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AI & Society

AI and society: a reflection on implications and responsibility

In today's digital era, Artificial Intelligence (AI) is a key technology that is reshaping our social and professional interactions. This chapter encourages reflection and discussion on the implications of AI in the context of your organization and society. We have identified 7 aspects that are discussed in the context of "AI and society". For each aspect, questions have been compiled for self-reflection and discussion with your learning group:

  • Workplaces and automation
  • Transparency and comprehensibility
  • Distortions and discrimination
  • Privacy and data protection
  • Digital dependency
  • Ethics and value system
  • Regulation

Jobs and automation

AI and automation will fundamentally change many areas of work and professional fields. This triggers both hopes and fears. The loss of jobs through automation is being discussed, especially for lower-skilled jobs, particularly in the office sector. On the other hand, relieving people of monotonous subtasks can also create space for more creative tasks. In many areas, such as medical diagnostics or environmental management, AI is already demonstrating a level of performance in many tasks that is comparable to that of humans. New activities and professions will emerge in data management and AI training. Overall, however, there could be a new polarization between the qualified employees who use and deploy AI for themselves and those who lose their "market value" by not using it.

Questions for reflection:

  • What specific effects do you expect AI and automation to have on jobs and activities in your company?
  • How are the effects being discussed?
  • What opportunities do AI-supported assistance systems offer for simplifying certain tasks? What new and creative activities could arise?
  • How do you yourself view the development of AI? Do you see opportunities or risks for your own development? Where would you like to benefit from AI and use it yourself? Where are you cautious or skeptical?

Transparency and traceability

Traceability plays an important role in AI systems on two levels:

  • Training material: It is not always possible to trace which material an AI was trained on. Depending on the training material, an AI generates distortions in the analysis process (e.g. with regard to gender or other characteristics) and even political "beliefs".

  • Results: Transparency of AI systems is crucial for trust and accountability. What happens in the black box between a prompt and the output, especially when automated decisions are made based on AI responses. With today's AI systems, it is not possible to understand how the result comes about. Research in the field of "Explainable AI" promises to remedy this situation.

Reflection questions:

  • How does my organization ensure the transparency and traceability of AI systems?
  • Can we explain the decision-making processes of our AI systems in an understandable way or are they a black box?
  • How transparent do we make the algorithm models and training data used to customers and users?
  • What monitoring and testing systems are in place to identify and correct incorrect decisions made by AI?
  • How do we communicate openly with customers if errors do occur?
  • Do we educate and train our employees to monitor AI systems competently?
  • How can we as a company contribute to greater transparency and comprehensibility of AI?

Bias and discrimination

AI systems can reflect and reinforce existing biases and discrimination if the underlying data is unfair or contains stereotypes. The use of AI systems in applicant selection or in the financial and insurance sector, for example when granting loans, is frequently discussed. Algorithmic biases in AI systems can take various forms, such as gender bias, racial bias and age discrimination.

Reflection questions:

  • Does the data used to train AI in our organization potentially contain hidden biases and prejudices?
  • Does the data reflect the diversity of society or only small privileged groups?
  • How diverse and interdisciplinary are the teams that develop AI?
  • What testing methods are available to detect and eliminate discrimination in AI systems?
  • How can more awareness of this issue be created?

Privacy and data protection

The use of AI raises a number of questions regarding the handling of personal data. Data protection violations due to improper handling of AI systems can have serious consequences.It should be remembered that many providers, especially of free AI tools, use user input to train their models.The greatest data protection risk here is that confidential data from input in prompts is unknowingly transferred to the provider's large language model.

Reflection questions:

  • What personal customer data do we use for our AI systems?Is the data correctly pseudonymized?
  • How transparent do we make the use of customer data by AI? What consent do we obtain?
  • How do we ensure that AI systems do not use data in an uncontrolled manner for unintended purposes? What would be the consequences if internal company data were to end up in publicly accessible systems?
  • Are data protection impact assessments carried out before AI systems are used?
  • How do we train and sensitize our employees to handle data securely and responsibly?

Digital dependency

AI has the potential to enhance our cognitive abilities and improve decision-making, but it also harbors the risk of creating dependencies.As AI moves into more and more areas of life, the progressive acquisition of specific skills by people is becoming increasingly important in order to maintain their sovereignty and not trade it for a deep dependence on technology.Put simply, will AI make us smarter or dumber?Will relying on AI disempower us to a certain extent?

Reflection questions:

  • Which skills will become more important in a working world shaped by AI?Creativity, social skills, problem solving,...
  • Do we offer exchange forums to reduce fears of AI and gain confidence in dealing with it?
  • Will humans remain the final decision-making authority for critical AI applications or will we leave important processes entirely to the algorithm?
  • How do we strengthen media literacy in order to recognize and counteract undesirable developments?

Ethics and value system

The ethical dimension of AI encompasses various concerns, such as fairness and responsibility. The debate is about who AI should serve: The good of all people and not just a few corporations.Investigative journalists have also examined the work of so-called "clickworkers". Workers from low-wage countries (Kenya, Pakistan, Venezuela) train the models by, for example, linking texts and images, which machines are not yet able to do so well on their own, or filtering out unwanted responses from chatbots. The globalization of this round-the-clock business supports constant price undercutting.A key question is accountability throughout the value chain of AI use and who is accountable, especially when AI systems - perhaps even autonomously - make faulty or harmful decisions? Should manufacturers be liable? Or the users?

Reflection questions:

  • What ethical guidelines for AI exist in my company? Who was involved in their creation?
  • Do the guidelines also reflect my personal values such as justice, responsibility, protection of intellectual property and sustainability?
  • Are processes in place to discuss ethical issues across disciplines?
  • How can compliance with ethical principles be ensured throughout the entire development process of AI systems?
  • What training is needed to increase awareness and skills in ethics, responsibility and AI?

Regulation

The area of tension here is the balance of interests between exploiting innovation potential and minimizing risk. Some fear that regulation will hinder innovation. Others see risks for society and democracy if AI is used completely unregulated. In this context, it is important to discuss the level of regulation so that it can be effective - national, European, international or sector-specific for particularly sensitive areas. In this context, the role of voluntary commitments and certifications should also be emphasized as an alternative to regulations with sanctions.

Questions for reflection:

  • Where might there be risks in my company that require regulation?
  • Are there already internal rules or principles for responsible AI in my company? Should this be expanded?
  • How can high AI standards and the ability to innovate be ensured at the same time?
  • Should there be broad societal debates on regulation? How can we contribute constructively?

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