Workshop Schedule

De-biased, diverse, divisive - On ethical perspectives regarding the de-biasing of GenAI and their actionability

AI tech companies cannot seem to get it right. After years of evidence-based criticism of biases in AI, in particular decision models, LLMs and other generative AI, after years of research and toolbox provision for de-biasing, many companies have implemented such safeguards into their services. However, ridicule and protests have recently erupted when users discovered generated images that were “(overly?) diversified” with respect to gender and ethnicity and answers to ethical questions that were “(overly?) balanced” with regard to moral stances. Is this seeming contradiction just a backlash, or does it point to deeper issues? In this talk, I will analyse instances of recent discourse on too little or too much “diversification” of (Gen)AI and relate this to methodological criticism of “de-biasing”. A second aim is to contribute to the broadening and deepening of answers that computer science and engineering can and should give to enhance fairness and justice.

Beyond the AI hype: Balancing Innovation and  Social Responsibility

AI can extend human capabilities but requires addressing challenges in education, jobs, and biases. Taking a responsible approach involves understanding AI's nature, design choices, societal role, and ethical considerations. Recent AI developments, including foundational models, transformer models, generative models, and large language models (LLMs), raise questions about whether they are changing the paradigm of AI, and about the responsibility of those that are developing and deploying AI systems. In all these developments, is vital to understand that AI is not an autonomous entity but rather dependent on human responsibility and decision-making. 

In this talk, I will further discuss the need for a responsible approach to AI that emphasize trust, cooperation, and the common good. Taking responsibility involves regulation, governance, and awareness. Ethics and dilemmas are ongoing considerations, but require understanding that trade-offs must be made and that decision processes are always contextual. Taking responsibility requires designing AI systems with values in mind, implementing regulations, governance, monitoring, agreements, and norms. Rather than viewing regulation as a constraint, it should be seen as a stepping stone for innovation, ensuring public acceptance, driving transformation, and promoting business differentiation. Responsible Artificial Intelligence (AI) is not an option but the only possible way to go forward in AI.