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.
2) Research Funding
Published by: Future of Life Institute (FLI), Beneficial AI 2017 in Asilomar AI Principles
Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as:
How can we make future AI systems highly robust, so that they do what we want without malfunctioning or getting hacked?
How can we grow our prosperity through automation while maintaining people’s resources and purpose?
How can we update our legal systems to be more fair and efficient, to keep pace with AI, and to manage the risks associated with AI?
What set of values should AI be aligned with, and what legal and ethical status should it have?
2. The Principle of Non maleficence: “Do no Harm”
Published by: The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI
AI systems should not harm human beings. By design, AI systems should protect the dignity, integrity, liberty, privacy, safety, and security of human beings in society and at work. AI systems should not threaten the democratic process, freedom of expression, freedoms of identify, or the possibility to refuse AI services. At the very least, AI systems should not be designed in a way that enhances existing harms or creates new harms for individuals. Harms can be physical, psychological, financial or social. AI specific harms may stem from the treatment of data on individuals (i.e. how it is collected, stored, used, etc.). To avoid harm, data collected and used for training of AI algorithms must be done in a way that avoids discrimination, manipulation, or negative profiling. Of equal importance, AI systems should be developed and implemented in a way that protects societies from ideological polarization and algorithmic determinism.
Vulnerable demographics (e.g. children, minorities, disabled persons, elderly persons, or immigrants) should receive greater attention to the prevention of harm, given their unique status in society. Inclusion and diversity are key ingredients for the prevention of harm to ensure suitability of these systems across cultures, genders, ages, life choices, etc. Therefore not only should AI be designed with the impact on various vulnerable demographics in mind but the above mentioned demographics should have a place in the design process (rather through testing, validating, or other).
Avoiding harm may also be viewed in terms of harm to the environment and animals, thus the development of environmentally friendly AI may be considered part of the principle of avoiding harm. The Earth’s resources can be valued in and of themselves or as a resource for humans to consume. In either case it is necessary to ensure that the research, development, and use of AI are done with an eye towards environmental awareness.
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.
1. Demand That AI Systems Are Transparent
A transparent artificial intelligence system is one in which it is possible to discover how, and why, the system made a decision, or in the case of a robot, acted the way it did.
A. We stress that open source code is neither necessary nor sufficient for transparency – clarity cannot be obfuscated by complexity.
B. For users, transparency is important because it builds trust in, and understanding of, the system, by providing a simple way for the user to understand what the system is doing and why.
C. For validation and certification of an AI system, transparency is important because it exposes the system’s processes for scrutiny.
D. If accidents occur, the AI will need to be transparent and accountable to an accident investigator, so the internal process that led to the accident can be understood.
E. Workers must have the right to demand transparency in the decisions and outcomes of AI systems as well as the underlying algorithms (see principle 4 below). This includes the right to appeal decisions made by AI algorithms, and having it reviewed by a human being.
F. Workers must be consulted on AI systems’ implementation, development and deployment.
G. Following an accident, judges, juries, lawyers, and expert witnesses involved in the trial process require transparency and accountability to inform evidence and decision making.
The principle of transparency is a prerequisite for ascertaining that the remaining principles are observed.
See Principle 2 below for operational solution.