responsibility gaps-B

*There are multiple notes about this text. See also: responsibility gaps-A*

Definition of responsibility gaps

Responsibility gaps in AI refer to situations where it becomes challenging to determine who is accountable or responsible for the actions, decisions, or consequences of an AI system. These gaps can arise due to various factors, such as the complexity of AI systems, the involvement of multiple stakeholders, unclear regulations and standards, or limited oversight and control. De Sio & Mecacci (2021) argue that the responsibility gap comprises not just a single problem but rather a collection of at least four interrelated problems. These gaps include deficiencies in culpability, moral and public accountability, and active responsibility. These problems arise from various sources, some stemming from technical aspects, while others are rooted in organizational, legal, ethical, and societal factors.

Implications of commitment to addressing responsibility gaps

When committing to addressing responsibility gaps in AI, this means committing to the ethical principle of accountability, transparency, and fairness. This means taking ownership and being answerable for the actions, decisions, and consequences of AI systems. It also involves ensuring that the design, development, and deployment of AI technologies are done in a way that minimizes negative impacts, upholds ethical standards, and protects the well-being of individuals and society.

Several factors are at stake, such as ethical and social norms, trust and public acceptance, legal compliance, or the long term use of AI.
Some key requirements for the appropriate design of AI technologies can be argued for this concept such as:

• Explainability and transparency: the AI systems should be explainable and transparent in order to allow users and stakeholders to understand the decisions the system makes. This way it is easier to assess who is responsible.
• Fairness and bias mitigation: as addition to explainablity, fairness and bias should be addressed in order to ensure a fair and equitable outcome.
• Human oversight and control: humans should have an oversight of the AI systems, especially in critical decision-making. This way, human responsibility is maintained.
• Ethical review and impact assessment: before deploying AI technologies, ethical reviews and impact assessments should be conducted to anticipate and mitigate potential risks and challenges.

Societal transformations required for addressing concern raised by responsibility gaps

The responsibility gap refers to who is responsible for the actions of the AI. This is a difficult disucssion, especially if there are different views on the outcome. What is important is international collaboration or public-private partnerships. As AI has global implications, international collaboration on ethical AI frameworks and best practices can ensure a cohesive and responsible approach to AI development worldwide. The view on AI can be different among different cultures, therefore international collaboration is valuable. Additionally, collaborations between governments, academia, industry, and civil society can foster collective responsibility in addressing AI-related challenges and promote responsible AI innovation.

Most important is education. Integrating ethics education and training into AI-related disciplines and computer science programs can foster a culture of responsibility and awareness among AI developers. It will help them understand the societal implications of their work and encourage ethical decision-making throughout the development process. Educating people may cause a societal transformation. Relatively few people have knowledge about AI, causing it to be a scary phenomenon. Education could help change this view of people.

Sources

• Santoni de Sio, F., Mecacci, G. Four Responsibility Gaps with Artificial Intelligence: Why they Matter and How to Address them. Philos. Technol. 34, 1057–1084 (2021). https://doi-org.proxy.library.uu.nl/10.1007/s13347-021-00450-x