With its disruptive potential, Artificial Intelligence (AI) is revolutionising many fields. In particular, generative AI, such as ChatGPT, is generating both fascination and uncertainty. But how will these developments affect the working world of tomorrow?

As a technology and compliance expert, I look closely at the opportunities and risks of AI. The insights I have gained through dialogue with experts in various fields and through critical discussions with colleagues motivate me to formulate a few key thoughts to enrich the necessary public debate.

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 AI and the labour market

The fear of job losses due to automation is widespread. However, according to the McKinsey Global Institute, generative AI (GenAI), such as ChatGPT or Claude, could enable up to $4.4 trillion in annual productivity growth. The disruptive effect of artificial intelligence such as GenAI in the workplace is particularly evident in knowledge-intensive and personal areas such as customer service, marketing and software development. In the financial sector, GenAI could make repetitive tasks more efficient, leading to a realignment of the workforce. With GenAI, the focus is less on manual activities and more on automating complex cognitive processes. This means that many professions will need to gain further qualifications and adapt their skills to changing requirements. Lifelong learning and continuous training are essential to benefit from the potential of AI and to remain competitive.

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"The true progress of AI lies not in its computing power, but in its ability to foster human prosperity."

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 The Social Perspective

Artificial Intelligence (AI) raises not only technological but also profound social questions, especially with regard to transparency, control and ethics. The lack of transparency of many AI models, such as neural networks, raises concerns about the traceability and control of these technologies. This is particularly relevant in the financial sector, where the processing of sensitive data, such as financial information and personal data, is at the forefront. Insufficiently transparent systems run the risk of causing misuse and loss of trust. One example is credit underwriting algorithms that unintentionally discriminate against certain segments of the population.

To counter such risks, it is important to make AI systems in the financial sector transparent and explainable. Technologies such as Explainable AI (XAI) can help to better understand and communicate how AI systems work. At the same time, robust oversight mechanisms are needed to ensure compliance with ethical standards. Another example of the need for transparent AI systems is the risk assessment of investments, where unclear AI decision-making processes can lead to significant financial losses.

In addition to transparency, other ethical issues are of key importance in the financial sector. For example, algorithmic biases can disadvantage certain groups of people, which is contrary to the principle of equality. To avoid such biases, AI systems must be designed to be fair and non-discriminatory. The training data used and the specific modelling are important levers. A practical example of the importance of ethical AI practices can be found in the use of AI in the insurance industry, where unfair algorithms can lead to unfair premium calculations.

Finally, education and digital literacy play a crucial role in ensuring the responsible use of AI. This applies to both users and developers of AI systems. Financial service providers therefore will need to fully inform their customers and employees about the opportunities and risks of AI. At the same time, ethical guidelines should be formulated for developers so that ethical aspects are taken into account in the design of AI systems. An example of the importance of education in dealing with AI can be found in the area of online banking, where educating customers contributes significantly to security and efficiency.‍

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Regulation: The EU AI Act‍

The EU AI Act is a major step forward in the regulation of AI. It aims to prevent misuse, protect society and foster innovation. This is particularly important in the financial sector. During the negotiations, consensus has already been reached on important aspects such as certification rules for high-risk AI systems. However, there are still outstanding issues, in particular the definition of AI, its use in law enforcement and the regulation of general-purpose AI models. These issues will be addressed in future trilogue meetings.

Democratising AI

AI has the potential to make education more personalised and accessible, for example through adaptive learning systems. To realise this potential, teaching concepts need to integrate AI. This will require massive efforts in teacher training and in school curricula. For AI to have a positive impact on the world of work and society, all societal actors must be actively involved in shaping change.

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Conclusion

The rapid development of AI underlines the need for education and lifelong learning. In the financial sector, where AI optimises work processes and improves risk management and customer experience, the ability of the workforce to adapt is essential. AI is neither a guarantee of job losses nor a miracle healer. With targeted investment in education and its balanced adoption, AI can drive sustainable progress. The EU AI law must and will show how careful governance can maximise the benefits of AI while protecting societal interests..

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Citations:

1. McKinsey & Company. (2023, November 23). Generative AI can become a productivity booster. McKinsey Global Institute. Retrieved November 23, 2023, from McKinsey Global Institute Website https://www.mckinsey.com/de/news/presse/genai-ist-ein-hilfsmittel-um-die-produktivitaet-zu-steigern-und-das-globale-wirtschaftswachstum-anzukurbeln
2. Federal Commissioner for Data Protection and Freedom of Information. (2022, November 17). AI in law enforcement and hazard prevention - Report on the public consultation process. Retrieved November 23, 2023, from BfDI Website https://www.bfdi.bund.de/DE/DerBfDI/Inhalte/Konsultationsverfahren/KI-Strafverfolgung/KI-Strafverfolgung-Bericht.html
3. European Commission. (2021). Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts (COM/2021/206 final). Retrieved November 23, 2023, from EUR-Lex Website https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52021PC0206
4. The article was translated from German: Schmidt, A. (2023, November 27). KI-Revolution am Arbeitsplatz: Verlust, Veränderung oder Verheißung? LinkedIn. https://www.linkedin.com/pulse/ki-revolution-am-arbeitsplatz-verlust-ver%2525C3%2525A4nderung-oder-schmidt-lrure%3FtrackingId=1NbKXY2nTC6EgZSuhWEb5A%253D%253D/?trackingId=1NbKXY2nTC6EgZSuhWEb5A%3D%3D

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Nov 23 2023