Recommendation of the Council on Artificial Intelligence – OECD



Recommendation of the Council on Artificial Intelligence

Adopted on:  22/05/2019


Section 1: Principles for responsible stewardship of trustworthy AI

II.RECOMMENDS that Members and non-Members adhering to this Recommendation (hereafter the “Adherents”) promote and implement the following principles for responsible stewardship of trustworthy AI, which are relevant to all stakeholders.

III.CALLS ON all AI actors to promote and implement, according to their respective roles, the following Principles for responsible stewardship of trustworthy AI.

IV.UNDERLINES that the following principles are complementary and should be considered as a whole.

1.1.Inclusive growth, sustainable development and well-being

Stakeholders should proactively engage in responsible stewardship of trustworthy AI in pursuit of beneficial outcomes for people and the planet, such as augmenting human capabilities and enhancing creativity, advancing inclusion of underrepresented populations, reducing economic, social, gender and other inequalities, and protecting natural environments, thus invigorating inclusive growth, sustainable development and well-being.

1.2.Human-centred values and fairness

a)AI actors should respect the rule of law, human rights and democratic values, throughout the AI system lifecycle. These include freedom, dignity and autonomy, privacy and data protection, non-discrimination and equality, diversity, fairness, social justice, and internationally recognised labour rights.

b)To this end, AI actors should implement mechanisms and safeguards, such as capacity for human determination, that are appropriate to the context and consistent with the state of art.

1.3.Transparency and explainability

AI Actors should commit to transparency and responsible disclosure regarding AI systems. To this end, they should provide meaningful information, appropriate to the context, and consistent with the state of art: foster a general understanding of AI systems, make stakeholders aware of their interactions with AI systems, including in the workplace, enable those affected by an AI system to understand the outcome, and, enable those adversely affected by an AI system to challenge its outcome based on plain and easy-to-understand information on the factors, and the logic that served as the basis for the prediction, recommendation or decision.

1.4.Robustness, security and safety

a)AI systems should be robust, secure and safe throughout their entire lifecycle so that, in conditions of normal use, foreseeable use or misuse, or other adverse conditions, they function appropriately and do not pose unreasonable safety risk.

b)To this end, AI actors should ensure traceability, including in relation to datasets, processes and decisions made during the AI system lifecycle, to enable analysis of the AI system’s outcomes and responses to inquiry, appropriate to the context and consistent with the state of art.

c)AI actors should, based on their roles, the context, and their ability to act, apply a systematic risk management approach to each phase of the AI system lifecycle on a continuous basis to address risks related to AI systems, including privacy, digital security, safety and bias.


AI actors should be accountable for the proper functioning of AI systems and for the respect of the above principles, based on their roles, the context, and consistent with the state of art.


You can find the full document and the link below:


Ethics Guidelines For Trustworthy AI


Ethics Guidelines For Trustworthy AI


European Commission High-Level Expert Group

April 8, 2019



The aim of the Guidelines is to promote Trustworthy AI. Trustworthy AI has three components, which should be met throughout the system’s entire life cycle: (1) it should be lawful, complying with all applicable laws and regulations (2) it should be ethical, ensuring adherence to ethical principles and values and (3) it should be robust, both from a technical and social perspective since, even with good intentions, AI systems can cause unintentional harm. Each component in itself is necessary but not sufficient for the achievement of Trustworthy AI. Ideally, all three components work in harmony and overlap in their operation. If, in practice, tensions arise between these components, society should endeavour to align them.

These Guidelines set out a framework for achieving Trustworthy AI. The framework does not explicitly deal with Trustworthy AI’s first component (lawful AI). Instead, it aims to offer guidance on the second and third components: fostering and securing ethical and robust AI. Addressed to all stakeholders, these Guidelines seek to go beyond a list of ethical principles, by providing guidance on how such principles can be operationalised in sociotechnical systems. Guidance is provided in three layers of abstraction, from the most abstract in Chapter I to the most concrete in Chapter III, closing with examples of opportunities and critical concerns raised by AI systems.

I. Based on an approach founded on fundamental rights, Chapter I identifies the ethical principles and their correlated values that must be respected in the development, deployment and use of AI systems.

