded that none of them could be transferred 1:1 to our business model. Often these principles or guidelines were very fuzzy or too detailed and unsuitable for a consulting company that operates in a B2B setting as a service provider. So, we decided we needed to devise a solution ourselves.

The first discussions showed four big challenges:

  • On the one hand, the AI Principles must be formulated clearly and for a high-level audience so that non-experts also understand their meaning. On the other hand, they must be specific to be able to integrate them into our delivery processes.
  • As a service provider, we may have limited control and decision power about some aspects of an AI solution. Therefore, we must understand what we can decide and what is beyond our control.
  • Our AI Principles will only add sustainable value if we can act according to them. Therefore, we need to promote them in our projects to the customers. We recognize that budget constraints, financial targets, and other factors might work against the proper application of these principles as it will need additional time and money.
  • Furthermore, what is wrong and right is not always obvious. Our discussions showed that there are many different perceptions of the right and necessary things to do. This means we will have to find common ground on which we can all agree.

Our two key take-aways

A key insight from these thoughts was that we would need two things.

As a first step, we need high-level principles that are understandable, clear, and where everyone is on board. These principles act as a guiding idea and give orientation when decisions are made. In a second step, we will use them to derive best practices or a framework that translates these principles into concrete actions during all phases of our project delivery.

The second major thing we learned, is that it is tough to undergo this process and ask these questions but also that it is inevitable for every company that develops or uses AI technology.

What comes next

So far, we are nearly at the end of the first step. We will soon communicate the statworx AI Principles through our channels. If you are currently in this process, too, we would be happy to get in touch to understand what you did and learned.

References

https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html

https://www.nytimes.com/2018/04/04/us/politics/cambridge-analytica-scandal-fallout.html

https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G

https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence

https://www.bundesregierung.de/breg-de/themen/umgang-mit-desinformation/deep-fakes-1876736

https://www.welt.de/wirtschaft/article173642209/Jobverlust-Diese-Jobs-werden-als-erstes-durch-Roboter-ersetzt.html

Jan Fischer Jan Fischer Jan Fischer Jan Fischer Jan Fischer Jan Fischer

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