In Germany, more than half of the population rejects fully automated decisions. The myth of the uncontrollable intelligent machine exists. On the other hand, algorithmic decision-making (support) systems are already being used successfully in many areas of everyday life, not least in medical diagnostics or in assessing creditworthiness. In order to increase user acceptance of algorithms and intelligent systems, the field of Explainable Artificial Intelligence (XAI) aims to provide automatic explanations for algorithmic decisions. Counterfactual explanations are being hyped in science and practice – but how good are they really?

The seminar paper should provide a structured overview of the scientific literature on XAI and, in particular, on counterfactual explanations. It should then critically assess the added value that counterfactual explanations bring to users of intelligent systems.

Literature assistance: https://arxiv.org/abs/2010.10596

People analytics, as a form of algorithmic management, is gaining popularity in the wake of the decentralisation of work and the search for tools to support (self-)organisation and leadership in distributed teams. However, alongside the supposed potential of people analytics, there is also the inherent danger that it will serve as surveillance software and reinforce existing prejudices and discrimination.

This seminar paper aims to provide a comprehensive overview of the scientific literature on employee perceptions of people analytics. Subsequently, a scenario will be designed to measure employee perceptions regarding the introduction of people analytics in the work environment.
 

Literature assistance

Artificial intelligence (AI) has surpassed humans in diagnosing X-rays and playing chess. At the same time, headlines are being made because AI systems are being used improperly and making discriminatory decisions. For example, when job applications are filtered using an AI system and only male applicants are selected as a result. Therefore, consideration of fairness and bias in the development of such systems has already gained considerable importance. There are various ways to define when an AI system is fair, including the concept of "counterfactual fairness".

This seminar paper aims to provide a comprehensive overview of the scientific literature on counterfactual fairness. Subsequently, a scenario will be designed to demonstrate how counterfactual fairness could contribute to the fairness (with a focus on traceability) of AI systems.

Literature assistance

Post-merger integration (PMI) encompasses the process of merging at least two companies into a single legal entity. This process continues until a uniform structure has been established between the companies involved. A key success factor in PMI is effective collaboration between new colleagues. The so-called "relationship risk" represents a decisive integration risk in this context.

This seminar paper will first provide an overview of the scientific literature on analytics solutions for evidence-based PMI decisions. Based on this, a scenario will be developed that shows how the characteristics and developments of relationship risks can generally be assessed from the employee's perspective.

Literature reference
 

The development of language models such as OpenAI's Codex and technologies based on it, such as GitHub Copilot, have fundamentally changed the way software is developed. These AI-powered tools can generate, analyse, debug and optimise code, redefining the role of software developers. At the same time, controversial discussions are emerging about the risks, limitations and ethical aspects of their use.

This seminar paper will first examine the potential and challenges of GitHub Copilot and comparable technologies. The focus will be on analysing scientific literature in order to gain a sound understanding of current developments. Based on this, a scenario will be designed that outlines possible future perspectives for software development and highlights the importance of AI-supported tools in this context.

Literature assistance 
 

Deepfakes have blurred the line between reality and fiction and represent one of the greatest challenges of the digital age. From a supposed call for surrender by the Ukrainian president to images showing Chancellor Scholz in prison, artificially generated content can spread disinformation in a targeted manner and cause considerable damage.

This seminar paper first analyses the risks of deepfakes and current approaches to detecting and combating them. The focus is on examining scientific literature to gain in-depth insights into existing challenges and solution strategies. The aim is to identify strategies that help to ensure the integrity of digital content and curb the misuse of this technology.

Literature assistance
 

The spread of generative artificial intelligence (GenAI) and technologies based on it, such as ChatGPT, opens up new perspectives for school education. Such technologies can support pupils in researching, writing texts and understanding complex topics by offering personalised and interactive learning opportunities. At the same time, there are legitimate concerns about possible negative effects, such as the impairment of independent thinking or the threat to the integrity of school performance through unreflective use and excessive dependence on GenAI.

