A List of AI Governance Levers for Companies
By Yolanda Lannquist, Head of Research & Advisory at The Future Society
Over the past few years AI governance and policy actors including companies, regulators and academics have put forward many different approaches to ensure technology companies’ AI development and applications have appropriate ethical and safety precautions. I’ve gathered these into a list that doesn’t aim to analyze but rather to inform all possible options in a ‘toolbox’. In practice, a combination is needed since each brings strengths and weaknesses and is relevant for different objectives and contexts. It’s a live list that will be continuously updated; please comment with any new suggestions!
- Regulation (e.g. EU’s GDPR, California Consumer Privacy Act (CCPA), San Francisco restrictions or bans on government use of facial recognition technologies, California’s requirement for bots disclosure)
- Funding research grants or projects for safe, explainable, or otherwise ethical AI
- Public procurement requirements include ethical criteria (e.g. Canada’s list of pre-qualified responsible AI suppliers, The AI-RFX Procurement)
- Sector-specific policy guidance or frameworks (e.g. U.S. Draft Memorandum for the Heads of Departments and Agencies: Guidance for Regulation of Artificial Intelligence Applications)
- Endorsing internal ethical principles (e.g. Microsoft’s AI Principles)
- Endorsing external ethical principles (e.g. Asilomar Principles, Montreal Declaration for Responsible AI, OECD AI Principles, European Commissions High-Level Expert Group on AI’s Ethics Guidelines for Trustworthy AI, etc.).
- Voluntary labeling of AI products & services as complying with the above principles, e.g. EU Ethics Guidelines for Trustworthy AI
- Internal ‘Ethics officers’ or Chief Ethics Officer
- Technology advisory bodies/councils/committees comprised of in-house or external advisors (e.g. Microsoft AETHER Committee)
- Reference for board of directors or C-Suite oversight (E.g. WEF Oversight Toolkit for Boards of Directors)
- Mandatory ethics courses in-house
- Ethics built into design in engineering projects
- Technical tools to simplify mitigating risks (e.g. Interpret ML, The AI Fairness 360 Toolkit, The TensorFlow Privacy library)
- Documentation about AI systems to list characteristics or benchmark evaluation of ML models: ‘model cards’ (e.g. Google), ‘nutrition labels’, Partnership on AI’s ABOUT ML or data sets e.g. ‘datasheets’ explaining data collection, processing and composition for datasets.
- Reference to a checklist (e.g. The Assessment List of the Ethics Guidelines for Trustworthy AI) or models (e.g. The a3i Trust-in-AI framework) for trustworthy AI systems
Third parties & standards associations
- Technical standards or guidelines e.g. IEEE P7000™ Standards Series or ISO
- Third party audits (e.g. independent audits of algorithmic systems)
- Certifications for staff operationalizing AI systems (mandatory or voluntary) (e.g. IEEE Ethics Certification Program for Autonomous and Intelligent Systems (ECPAIS))
- Technology review boards (external)
- Toolkit or resources for advocates to audit and support transparency and accountability (e.g. AI Now Institute Algorithmic Accountability Policy Toolkit)
- Insurance policies
- Courses to educate the public about AI to better hold AI developers accountable (e.g. Finland’s Elements of AI course)
- Fear of public backlash, loss of consumer trust, and negative press or social media
- Positive press and praise for companies being ethical
- Ethics coursework in computer science courses
- Processes for employees for whistle-blowing or denying unethical projects
Decentralized and distributed technology solutions
- Incentive mechanisms including based on cryptoeconomics
Presentations and publications by the author, Nicolas Miailhe, Jessica Cussins Newman, Professor Wendell Wallach, Professor Allan Dafoe, Professor Francesca Rossi and the AI Now Institute.
Special acknowledgement for the forthcoming publication from which I’ve gathered several examples: Jessica Cussins Newman, 2020, AI Decision Points: Three case studies explore efforts to operationalize AI principles. UC Berkeley Center for Long-Term Cybersecurity White Paper Series [hyperlink forthcoming].
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Cover photo from William Santo on Unsplash