Key guidance derived from Chapter I:

  • Develop, deploy and use AI systems in a way that adheres to the ethical principles of: respect for human autonomy, prevention of harm, fairness and explicability. Acknowledge and address the potential tensions between these principles.
  • Pay particular attention to situations involving more vulnerable groups such as children, persons with disabilities and others that have historically been disadvantaged or are at risk of exclusion, and to situations which are characterised by asymmetries of power or information, such as between employers and workers, or between businesses and consumers.
  • Acknowledge that, while bringing substantial benefits to individuals and society, AI systems also pose certain risks and may have a negative impact, including impacts which may be difficult to anticipate, identify or measure (e.g. on democracy, the rule of law and distributive justice, or on the human mind itself.) Adopt adequate measures to mitigate these risks when appropriate, and proportionately to the magnitude of the risk.

II. Drawing upon Chapter I, Chapter II provides guidance on how Trustworthy AI can be realised, by listing seven requirements that AI systems should meet. Both technical and non-technical methods can be used for their implementation.

Key guidance derived from Chapter II:

  • Ensure that the development, deployment and use of AI systems meets the seven key requirements for Trustworthy AI: (1) human agency and oversight, (2) technical robustness and safety, (3) privacy and data governance, (4) transparency, (5) diversity, non-discrimination and fairness, (6) environmental and societal well-being and (7) accountability.
  • Consider technical and non-technical methods to ensure the implementation of those requirements.
  • Foster research and innovation to help assess AI systems and to further the achievement of the requirements; disseminate results and open questions to the wider public, and systematically train a new generation of experts in AI ethics.
  • Communicate, in a clear and proactive manner, information to stakeholders about the AI system’s capabilities and limitations, enabling realistic expectation setting, and about the manner in which the requirements are implemented. Be transparent about the fact that they are dealing with an AI system.
  • Facilitate the traceability and auditability of AI systems, particularly in critical contexts or situations.
  • Involve stakeholders throughout the AI system’s life cycle. Foster training and education so that all stakeholders are aware of and trained in Trustworthy AI.
  • Be mindful that there might be fundamental tensions between different principles and requirements. Continuously identify, evaluate, document and communicate these trade-offs and their solutions.

III. Chapter III provides a concrete and non-exhaustive Trustworthy AI assessment list aimed at operationalising the key requirements set out in Chapter II. This assessment list will need to be tailored to the specific use case of the AI system.

Key guidance derived from Chapter III:

  • Adopt a Trustworthy AI assessmentlist when developing, deploying or using AI systems, and adapt it to the specific use case in which the system is being applied.
  • Keep in mind that such an assessment list will never be exhaustive. Ensuring Trustworthy AI is not about ticking boxes, but about continuously identifying and implementing requirements, evaluating solutions, ensuring improved outcomes throughout the AI system’s lifecycle, and involving stakeholders in this.

A final section of the document aims to concretise some of the issues touched upon throughout the framework, by offering examples of beneficial opportunities thatshould be pursued, and critical concerns raised by AI systems that should be carefully considered.

While these Guidelines aim to offer guidance for AI applications in general by building a horizontal foundation to achieve Trustworthy AI, different situations raise different challenges. It should therefore be explored whether, in addition to this horizontal framework, a sectorial approach is needed, given the context-specificity of AI systems.

These Guidelines do not intend to substitute any form of current or future policymaking or regulation, nor do they aim to deter the introduction thereof. They should be seen as a living document to be reviewed and updated over time to ensure their continuous relevance as the technology, our social environments, and our knowledge evolve. This document is a starting point for the discussion about “Trustworthy AI for Europe”.

Beyond Europe, the Guidelines also aim to foster research, reflection and discussion on an ethical framework for AI systems at a global level.


You can find original Guidelines and the link below:

AI Now Report 2018

AI Now Report 2018


December 2018




  1. Governments need to regulate AI by expanding the powers of sector-specific agencies to oversee, audit, and monitor these technologies by domain. The implementation of AI systems is expanding rapidly, without adequate governance, oversight, or accountability regimes. Domains like health, education, criminal justice, and welfare all have their own histories, regulatory frameworks, and hazards. However, a national AI safety body or general AI standards and certification model will struggle to meet the sectoral expertise requirements needed for nuanced regulation. We need a sector-specific approach that does not prioritize the technology, but focuses on its application within a given domain. Useful examples of sector-specific approaches include the United States Federal Aviation Administration and the National Highway Traffic Safety Administration.
  1. Facial recognition and affect recognition need stringent regulation to protect the public interest. Such regulation should include national laws that require strong oversight, clear limitations, and public transparency. Communities should have the right to reject the application of these technologies in both public and private contexts. Mere public notice of their use is not sufficient, and there should be a high threshold for any consent, given the dangers of oppressive and continual mass surveillance. Affect recognition deserves particular attention. Affect recognition is a subclass of facial recognition that claims to detect things such as personality, inner feelings, mental health, and “worker engagement” based on images or video of faces. These claims are not backed by robust scientific evidence, and are being applied in unethical and irresponsible ways that often recall the pseudosciences of phrenology and physiognomy. Linking affect recognition to hiring, access to insurance, education, and policing creates deeply concerning risks, at both an individual and societal level.
  1. The AI industry urgently needs new approaches to governance. As this report demonstrates, internal governance structures at most technology companies are failing to ensure accountability for AI systems. Government regulation is an important component, but leading companies in the AI industry also need internal accountability structures that go beyond ethics guidelines. This should include rank-and-file employee representation on the board of directors, external ethics advisory boards, and the implementation of independent monitoring and transparency efforts. Third party experts should be able to audit and publish about key systems, and companies need to ensure that their AI infrastructures can be understood from “nose to tail,” including their ultimate application and use.
  1. AI companies should waive trade secrecy and other legal claims that stand in the way of accountability in the public sector. Vendors and developers who create AI and automated decision systems for use in government should agree to waive any trade secrecy or other legal claim that inhibits full auditing and understanding of their software. Corporate secrecy laws are a barrier to due process: they contribute to the “black box effect” rendering systems opaque and unaccountable, making it hard to assess bias, contest decisions, or remedy errors. Anyone procuring these technologies for use in the public sector should demand that vendors waive these claims before entering into any agreements.
  1. Technology companies should provide protections for conscientious objectors, employee organizing, and ethical whistleblowers. Organizing and resistance by technology workers has emerged as a force for accountability and ethical decision making. Technology companies need to protect workers’ ability to organize, whistleblow, and make ethical choices about what projects they work on. This should include clear policies accommodating and protecting conscientious objectors, ensuring workers the right to know what they are working on, and the ability to abstain from such work without retaliation or retribution. Workers raising ethical concerns must also be protected, as should whistleblowing in the public interest.
  1. Consumer protection agencies should apply “truth-in-advertising” laws to AI products and services. The hype around AI is only growing, leading to widening gaps between marketing promises and actual product performance. With these gaps come increasing risks to both individuals and commercial customers, often with grave consequences. Much like other products and services that have the potential to seriously impact or exploit populations, AI vendors should be held to high standards for what they can promise, especially when the scientific evidence to back these promises is inadequate and the longer-term consequences are unknown.
  1. Technology companies must go beyond the “pipeline model” and commit to addressing the practices of exclusion and discrimination in their workplaces. Technology companies and the AI field as a whole have focused on the “pipeline model,” looking to train and hire more diverse employees. While this is important, it overlooks what happens once people are hired into workplaces that exclude, harass, or systemically undervalue people on the basis of gender, race, sexuality, or disability. Companies need to examine the deeper issues in their workplaces, and the relationship between exclusionary cultures and the products they build, which can produce tools that perpetuate bias and discrimination. This change in focus needs to be accompanied by practical action, including a commitment to end pay and opportunity inequity, along with transparency measures about hiring and retention.
  1. Fairness, accountability, and transparency in AI require a detailed account of the “full stack supply chain.” For meaningful accountability, we need to better understand and track the component parts of an AI system and the full supply chain on which it relies: that means accounting for the origins and use of training data, test data, models, application program interfaces (APIs), and other infrastructural components over a product life cycle. We call this accounting for the “full stack supply chain” of AI systems, and it is a necessary condition for a more responsible form of auditing. The full stack supply chain also includes understanding the true environmental and labor costs of AI systems. This incorporates energy use, the use of labor in the developing world for content moderation and training data creation, and the reliance on clickworkers to develop and maintain AI systems.
  1. More funding and support are needed for litigation, labor organizing, and community participation on AI accountability issues. The people most at risk of harm from AI systems are often those least able to contest the outcomes. We need increased support for robust mechanisms of legal redress and civic participation. This includes supporting public advocates who represent those cut off from social services due to algorithmic decision making, civil society organizations and labor organizers that support groups that are at risk of job loss and exploitation, and community-based infrastructures that enable public participation.
  1. University AI programs should expand beyond computer science and engineering disciplines. AI began as an interdisciplinary field, but over the decades has narrowed to become a technical discipline. With the increasing application of AI systems to social domains, it needs to expand its disciplinary orientation. That means centering forms of expertise from the social and humanistic disciplines. AI efforts that genuinely wish to address social implications cannot stay solely within computer science and engineering departments, where faculty and students are not trained to research the social world. Expanding the disciplinary orientation of AI research will ensure deeper attention to social contexts, and more focus on potential hazards when these systems are applied to human populations.