This seminar paper aims to provide a well-founded overview of the scientific literature on the opportunities and challenges of GenAI-supported learning technologies. On this basis, a scenario will be developed that outlines possible solutions for the responsible and effective integration of ChatGPT and similar GenAI applications into school teaching.

Literature assistance
 

Retrieval-Augmented Generation (RAG) models combine the advantages of information retrieval systems with the capabilities of Generative Artificial Intelligence (GenAI) to provide accurate and context-sensitive answers to complex questions. This innovative technology makes it possible to dynamically integrate external knowledge sources into the generation process, opening up a wide range of new applications, particularly in data-intensive areas such as education, research and corporate communications. At the same time, RAG models raise important questions about data protection, bias and the reliability of the retrieved information.

This seminar paper will examine the current scientific literature on RAG models in order to analyse their potential and challenges. The aim is to identify practical applications and the limitations of this technology and to evaluate its significance for future-oriented knowledge processing.

Literature assistance
 

The advent of generative artificial intelligence (GenAI) is changing not only work processes, but also the distribution of roles between humans and machines. AI models such as ChatGPT and GitHub Copilot no longer act merely as tools, but as creative partners that generate text, code or images together with humans – a process known as "co-creation". This new form of collaboration raises fundamental questions: Who has control over the creative process? How is the role of humans in creative and cognitive professions changing? And how is responsibility distributed in such hybrid work processes? The challenge is to better understand the dynamics of this human-AI interaction: What skills do users need in the co-creation process? How are creative processes changing as a result of AI assistance? And what impact does this have on autonomy, creativity and decision-making?

This seminar paper aims to analyse the current state of research on human-AI collaboration. The aim is to examine typical fields of application (e.g. education, software development, design), systematise existing concepts of co-creation and discuss the associated opportunities and challenges. In addition, open questions regarding the distribution of roles, responsibility and the design of such hybrid systems will be examined.

Literature assistance

In education, human resources, the justice system and lending, decisions that affect people are increasingly being supported, either partially or completely, by artificial intelligence (AI). AI-supported systems are used to award grades, filter job applications and assess credit risks. This raises a key question: can these systems make fair and transparent judgements, or do they unconsciously reinforce existing inequalities? Although AI models often make decisions faster and supposedly more objectively than humans, they are subject to criticism: they reproduce existing biases in the training data, are non-transparent ("black boxes") and difficult to control. Legal and ethical questions of responsibility and explainability are also gaining in importance – especially when AI systems evaluate people or decide on opportunities and exclusions.

This seminar paper will examine the conditions under which AI-based evaluation systems can be considered fair. The aim is to outline the technical basis of algorithmic decisions, analyse current case studies from education, justice and human resources management, and discuss criteria for fair, transparent and responsible AI evaluation systems.

Literature guide

Energy supply is at the heart of many social and political discussions – it is fundamental to the economy, everyday life and climate protection. However, with the increasing use of artificial intelligence (AI) to control and optimise modern energy systems, the challenge of making these complex models comprehensible and trustworthy is also growing. This is exactly where Explainable Artificial Intelligence (XAI) comes in: it makes the decisions of AI models understandable – a crucial factor for security, transparency and acceptance.

The aim of this seminar paper is to provide a structured overview of the scientific literature on XAI in the energy sector. This will be followed by a critical assessment of current XAI approaches based on typical application examples.

Literature guide

Who gets a loan? How is stock market risk assessed? Such decisions are increasingly being made by AI models – but many of them are incomprehensible to customers and experts alike. To ensure that trust and fairness in finance do not fall by the wayside, researchers are developing methods that make machine decisions transparent and explainable – known as Explainable AI (XAI).

The seminar paper should provide a structured overview of the scientific literature on XAI in the financial sector. This will be followed by an analysis of typical fields of application and explanation methods based on selected case studies.

Literature guide