The challenge is that human values are typically difficult to learn, since they can be based on complex mental processes, can be working on multiple timescales, can be difficult to put on one value scale, can involve both intuition and reasoning and may involve other interactions such as signalling and trust-building. Furthermore, they require ontological agreement between human and machine: do they see the world in the same way? Many of these problems are shared with technical AI work (e.g. computer vision) but for use in ethical systems much more work is needed. Against learning from scratch The value learning problem is difficult for many reasons. In addition, any type of purely statistical learning procedure faces other difficulties related to opacity and the limited possibilities to employ knowledge one might have about a domain. However, there are machine learning techniques that allow for the insertion of knowledge as a bias for learning, and the extraction of learnt knowledge after learning. Consider the robot learning technique by Moldovan et al. (2012) where a robot needs to learn from demonstration how physical objects are to be manipulated and how they behave when manipulating. Without any prior knowledge, the robot would have quite a challenging learning problem, mapping the pixels of its cameras all the way to motor commands in its hands. Instead, by adding some common- sense knowledge about the world, like "if you move object A, and object B is far away, then you can safely assume B will not be affected", or "if you want to manipulate an object, you can either push, tap, or grab". This type of knowledge will make the learning problem easier and at the same time it focuses (or: biases) the learning efforts on the things that really matter. Other, general common-sense knowledge could also help in choosing the right behavior (based on a reward function) such as "green objects are typically heavy", and "one cannot place an object on a ball-shaped object". In machine learning we call this kind of bias declarative, since it is knowledge that can be explicitly used, stored, and "looked at". Declarative models have been used before in ethical reasoning in AI (Anderson and Anderson, 2007) and other ethical studies (van Otterlo, 2014a). In order for inserting knowledge to work, we need to solve the ontological issue: knowledge should be at the right level and meanings should mean the "same" for AI and for humans. To bridge AI and human (cognitive) thinking, the rational agent view is a suitable view. In AI, a rational agent is "one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome" (Russell and Norvig, 2009). In cognitive science we can take the intentional stance view introduced by Daniel Dennett (2013). The intentional stance sees entities as rational agents having mental notions such as beliefs, goals and desires. Using this viewpoint, we assume the agent takes into account such beliefs and desires to optimize its behavior. For people this is the most intuitive form of description of other people's behavior. But, it is also common to use it to talk about algorithms: I can say that Google believes I like Lego and therefore it desires to feed me advertisements about it and sets a goal to prioritize websites referring to Lego. I can also say that Google believes that I want pizza when I enter "food" as a query since it knows from my profile it is my favourite food. Code of ethics as a moral contract between humans and machines Coming back to the archivist singularity mentioned in the introduction, I propose a simple strategy to construct Paul, the Intentional Algivist as a robotic, algorithmic agent for the archives that has moral principles just like human archivists. What could be better declarative, human knowledge about ethical values in the archival domain than the previously discussed archival codes of ethics? Indeed, these hold general consensus ideas on how an archivist should behave ethically, dealing with issues such as privacy, access, and fair use of the archive. In addition, they are full of intentional descriptions, see for example: "The Archivist should endeavour to promote access to records to the fullest extent consistent with the public interest, but he should carefully observe any proper restrictions on the use of records". This is clearly a bias on how the algivist should behave and it contains intentional constructs such as a goal, a desire and several (implicit) beliefs. Codes of ethics are solid knowledge bases of the most important ethical guidelines for the profession, and typically they are defined to be transparent, human-readable and public. Using codes of ethics as a knowledge bias in adaptive algivists that learn ethical behavior is natural, since it merely translates (through the rational agent connection) an ethical code that was designed as a bias for human behavior, and uses that as a guide or constraint, or: as a moral contract between man and machine. I see a practical way to go in which an algivist is endowed with the ethical values contained in the code of ethics, after which it observes human archivists at work to fine-tune its behavior based on their example. Human archivists will slowly transform into trainers and coaches of algivists: the more advanced algivists become, the more humans will guide them and leave the archival work to them. But, before this happens, much still needs to be done, both by AI researchers as well as by archivists themselves. What does the field of AI need to do? AI needs to keep on progressing as always, but more research is needed on several aspects specifically. Language understanding and formalization of human (common-sense) knowledge needs to be improved to translate codes of ethics automatically in forms that the algivist can use for acting, and for reasoning. We know that even the impossible Roadrunner cartoon logic has at some point been formalized (McCartney and Anderson, 1996), so nothing is impossible. Furthermore, robotic skills need to improve a lot. Manipulation skills are somewhat sufficient for laboratory conditions (e.g. Moldovan et al., 2012) and there has been some progress in - for archivists, related - environments such as libraries93, but obtaining general movement and object manipulation skills in any physical archive will take enormous efforts still. Once parts of the archive have been made digital, many of the archival selection, ordering and description tasks can be handled well, although also there much improvement is possible in the semantic understanding of documents, images, and other items. What do archivists need to do? Archivists will need to assist AI researchers as experts in archives, and they need to decide at least two things. The ethics of choosing THE code of ethics: The core idea is to inject ethical codes into machines. Out of the many possible versions, which one should be picked? And who decides upon that? Archivists, committees of experts, programmers, or more general democratic methods? For this to work, we may also need to investigate more which kinds of values hold in professions as held by archivists and librarians. archives in liquid times 286 martijn van otterlo from intended archivists to intentional algivists. ethical codes for humans and machines in the archives 93 https://phys.org/news/2016-06-automated-robot-scans-library-shelves.html 287

Periodiekviewer Koninklijke Vereniging van Archivarissen

Jaarboeken Stichting Archiefpublicaties | 2017 | | pagina 145