We aim to act responsibly, create and promote an AI that is lawful, ethical, inclusive and robust. It is important to ensure the protection of fundamental (human) rights and user safety.
To that end, this document gathers a set of concrete guidelines, that are structured around 7 principles. Those principles about AI trustworthiness were primarily identified by the European Union (EU). The reason why we tackle AI trustworthiness by complying with these principles is that we believe they provide a relevant overview of all components involved in the development of an AI system.
The primary audience of this document is AI practitioners, especially because it conveys a set of technical guidelines. However, concepts are always introduced on a high level scale, before being tackled on a lower level - and technical - scale. As a result, we strongly encourage non-AI practitioners to also read it.
As stated by the EU, “human oversight ensures that an AI system does not undermine human autonomy or cause other adverse effects”.
Guidelines depending on the AI system life cycle: AI contributors should always assess “why” they set up an AI system prior to actually doing it ; and users of an AI system must be able to control it.
Risk & criticality: AI contributors must evaluate the risk and criticality of an AI system before starting to implement it, namely during ideation phases, among following risks: Unacceptable Risk, High Risk, Limited Risk, Minimal Risk.
Practical guidelines: https://datacraft-paris.github.io/trustworthyai/human.html
Trustworthy AI must ensure that privacy, as a fundamental right, is respected for all parties – users, individuals targeted or employees. Quality and integrity of the data must also be protected, and access to data must be regulated.
Respect for privacy and data protection: Any system gathering data must ensure that the consent of the user is respected, and his privacy protected.
Quality and integrity of data: To avoid any harmful consequence of an AI system, the quality and integrity of the data must be ensured at any time
Access to data: As a general principle, only people with valid reasons should access each dataset
Practical guidelines: https://datacraft-paris.github.io/trustworthyai/privacydatagov.html
Development robustness: AI contributors must comply with development standards to ease reliability, readability of procedures and handovers.
Data project lifecycle safety:
Security: Artificial Intelligence is no exception when it comes to security, AI systems are exposed to a wide range of attacks. Consequently, AI contributors must be sensitised to Machine Learning security and enforce security audits, especially when humans are impacted by an AI system decision.
Practical guidelines: https://datacraft-paris.github.io/trustworthyai/robustnesssafety.html
Transparency and explicability are two fundamental and distinct notions when it comes to AI trustworthiness. While explicability aims at understanding the logic behind an algorithm, transparency aims at making “public” (totally or partially - within the scope of an organisation e.g.) the AI system and anything that lives with it (data, …).
Communication: AI contributors should always clearly and timely communicate results to stakeholders.
AI contributor’s management: AI contributors should always be put in a favourable condition by the management to give the alert in case of fraudulent, illegal, illegitimate, discriminatory or unethical results.
AI explicability: AI explicability aims at creating numerical methods to extract the logics behind an AI system’s decision. AI contributors must be sensitised to understand results output by an AI system and study both global interpretation (at model level) and local interpretation (at individual level). This balance is the only way to achieve an exhaustive understanding of the AI system’s behavior, which in turns unlocks trust in the AI system.
AI transparency: While creating an AI system, AI contributors should enforce the system’s transparency to challenge the AI system’s weaknesses, challenge potential pitfalls of the training data, which in turn helps to check legal & ethical compliance, and to protect individuals.
Practical guidelines: https://datacraft-paris.github.io/trustworthyai/transparencyexplicability.html
AI contributors must foster the creation of bias-free AI systems and give themselves the means to reasonably arbitrage between performance and fairness, through the use of proper frameworks. We will discuss an AI project lifecycle through the eyes of fairness, and explain how one can detect discrimination through statistical methods.
AI Project lifecycle
Identification metrics and ways for correcting discrimination bias and unfairness
Practical guidelines: https://datacraft-paris.github.io/trustworthyai/diversitynondiscrimination.html
The EU Ethical AI Guidelines notes the following on this topic:
“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.”
This guideline, although a bit philosophical, means that we should be raising awareness of the impact of AI on the environment and societal well-being. The following table has the purpose to raise this awareness and it mentions some practical steps that AI contributors may take in this regard.
Practical guidelines: https://datacraft-paris.github.io/trustworthyai/environmentalsocietal.html
Mechanisms must be put in place to ensure responsibility and accountability for AI systems and their outcomes, both before and after their deployment.
Practical guidelines: https://datacraft-paris.github.io/trustworthyai/accountability.html