On Good AI Governance : 14 Priority Actions, a S.M.A.R.T. Model of Governance, and a Regulatory Toolbox

AI4People's second year of activities has focused on applying - concretely, in real world scenarios and through appropriate governance - those ethical principles of AI announced by AI4People in 2018. The 2019 White Paper gives shape to - whilst establishing priorities and critical issues - 14 Priority Actions, a Model of S.M.A.R.T. Governance and a Regulatory Toolbox, to which governments and businesses alike can refer to - immediately and efficiently. To conceive the aforementioned, we examine current initiatives and debates on the governance of AI, and consequently provide: - A definition of the notion of governance and the principles that are at stake in this context - 14 Priority Actions that can be undertaken immediately, existing within three new groups of priority: (i) forms of engagement; (ii) no-regrets actions; and (iii) coordination mechanisms for the governance of AI - A S.M.A.R.T. Model of Governance, for both governments and businesses, adequate for tackling the normative challenges of AI, while being Scalable, Modular, Adaptable, Reflexive, and Technologically-savvy. We call for specific forms of governance that are neither bottom-up, nor top-down, but that are inbetween, and argue that neither co-regulatory models of AI governance - nor forms of self-regulation, nor its variants, such as 'monitored self-regulation' - are adequate - A Regulatory Toolbox, illustrating how the model of S.M.A.R.T. governance works

The LACE Learning Analytics in the Workplace (LAW) Manifesto

This manifesto for Learning Analytics in the Workplace builds on the results reported in Review 3: Policy recommendations for learning analytics from three stakeholder workshops. The manifesto sets out that the EU should identify and cooperate with all the relevant stakeholders, such as industry leaders, employers, workers, universities, teachers, social partners, trade and teacher unions, with the aim to identify the 21 st century skills, to improve the training of existing workforce maintaining the equilibrium between the needing of industries and society. Moreover, the EU and national educational authorities, together with companies and social partners, could improve the research and development of IT tools that are able to help leverage a mix of formal and informal learning situations during workforce daily operations. In the final part of the Manifesto, two case studies on the use of Learning Analytics at the workplace are presented, related to the EU project Watch Me and to SkillawareTM, an IT platform for electronic performance support

AI4People's 7 AI Global Frameworks: Insurance

Following its past work on AI ethics (with the “AI4People’s Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations”) and on AI governance (with the “AI4People Report on Good AI Governance: 14 Priority Actions, a S.M.A.R.T. Model of Governance, and a Regulatory Toolbox”), in 2020 AI4People has identified seven strategic sectors (Automotive, Banking & Finance, Energy, Healthcare, Insurance, Legal Service Industry, Media & Technology) for the deployment of ethical AI, appointing 7 different committees to analyze how can trustworthy AI be implemented in these sectors: the AI4People’s 7 AI Global Frameworks are the result of this effort

AI in media & technology sector: opportunities, risks, requirements and recommendations

As AI systems increasingly pervade modern society and lead to manifold and diverse consequences, the development of internationally recognized and industry-specific frameworks focusing on legal and ethical principles is crucial. This report aims at (a) understanding how the 7 Key Requirements for Trustworthy AI impact the Media and Technology sector (MTS) and at (b) putting forward guidelines to ensure compliance with the 7 Key Requirements. The report identifies four application areas of AI MTS, i.e. automating data capture and processing, automating content generation, automating content mediation and automating communication. Subsequently, the 7 Key Requirements are discussed within each of the four identified themes. Ultimately, recommendations are made to ensure that AI development and adoption in Media and Technology sector is compliant with the 7 Key Requirements. Three clusters of recommendations are proposed: (1) addressing data power and positive obligations, (2) empowerment by design and risk assessments and (3) cooperative responsibility and stakeholder engagements

AI4People - An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations

This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.ISSN:1572-8641ISSN:0924-649