“attention”
Back Attention in Machine Learning (ML) refers to a mechanism that allows a model to focus selectively on certain parts of the input data, rather than treating all elements equally. This helps the model prioritize the most relevant information for the task. For example, in the sentence “Cat on a mat,” the attention mechanism might […]
algorithmic accountability
Back Algorithms have a document of accountability that outlines its decision-making process, evaluated against key criteria: Transparency, Auditability, Ethical Considerations, Risk Mitigation, Accountability Mechanisms, and Stakeholder Engagement. These criteria are crucial in high-stakes domains such as criminal justice, hiring and employment, healthcare, finance and credit, and government decision-making. However, holding algorithms accountable raises complex ethical […]
Ethical protocols
Back Protocols are a set of guidelines designed to establish best practices for ensuring that artificial intelligence (AI) systems are developed, deployed, and governed according to ethical criteria. These criteria often include: While the primary goal may be social impact and responsibility, it’s important to recognize that the underlying motivations can vary. In some […]
Explainability
Back Effective documentation of artificial intelligence (AI) models is essential for transparency and accountability. To be truly effective, the documentation must clearly explain how the model makes decisions and the social impacts of those decisions, including considerations of fairness, risks, security, and transparency. This documentation should be written in a way that is accessible to […]
Bias
Back Bias in Artificial Intelligence (AI) occurs when an AI model reflects prejudices such as sexism or racism. This bias can be obvious, like an AI biometric surveillance system that exclusively picks out people-of-color to be potential criminals, or it can be hidden, such as in one word, that feels violating. Such bias often stems […]
Little Sparks
Back Little Sparks can refer to moments when an AI model behaves in an unexpected yet beneficial way, offering new insights or possibilities. It can also describe the small, innovative ideas that Designers for Responsible AI might have—moments of inspiration that emerge in an otherwise highly technical, scientific field. These sparks are often tied to […]
Values by Design
Back Values by Design refers to embedding ethical principles into every stage of a product’s lifecycle, from conception to deployment. In the context of Artificial Intelligence (AI), it’s important to recognize that AI itself does not have inherent values—it is a tool shaped by human creators. Therefore, Values by Design in AI requires identifying those […]
Values by Use
Back Values by Use examines how AI models are applied in the real world and the ethical implications that arise from their usage. It focuses on how users’ actions and decisions can shape the outcomes of AI systems, while also considering external factors such as regulatory frameworks, societal norms, and cultural contexts. This perspective emphasizes […]
Heterogenous engineering
Back Heterogeneous Engineering and Responsible AI are closely linked, especially when it comes to ensuring fairness and ethical design. Fairness cannot be treated as a purely technical standard, as human contexts such as relationships, organizational structures, and social norms must be part of the design and engineering process from the beginning. By considering these factors, […]
Socio-technical diagram
Back About people, people people. Socio-technical diagramming is the practice of visually representing both technological systems and their social contexts, highlighting the interconnections between them. In sociotechnical systems theory, this approach encompasses not just the visible elements, like hardware and software, but also the often invisible social structures that influence and are influenced by technology. […]