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.
1. Principle 1 — Human Rights
Issue: How can we ensure that A IS do not infringe upon human rights?
To best honor human rights, society must assure the safety and security of A IS so that they are designed and operated in a way that benefits humans:
1. Governance frameworks, including standards and regulatory bodies, should be established to oversee processes assuring that the use of A IS does not infringe upon human rights, freedoms, dignity, and privacy, and of traceability to contribute to the building of public trust in A IS.
2. A way to translate existing and forthcoming legal obligations into informed policy and technical considerations is needed. Such a method should allow for differing cultural norms as well as legal and regulatory frameworks.
3. For the foreseeable future, A IS should not be granted rights and privileges equal to human rights: A IS should always be subordinate to human judgment and control.
4. Principle 4 — Transparency
Issue: How can we ensure that A IS are transparent?
Develop new standards* that describe measurable, testable levels of transparency, so that systems can be objectively assessed and levels of compliance determined. For designers, such standards will provide a guide for self assessing transparency during development and suggest mechanisms for improving transparency. (The mechanisms by which transparency is provided will vary significantly, for instance 1) for users of care or domestic robots, a why did you do that button which, when pressed, causes the robot to explain the action it just took, 2) for validation or certification agencies, the algorithms underlying the A IS and how they have been verified, and 3) for accident investigators, secure storage of sensor and internal state data, comparable to a flight data recorder or black box.)
*Note that IEEE Standards Working Group P7001™ has been set up in response to this recommendation.
• Require Accountability for Ethical Design and Implementation
The social implications of computing have grown and will continue to expand as more people have access to implementations of AI. Public policy should work to identify and mitigate discrimination caused by the use of AI and encourage designing in protections against these harms.
• Standing for “Accountable Artificial Intelligence”: Governments, industry and academia should apply the Information Accountability Foundation’s principles to AI. Organizations implementing AI solutions should be able to demonstrate to regulators that they have the right processes, policies and resources in place to meet those principles.
• Transparent decisions: Governments should determine which AI implementations require algorithm explainability to mitigate discrimination and harm to individuals.
4. Adopt a Human In Command Approach
An absolute precondition is that the development of AI must be responsible, safe and useful, where machines maintain the legal status of tools, and legal persons retain control over, and responsibility for, these machines at all times.
This entails that AI systems should be designed and operated to comply with existing law, including privacy. Workers should have the right to access, manage and control the data AI systems generate, given said systems’ power to analyse and utilize that data (See principle 1 in “Top 10 principles for workers’ data privacy and protection”). Workers must also have the ‘right of explanation’ when AI systems are used in human resource procedures, such as recruitment, promotion or dismissal.