As AI increasingly changes the nature of work, workers, customers and vendors need to have information about how AI systems operate so that they can understand how decisions are made. Their involvement will help to identify potential bias, errors and unintended outcomes. Transparency is not necessarily nor only a question of open source code. While in some circumstances open source code will be helpful, what is more important are clear, complete and testable explanations of what the system is doing and why. Intellectual property, and sometimes even cyber security, is rewarded by a lack of transparency. Innovation generally, including in algorithms, is a value that should be encouraged. How, then, are these competing values to be balanced? One possibility is to require algorithmic verifiability rather than full algorithmic disclosure. Algorithmic verifiability would require companies to disclose not the actual code driving the algorithm but information allowing the effect of their algorithms to be independently assessed. In the absence of transparency regarding their algorithms’ purpose and actual effect, it is impossible to ensure that competition, labour, workplace safety, privacy and liability laws are being upheld. When accidents occur, the AI and related data will need to be transparent and accountable to an accident investigator, so that the process that led to the accident can be understood.
3. New technology, including AI systems, must be transparent and explainable
For the public to trust AI, it must be transparent. Technology companies must be clear about who trains their AI systems, what data was used in that training and, most importantly, what went into their algorithm’s recommendations. If we are to use AI to help make important decisions, it must be explainable.
5 DEMOCRATIC PARTICIPATION PRINCIPLE
AIS must meet intelligibility, justiﬁability, and accessibility criteria, and must be subjected to democratic scrutiny, debate, and control.
1) AIS processes that make decisions affecting a person’s life, quality of life, or reputation must be intelligible to their creators.
2) The decisions made by AIS affecting a person’s life, quality of life, or reputation should always be justiﬁable in a language that is understood by the people who use them or who are subjected to the consequences of their use. Justiﬁcation consists in making transparent the most important factors and parameters shaping the decision, and should take the same form as the justiﬁcation we would demand of a human making the same kind of decision.
3) The code for algorithms, whether public or private, must always be accessible to the relevant public authorities and stakeholders for veriﬁcation and control purposes.
4) The discovery of AIS operating errors, unexpected or undesirable effects, security breaches, and data leaks must imperatively be reported to the relevant public authorities, stakeholders, and those affected by the situation.
5) In accordance with the transparency requirement for public decisions, the code for decision making algorithms used by public authorities must be accessible to all, with the exception of algorithms that present a high risk of serious danger if misused.
6) For public AIS that have a signiﬁcant impact on the life of citizens, citizens should have the opportunity and skills to deliberate on the social parameters of these AIS, their objectives, and the limits of their use.
7) We must at all times be able to verify that AIS are doing what they were programmed for and what they are used for.
8) Any person using a service should know if a decision concerning them or affecting them was made by an AIS.
9) Any user of a service employing chatbots should be able to easily identify whether they are interacting with an AIS or a real person.
10) Artiﬁcial intelligence research should remain open and accessible to all.
2. A.I. must be transparent
We should be aware of how the technology works and what its rules are. We want not just intelligent machines but intelligible machines. Not artificial intelligence but symbiotic intelligence. The tech will know things about humans, but the humans must know about the machines. People should have an understanding of how the technology sees and analyzes the world. Ethics and design go hand in hand.
2. Transparent and explainable AI
We will be explicit about the kind of personal and or non personal data the AI systems uses as well as about the purpose the data is used for. When people directly interact with an AI system, we will be transparent to the users that this is the case.
When AI systems take, or support, decisions we take the technical and organizational measures required to guarantee a level of understanding adequate to the application area. In any case, if the decisions significantly affect people's lives, we will ensure we understand the logic behind the conclusions. This will also apply when we use third party technology.