You can find  the full report from the link below:

A Right to Reasonable Inferences: Re-thinking Data Protection Law in the Age of Big Data and AI

A Right to Reasonable Inferences:

Re-thinking Data Protection Law in the Age of Big Data and AI



Sandra Wachter  & Brent Mittelstadt


University of Oxford – Oxford Internet Institute


 September 13, 2018






“Big Data analytics and artificial intelligence (AI) draw non-intuitive and unverifiable inferences and predictions about the behaviours, preferences, and private lives of individuals. These inferences draw on highly diverse and feature-rich data of unpredictable value, and create new opportunities for discriminatory, biased, and invasive decision-making. Concerns about algorithmic accountability are often actually concerns about the way in which these technologies draw privacy invasive and non-verifiable inferences about us that we cannot predict, understand, or refute. Data protection law is meant to protect people’s privacy, identity, reputation, and autonomy, but is currently failing to protect data subjects from the novel risks of inferential analytics. The broad concept of personal data in Europe could be interpreted to include inferences, predictions, and assumptions that refer to or impact on an individual. If seen as personal data, individuals are granted numerous rights under data protection law. However, the legal status of inferences is heavily disputed in legal scholarship, and marked by inconsistencies and contradictions within and between the views of the Article 29 Working Party and the European Court of Justice.

As we show in this paper, individuals are granted little control and oversight over how their personal data is used to draw inferences about them. Compared to other types of personal data, inferences are effectively ‘economy class’ personal data in the General Data Protection Regulation (GDPR). Data subjects’ rights to know about (Art 13-15), rectify (Art 16), delete (Art 17), object to (Art 21), or port (Art 20) personal data are significantly curtailed when it comes to inferences, often requiring a greater balance with controller’s interests (e.g. trade secrets, intellectual property) than would otherwise be the case. Similarly, the GDPR provides insufficient protection against sensitive inferences (Art 9) or remedies to challenge inferences or important decisions based on them (Art 22(3)).

This situation is not accidental. In standing jurisprudence the European Court of Justice (ECJ; Bavarian Lager, YS. and M. and S., and Nowak) and the Advocate General (AG; YS. and M. and S. and Nowak) have consistently restricted the remit of data protection law to assessing the legitimacy of input personal data undergoing processing, and to rectify, block, or erase it. Critically, the ECJ has likewise made clear that data protection law is not intended to ensure the accuracy of decisions and decision-making processes involving personal data, or to make these processes fully transparent.

Conflict looms on the horizon in Europe that will further weaken the protection afforded to data subjects against inferences. Current policy proposals addressing privacy protection (the ePrivacy Regulation and the EU Digital Content Directive) fail to close the GDPR’s accountability gaps concerning inferences. At the same time, the GDPR and Europe’s new Copyright Directive aim to facilitate data mining, knowledge discovery, and Big Data analytics by limiting data subjects’ rights over personal data. And lastly, the new Trades Secrets Directive provides extensive protection of commercial interests attached to the outputs of these processes (e.g. models, algorithms and inferences).

In this paper we argue that a new data protection right, the ‘right to reasonable inferences’, is needed to help close the accountability gap currently posed ‘high risk inferences’ , meaning inferences that are privacy invasive or reputation damaging and have low verifiability in the sense of being predictive or opinion-based. In cases where algorithms draw ‘high risk inferences’ about individuals, this right would require ex-ante justification to be given by the data controller to establish whether an inference is reasonable. This disclosure would address (1) why certain data is a relevant basis to draw inferences; (2) why these inferences are relevant for the chosen processing purpose or type of automated decision; and (3) whether the data and methods used to draw the inferences are accurate and statistically reliable. The ex-ante justification is bolstered by an additional ex-post mechanism enabling unreasonable inferences to be challenged. A right to reasonable inferences must, however, be reconciled with EU jurisprudence and counterbalanced with IP and trade secrets law as well as freedom of expression and Article 16 of the EU Charter of Fundamental Rights: the freedom to conduct a business.”


You can find the link and original paper below: