When involved in an innovation process, researchers can improve their technology building processes to advance new technological trends by aligning them with the societal challenges.Read more >>
Within innovation processes and when developing emerging technology scenarios and foresight exercises can provide unique insights on the anticipated impacts of socio-economic, political and technological drivers and unveil hidden assumptions among stakeholders.Read more >>
As the pace of technological change increases, organisations can undertake technological development projects to interpret, analyse, and assess larger amounts of data at a much faster pace.Read more >>
A successful digital strategy can enable organisations in the adoption of emerging technologies to complement their business and/or organisational processes and performance, in order to maintain and achieve competitive outputs.Read more >>
In domains where the outputs of a machine learning model can have real world consequences, algorithmic transparency can provide explanations of how these outputs were reached; thus enabling their benefits to be experienced more widely.Read more >>
At some point, institutions and organisations need to evaluate how existing and emerging technologies can be leveraged in novel ways against social, organisational or technical problems. To do so effectively, they need to understand the potential consequences, i.e., costs, benefits, opportunities and impacts, of those implementations to make informed strategic decisions.
We identify promising trends and develop scenarios and foresight exercises to anticipate, assess and optimize how to leverage emerging technologies to transform specific sectors. This information can help to guide the development of new policies, the application of new technologies to existing problems or the investment in new technology – with a view to having a positive impact on society.
We use a stakeholder-based approach to construct scenarios and apply co-design methods. Our policy recommendations and responsible machine learning applications always have users and society at the centre, to optimise the use of data, improve data literacy through explainable AI and enhance societal wellbeing.
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