The European ethics charter on the use of artificial intelligence in Judicial Systems and their environment was authored by the Council of Europe´s European Commission for the Efficiency of Justice CEPEJ and was published in France in December 2018. The charter aims to align regional efforts by defining a set of principles governing the design of artificial intelligence solutions, and their use in the context of the judicial system; based on the International Law of Human Rights.
As the charter was produced by joint authorship, gathering members of several government bodies from several European countries I have classified the author type as “Intergovernmental Organization”. Also, in the light of the objective pursued by the charter, and the nature of the principles it proposes, I have classified the document type as “Policies for Use”. Both classifications will allow future contrasts between documents and authors of the same type; enriching the analysis that I aim to present in this series of posts.
The principles proposed within the charter are listed below:
- Principle of respect for fundamental rights: ensure that the design and implementation of artificial intelligence tools and services are compatible with fundamental rights,
- Principle of non-discrimination: specifically prevent the development or intensification of any discrimination between individuals or groups of individuals,
- Principle of quality and security: with regard to the processing of judicial decisions and data, use certified sources and intangible data with models elaborated in a multi-disciplinary manner, in a secure technological environment,
- Principle of transparency, impartiality, and fairness: make data processing methods accessible and understandable, authorize external audits, and
- Principle “under user control”: preclude a prescriptive approach and ensure that users are informed actors and in control of the choices made.
From my computer science background, I find it difficult to adopt these principles as a methodological reference without them being subject to additional layers of interpretation, and integration into tools such as standards or checklists, to name a few examples. On the one hand, standards would support the assurance of expected outcomes of the artificial intelligence solutions since early development stages in accordance with the framework scope delimited by the proposed principles; and, on the other hand, checklists are an effective tool in the verification stages, used to whereas the designed solution complied with the proposed principles – using the same examples -.
In that same line of thoughts, from my experience defining checklists, and as a member of international software development standards designing working groups, I can highlight the following elements:
- The operationalization of the variables fundamental rights are based on, and artificial intelligence solutions are expected to comply with within the environment described by the charter being reviewed,
- The definition of the environment framing the possible discriminations to which an individual or groups of individuals may be exposed to given the attributes included on each decision; consisting of a finite number given their typology according to the environment enclosed within the charter,
- The definition of the current causal discrimination variables´ neighbor environments an individual or groups of individuals may be subject to according to the attributes included on each decision; given that discrimination is a variable phenomenon with – nonexclusive – temporal, geographical, and cultural dimensions.
- The definition of variables that can be integrated into metrics to assess the different intensity levels in possibly discriminatory decisions which individuals or groups of individuals may be exposed to,
- The creation of a certifying authority refereeing the adequacy of data sources used in support of the decision-making process when delivering justice,
- The creation of a certifying authority evaluating the suitability of team members, and the completeness of the multidisciplinary team, designers of the models for the processing of data to be used in support of the decision-making process while delivering justice,
- The operationalization of “accessibility” and “understanding” as dependent variables for understanding the methods used on data processing, which can be used in the definition of a metric that assesses the levels at which understanding of methods can be expressed, by potential stakeholders and auditors,
- The creation of a competent auditing authority certifying the adherence of the design team – of artificial intelligence solutions for the use of the justice administrators within the environment delimited in the charter – with the proposed principles,
- The definition of parameters that can be integrated into constraints within the artificial intelligence solution´s reasoning model, while in design stages, to avoid recommending decisions describing a prescriptive approach as per the context delimited by the charter, and
- The definition of parameters that can be integrated into metrics evaluating the prescriptive nature of the approach described by the artificial intelligence solutions´ recommended decisions within the charter delimited environment.
As necessary intermediate layers towards the principle’s adoption as a methodological reference for the design of artificial intelligence solutions in the administration of justice.
After an analysis of the language used in the document, in which I used the NLTK library and Python´s development environment for extracting the 50 most frequent n-grams from the charter´s body text it turned out that:
- The uni-grams with relative frequencies greater than .50 units described the environment delimited in the letter and not the objective intended with the principles proposal, or the variables in which they are expressed: Judicial (.87), Decisions (.82), Law (.72), Processing (.63), Legal (.62), Case (.56), Public/ Tools/ Judges/ Use (.53), and Justice (.52),
- The bi-grams, however, begin to delimit the charter´s scope in the context described by the uni-grams, displaying, with higher relative frequencies the terms: Machine Learning (.31), Judicial Decisions (.28), Artificial Intelligence/ Open Data (.27), Judicial Systems (.20) and Personal Data (.19). Although, other variables like Data Protection and Fair Trial exhibit lower values, .6 and .5 units of relative frequency, respectively.
- The tri-grams, on the other hand, connect both the environment and scope using the following text compositions: Protection Personal Data (.09), Artificial Intelligence Tools/ Processing Judicial Decisions/ Judicial Decisions Data (.06), and
- It is interesting how, through the identified trigrams: Use Artificial Intelligence/ Intelligence Tool Services/ Predictive Justice Tools and Checklist Evaluating Processing/ Evaluating Processing Methods, all with a relative frequency of .05; the letter itself points to the need for tools like the ones mentioned earlier in this post.
I would like to conclude by saying that the charter addressed in this post along with other documents I will further include in this series constitutes an effort to solve some of the ethical problems rooted in the design and use of artificial intelligence solutions. In this case, specifically in the context of the administration of justice; the remaining documents will include other scenarios. Also, I would like to add that, with this reading exercise I seek to draw attention to the opportunity of public policy designers and designers of artificial intelligence solutions to collaborate in the achievement of a common goal: what is the responsible design of artificial intelligence.
If you are interested in this topic and have any idea that complements this review of the European Ethical Letter on the Use of Artificial Intelligence in Judicial Systems and its environment let me know with a comment.
Image is taken from pixabay.