AI Registry: The Things We’ll Need That Support AI
AI isn’t just about the data and algorithms. To be successful, we as developers and deployers depend on a whole line of supporting elements. This section addresses some, but not all, of those elements, including the right governing policies, the right people, the right data, and the right equipment.
Explore the Four Fails in This Category:
- Good (Grief!) Governance
- Just Add (Technical) People
- Square Data, Round Problem
- My 8-Track Still Works, So What's the Issue?
Good (Grief!) Governance
We sometimes implement AI without a detailed strategy for how it will be governed, and there aren’t any laws that ensure oversight and accountability. In that vacuum, the technology itself is redefining cultural and societal norms.
Without proper governance, and legal accountability and oversight, the technology becomes the de-facto norm. Therefore, we must recognize that because we control the code, we may unintentionally become de-facto decision makers.
Examples
Police departments can purchase crime prediction products that estimate where crimes will occur or who will be involved. Many of the products are “black boxes,” meaning it is not clear how decisions are made, and many police departments deploy them in the absence of clear or publicly available policies to guide how they should be applied.1 Often a new technology is acquired and used first, while policy and governance for its use are developed later.
Employees of a contractor working for Google paid dark-skinned, homeless people $5 for letting the contractor take a picture of their faces in order to make its training dataset more diverse.2 In addition, these workers may have misled the homeless about the purpose of their participation. Without comprehensive legislation about data collection and privacy infringement, ending such questionable practices becomes the responsibility of the governance policies of each company.
Why is this a fail?
AI has reached a state of maturity where governance is a necessary, yet difficult, element. AI systems continue to be increasingly integrated into daily life, but this occurs without adequate governance, oversight, or accountability. This happens in part because:
1. AI is a probabilistic and dynamic process, meaning AI outcomes will not be fully replicable, consistent, and predictable. Therefore, new governance mechanisms must be developed.
2. Organizations allocate money to buy products, but often do not add funds for creating and testing internal governance policies. Therefore, those policies may not be introduced until the effects of the technology’s use have had an impact on people’s lives.
3. Government and private organizations sometimes keep policies that govern AI use and development hidden from the public in order to protect national security interests or trade secrets.3
4. There are no mature AI laws, standards or norms that apply across multiple domains, and laws within a specific domain are only now emerging. Therefore, standardizing policies or sharing best practices face additional obstacles.
The result is that in the United States there are few clear governance models for industry or government to replicate, and there are limited legal authorities that specify whom to hold accountable when things go wrong.4,5
What happens when things fail?
In response to unclear legal accountabilities, organizations have embraced declarations of ethical principles and frameworks that promote responsible AI development.6 These statements vary in detail and specificity, but almost all declare principles of transparency, non-discrimination, accountability, and safety. These current approaches represent important steps, but evidence shows that they are not enough. They are almost universally voluntary commitments, and few of the declarations include recommendations, specifics, or use cases for how to make the principles actionable and implementable (though in the largest AI companies, these are being developed).7 Finally, researchers have shown that pledges to uphold ethical principles do not guarantee ethical behavior.8
In parallel with private efforts, the US government is beginning to define guidance, but it is still in early stages. In January 2020, the White House published draft principles for guiding federal regulatory and non-regulatory approaches to AI,9 and state governments are also getting more involved in regulation.10 However, often state laws are contradictory or lag the technology. As of January 2020, several cities in California and Massachusetts have banned the use of facial recognition technology by public entities,11 but at the same time other US cities, as well as airports and private entities, are increasing their adoption of the same technology.12,13 Because this field of law is so new there are limited precedents.
Absent precedent, AI applications – or more accurately we, the developers –unintentionally create new norms. The dangers that we must keep in mind are that the AI can undermine traditional figures of authority and reshape the rule of law. Without proper governance, and legal accountability and oversight, the technology becomes the de-facto norm. Therefore, we must recognize that because we control the code, we may unintentionally become de-facto decision makers.14
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