Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS.
The Commission is proposing the first ever legal framework on AI, which addresses the risks of AI and positions Europe to play a leading role globally.
Proposal for a Regulation on a European approach for Artificial Intelligence Leer más »
Documentation is key – design decisions in AI development must be documented in detail, potentially taking inspiration from the field of risk management. There is a need to develop a framework for large-scale testing of AI effects, beginning with public tests of AI systems, and moving towards real-time validation and monitoring. Governance frameworks for decisions in AI development need to be clarified, including the questions of post-market surveillance of product or system performance. Certification of AI ethics expertise would be helpful to support professionalism in AI development teams. Distributed responsibility should be a goal, resulting in a clear definition of roles and responsibilities as well as clear incentive structures for taking in to account broader ethical concerns in the development of AI systems.
Addressing Ethical Dilemmas in AI: Listening to Engineers Leer más »
Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales affect model behavior.
In public health, contact tracing is the process to identify individuals who have been in contact with infected persons. Proximity tracing with smartphone applications and sensors could support contact tracing. It involves processing of sensitive personal data.
TechDispatch #1/2020: Contact Tracing with Mobile Applications Leer más »
The starting point to develop the operational definition is the definition of AI adopted by the High Level Expert Group on artificial intelligence. To derive this operational definition we have followed a mixed methodology. On one hand, we apply natural language processing methods to a large set of AI literature. On the other hand, we carry out a qualitative analysis on 55 key documents including artificial intelligence definitions from three complementary perspectives: policy, research and industry.
Defining Artificial Intelligence |🇪🇸 Definiendo Inteligencia Artificial Leer más »
El objetivo de este documento es alentar a los investigadores de inteligencia artificial y diseñadores de productos a cambiar del pensamiento unidimensional sobre los niveles de automatización / autonomía a un nuevo marco HCAI bidimensional.
Inteligencia artificial centrada en el ser humano: confiable y segura Leer más »
The purpose of this White Paper is to set out policy options on how to achieve these objectives. It does not address the development and use of AI for military purposes.The Commission invites Member States, other European institutions, and all stakeholders, including industry, social partners, civil society organisations, researchers, the public in general and any interested party, to react to the options and to contribute to the Commission’s future decision-making in this domain.
WHITE PAPER On Artificial Intelligence – A European approach to excellence and trust Leer más »
The Committee on Standards in Public Life Published (10 February 2020 ) its report and recommendations to the Prime Minister to ensure that high standards of conduct are upheld as technologically assisted decision making is adopted more widely across the public sector. (GOV.UK).
An article by the Allen Institute of Artificial Intelligence highlights a pending issue: machines do not really understand what humans write or read.
‘WinoGrande: An Adversarial Winograd Schema Challenge at Scale’ Leer más »