EU guidelines on ethics in Artificial Intelligence
Let’s look at how EU wants to policy Artificial Intelligence.
Policy-makers all over the globe are looking at how to tackle the risks associated with the development of AI. In April 2019, the EU published its guidelines on ethics in AI, becoming a frontrunner in the setting up of a framework for AI. Ethical rules on AI, where such exist, are so far essentially of a self-regulatory nature, and there is growing demand for more government oversight. In the EU, there are strong calls for clarifying the EU guidelines, fostering the adoption of ethical standards and adopting legally biding instruments in order to, inter alia, set common rules on transparency, set common requirements for fundamental rights impact assessments and provide an adequate legal framework for face recognition technology.
Should AI be regulated?
The discussion around artificial intelligence (AI) technologies and their impact on society is increasingly focused on the question of whether AI should be regulated. Following the call from the European Parliament to update and complement the existing Union legal framework with guiding ethical principles, the EU has carved out a ‘human-centric’ approach to AI that is respectful of European values and principles. As part of this approach, the EU published its guidelines on ethics in AI in April 2019, and European Commission President-elect, Ursula von der Leyen, has announced that the Commission will soon put forward further legislative proposals for a coordinated European approach to the human and ethical implications of AI.
What is Artificial Intelligence and why it might be a risk?
Artificial intelligence (AI) commonly refers to a combination of: machine learning techniques used for searching and analysing large volumes of data; robotics dealing with the conception, design, manufacture and operation of programmable machines; and algorithms and automated decision making systems (ADMS) able to predict human and machine behaviour and to make autonomous decisions. AI technologies can be extremely beneficial from an economic and social point of view and are already being used in areas such as healthcare (for instance, to find effective treatments for cancer) and transport (for instance, to predict traffic conditions and guide autonomous vehicles), or to efficiently manage energy and water consumption. AI increasingly affects our daily lives, and its potential range of application is so broad that it is sometimes referred to as the fourth industrial revolution.
However, while most studies concur that AI brings many benefits, they also highlight a number of ethical, legal and economic concerns, relating primarily to the risks facing human rights and fundamental freedoms. For instance, AI poses risks to the right to personal data protection and privacy, and equally so a risk of discrimination when algorithms are used for purposes such as to profile people or to resolve situations in criminal justice. There are also some concerns about the impact of AI technologies and robotics on the labour market (e.g. jobs being destroyed by automation). Furthermore, there are calls to assess the impact of algorithms and automated decision making systems (ADMS) in the context of defective products (safety and liability), digital currency (blockchain), disinformation-spreading (fake news) and the potential military application of algorithms (autonomous weapons systems and cybersecurity). Finally, the question of how to develop ethical principles in algorithms and AI design has also been raised.
Guidelines for AI
The Guidelines put forward a set of 7 key requirements that AI systems should meet in order to be deemed trustworthy. A specific assessment list aims to help verify the application of each of the key requirements:
- Human agency and oversight: AI systems should empower human beings, allowing them to make informed decisions and fostering their fundamental rights. At the same time, proper oversight mechanisms need to be ensured, which can be achieved through human-in-the-loop, human-on-the-loop, and human-in-command approaches
- Technical Robustness and safety: AI systems need to be resilient and secure. They need to be safe, ensuring a fall back plan in case something goes wrong, as well as being accurate, reliable and reproducible. That is the only way to ensure that also unintentional harm can be minimized and prevented.
- Privacy and data governance: besides ensuring full respect for privacy and data protection, adequate data governance mechanisms must also be ensured, taking into account the quality and integrity of the data, and ensuring legitimised access to data.
- Transparency: the data, system and AI business models should be transparent. Traceability mechanisms can help achieving this. Moreover, AI systems and their decisions should be explained in a manner adapted to the stakeholder concerned. Humans need to be aware that they are interacting with an AI system, and must be informed of the system’s capabilities and limitations.
- Diversity, non-discrimination and fairness: Unfair bias must be avoided, as it could could have multiple negative implications, from the marginalization of vulnerable groups, to the exacerbation of prejudice and discrimination. Fostering diversity, AI systems should be accessible to all, regardless of any disability, and involve relevant stakeholders throughout their entire life circle.
- Societal and environmental well-being: AI systems should benefit all human beings, including future generations. It must hence be ensured that they are sustainable and environmentally friendly. Moreover, they should take into account the environment, including other living beings, and their social and societal impact should be carefully considered.
- Accountability: Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes. Auditability, which enables the assessment of algorithms, data and design processes plays a key role therein, especially in critical applications. Moreover, adequate an accessible redress should be ensured.
Conclusion on regulation
In this context, it is important to build AI systems that are worthy of trust, since human beings will only be able to confidently and fully reap its benefits when the technology, including the processes and people behind the technology, are trustworthy.
Trustworthy AI has three components:
- it should be lawful, ensuring compliance with all applicable laws and regulations,
- it should be ethical, ensuring adherence to ethical principles and values and
- it should be robust, both from a technical and social perspective since to ensure that, even with good intentions, AI systems do not cause any unintentional harm.
Each component is necessary but not sufficient to achieve Trustworthy AI. Ideally, all three components work in harmony and overlap in their operation. Where tensions arise, we should endeavour to align them.
Europe has a unique vantage point based on its focus on placing the citizen at the heart of its endeavours. This focus is written into the very DNA of the European Union through the Treaties upon which it is built. The current document forms part of a vision that promotes Trustworthy AI which we believe should be the foundation upon which Europe can build leadership in innovative, cutting-edge AI systems. This ambitious vision will help securing human flourishing of European citizens, both individually and collectively. Our goal is to create a culture of “Trustworthy AI for Europe”, whereby the benefits of AI can be reaped by all in a manner that ensures respect for our foundational values: fundamental rights, democracy and the rule of law.
Disclaimer: this text is based upon two texts from European Parlament:
- Ethics guidelines for trustworthy AI
- EU guidelines on ethics in artificial intelligence
with a goal to popularise what’s being currently discussed in policy-making when it comes to Artificial Intelligence.