ACT | The App Association’s Policy Principles for Artificial Intelligence

Artificial intelligence (AI) is an evolving constellation of technologies that enable computers to simulate 

elements of human thinking, such as learning and reasoning. An encompassing term, AI entails a range 

of approaches and technologies, such as machine learning (ML), where algorithms use data, learn from 

it, and apply their newly-learned lessons to make informed decisions, and deep learning, where an 

algorithm based on the way neurons and synapses in the brain change as they are exposed to new 

inputs allows for independent or assisted decision-making. Already, AI-driven algorithmic decision tools 

and predictive analytics have substantial direct and indirect effects in consumer and enterprise context,

and show no signs of slowing in the future. 

Across use cases and sectors, AI has incredible potential to improve consumers’ lives through faster 

and better-informed decision-making, enabled by cutting-edge distributed cloud computing. Even now, 

consumers are encountering AI in their lives incrementally through the improvements they have seen in 

computer-based services they use, typically in the form of streamlined processes, image analysis, and 

voice recognition, all forms of what we consider “narrow” AI. These narrow applications of AI already

provide great societal benefit. As AI systems, powered by streams of data and advanced algorithms, 

continue to improve services and generate new business models, the fundamental transformation of 

economies across the globe will only accelerate. 

Nonetheless, AI also has the potential to raise a variety of unique considerations for policymakers. ACT 

| The App Association appreciates the efforts to develop a policy approach to AI that will bring its 

benefits to all, balanced with necessary safeguards to protect consumers.

To guide policymakers, we recommend the following principles for action: 

1. National AI Strategies: Many of the policy issues raised below involve significant work and 

changes that will impact a range of stakeholders. The cultural, workforce training and education, 

data access, and technology-related changes associated with AI will require strong guidance 

and coordination. National AI strategies incorporating guidance on the issues below will be vital 

to achieving the promise that AI offers to consumers and entire economies. We believe it is 

critical that countries also take this opportunity to encourage civil society organizations and 

private sector stakeholders to begin similar work. 

2. Research: Policy frameworks should support and facilitate research and development of AI by 

prioritizing and providing sufficient funding while also ensuring adequate incentives (e.g.,

streamlined availability of data to developers, tax credits) are in place to encourage private and 

non-profit sector research. Transparency research should be a priority and involve collaboration 

among all affected stakeholders who must responsibly address the ethical, social, economic, 

and legal implications that may result from AI applications.
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3. Quality Assurance and Oversight: Policy frameworks should utilize risk-based approaches to 

ensure that the use of AI aligns with the recognized standards of safety, efficacy, and equity. 

Providers, technology developers and vendors, and other stakeholders all benefit from 

understanding the distribution of risk and liability in building, testing, and using AI tools. Policy 

frameworks addressing liability should ensure the appropriate distribution and mitigation of risk 

and liability. Specifically, those in the value chain with the ability to minimize risks based on their 

knowledge and ability to mitigate should have appropriate incentives to do so. Some 

recommended guidelines include: 

• Ensuring AI is safe, efficacious, and equitable. 

• Supporting that algorithms, datasets, and decisions are auditable.

• Encouraging AI developers to consistently utilize rigorous procedures and enabling them 

to document their methods and results. 

• Requiring those developing, offering, or testing AI systems to provide truthful and easy 

to understand representations regarding intended use and risks that would be 

reasonably understood by those intended, as well as expected, to use the AI solution. 

• Ensuring that adverse events are timely reported to relevant oversight bodies for 

appropriate investigation and action. 

4. Thoughtful Design: Policy frameworks should require design of AI systems that are informed 

by real-world workflows, human-centered design and usability principles, and end-user needs. 

AI systems solutions should facilitate a transition to changes in the delivery of goods and 

services that benefit consumers and businesses. The design, development, and success of AI

should leverage collaboration and dialogue among users, AI technology developers, and other 

stakeholders in order to have all perspectives reflected in AI solutions. 

5. Access and Affordability: Policy frameworks should ensure AI systems are accessible and 

affordable. Significant resources may be required to scale systems. Policymakers should take 

steps to remedy the uneven distribution of resources and access and put policies in place that 

incent investment in building infrastructure, preparing personnel and training, as well as 

developing, validating, and maintaining AI systems with an eye toward ensuring value. 

6. Ethics: The success of AI depends on ethical use. A policy framework will need to promote 

many of the existing and emerging ethical norms for broader adherence by AI technologists, 

innovators, computer scientists, and those who use such systems. Policy frameworks should:

• Ensure that AI solutions align with all relevant ethical obligations, from design to 

development to use. 

• Encourage the development of new ethical guidelines to address emerging issues with 

the use of AI, as needed. 

• Maintain consistency with international conventions on human rights. 

• Ensure that AI is inclusive such that AI solutions beneficial to consumers are developed 

across socioeconomic, age, gender, geographic origin, and other groupings. 

• Reflect that AI tools may reveal extremely sensitive and private information about a user

and ensure that laws protect such information from being used to discriminate against 

certain consumers. 
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7. Modernized Privacy and Security Frameworks: While the types of data items analyzed by AI 

and other technologies are not new, this analysis will provide greater potential utility of those 

data items to other individuals, entities, and machines. Thus, there are many new uses for, and 

ways to analyze, the collected data. This raises privacy issues and questions surrounding 

consent to use data in a particular way (e.g., research, commercial product/service 

development). It also offers the potential for more powerful and granular access controls for 

consumers. Accordingly, any policy framework should address the topics of privacy, consent, 

and modern technological capabilities as a part of the policy development process. Policy 

frameworks must be scalable and assure that an individual’s data is properly protected, while 

also allowing the flow of information and responsible evolution of AI. This information is 

necessary to provide and promote high-quality AI applications. Finally, with proper protections in 

place, policy frameworks should also promote data access, including open access to 

appropriate machine-readable public data, development of a culture of securely sharing data 

with external partners, and explicit communication of allowable use with periodic review of 

informed consent. 

8. Collaboration and Interoperability: Policy frameworks should enable eased data access and 

use through creating a culture of cooperation, trust, and openness among policymakers, AI 

technology developers and users, and the public. 

9. Bias: The bias inherent in all data, as well as errors, will remain one of the more pressing 

issues with AI systems that utilize machine learning techniques in particular. Any regulatory 

action should address data provenance and bias issues present in the development and uses of 

AI solutions. Policy frameworks should: 

• Require the identification, disclosure, and mitigation of bias while encouraging access to 

databases and promoting inclusion and diversity. 

• Ensure that data bias does not cause harm to users or consumers. 

10. Education: Policy frameworks should support education for the advancement of AI, promote 

examples that demonstrate the success of AI, and encourage stakeholder engagements to keep 

frameworks responsive to emerging opportunities and challenges. 

• Consumers should be educated as to the use of AI in the service they are using. 

• Academic education should include curriculum that will advance the understanding of 

and ability to use AI solutions.

The App Association represents more than 5,000 small business software application development

companies and technology firms across the mobile economy. Our members develop innovative 

applications and products that meet the demands of the rapid adoption of mobile technology and that

improve workplace productivity, accelerate academic achievement, monitor health, and support the 

global digital economy. Our members play a critical role in developing new products across consumer 

and enterprise use cases, enabling the rise of the internet of things (IoT). Today, the App Association 

represents an ecosystem valued at approximately $1.7 trillion that is responsible for millions of jobs 

around the world.

For more information, please visit www.actonline.org.