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Creating Trustworthy AI: A Mozilla White Paper

This white paper unpacks Mozilla’s theory of change for supporting the development of more trustworthy artificial intelligence (AI).

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AI White Paper
Mozilla Foundation
v1.0
Mozilla Confidential
Agenda
2
Challenges
Challenges accelerated or
deepened by AI.
Introduction
Overview
What are the goals of the
white paper?
Questions
Timeline
Timeline for reviewing and
publishing the paper.
Pathway Forward
Unpacking Mozilla’s AI
Theory of Change.
3.
2.
1.
6.
5.
4.
Mozilla Confidential
Shifting
industry
norms
Building new
tech and
products
Generating
demand
1
2
3
Creating
regulations
and incentives
4
Agency
All AI is designed with
personal agency in mind.
Privacy, transparency, and
human well-being are key
considerations.
Accountability
Companies are held to account when
their AI systems make discriminatory
decisions, abuse data, or make people
unsafe.
A
B
3
Overview Introduction Challenges Pathway Forward Timeline Questions
AI Theory of Change
Overview
Mozilla Confidential
Why a white
paper?
4
■ Charts the provenance of our ideas and
thinking around AI.
■ Defines Mozilla’s distinct approach to
trustworthy AI.
■ Unpacks Mozilla’s AI theory of change, a
detailed map for our work.
■ Helps us invite others to collaborate and build
off our work. (Partners see Mozilla as a strong
and trusted partner in the AI space).
Overview Introduction Challenges Pathway Forward Timeline Questions
Mozilla Confidential 5
Introduction
Mozilla Confidential
Why Mozilla?
6
■ Mozilla has a rich history of reimagining
computing norms to favor openness and
innovation.
■ Mozilla has historically been a convener of
disparate groups that point towards a
common goal.
■ We’re at an inflection point in the
development of AI that’s not so different
from the early web.
■ Many of the challenges posed by AI are not
new. AI adds a new layer of complexity to key
issues Mozilla has already been working on.
Overview Introduction Challenges Pathway Forward Timeline Questions

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Creating Trustworthy AI: A Mozilla White Paper

  • 1. AI White Paper Mozilla Foundation v1.0
  • 2. Mozilla Confidential Agenda 2 Challenges Challenges accelerated or deepened by AI. Introduction Overview What are the goals of the white paper? Questions Timeline Timeline for reviewing and publishing the paper. Pathway Forward Unpacking Mozilla’s AI Theory of Change. 3. 2. 1. 6. 5. 4.
  • 3. Mozilla Confidential Shifting industry norms Building new tech and products Generating demand 1 2 3 Creating regulations and incentives 4 Agency All AI is designed with personal agency in mind. Privacy, transparency, and human well-being are key considerations. Accountability Companies are held to account when their AI systems make discriminatory decisions, abuse data, or make people unsafe. A B 3 Overview Introduction Challenges Pathway Forward Timeline Questions AI Theory of Change Overview
  • 4. Mozilla Confidential Why a white paper? 4 ■ Charts the provenance of our ideas and thinking around AI. ■ Defines Mozilla’s distinct approach to trustworthy AI. ■ Unpacks Mozilla’s AI theory of change, a detailed map for our work. ■ Helps us invite others to collaborate and build off our work. (Partners see Mozilla as a strong and trusted partner in the AI space). Overview Introduction Challenges Pathway Forward Timeline Questions
  • 6. Mozilla Confidential Why Mozilla? 6 ■ Mozilla has a rich history of reimagining computing norms to favor openness and innovation. ■ Mozilla has historically been a convener of disparate groups that point towards a common goal. ■ We’re at an inflection point in the development of AI that’s not so different from the early web. ■ Many of the challenges posed by AI are not new. AI adds a new layer of complexity to key issues Mozilla has already been working on. Overview Introduction Challenges Pathway Forward Timeline Questions
  • 7. Mozilla Confidential Definitions 7 ■ AI: The term AI is vague, but has largely come to represent a broad assemblage of technologies and techniques. ■ Trustworthy AI: AI that is demonstrably worthy of trust. Privacy, transparency and human well-being are key considerations and there are mechanisms for accountability. ■ Consumer technology: Products and services used by or purchased by the broad public. Note: B2B tech or tech used by governments/law enforcement would fall outside of this scope. Overview Introduction Challenges Pathway Forward Timeline Questions
  • 8. Mozilla Confidential ● Industry: Current incentives in the tech industry have resulted in business models that rely on unfettered access to data. The industry is dominated by a handful of tech giants who wield immense market and political power. ● Regulators: AI development has largely outpaced regulations, resulting in an environment where ideas are tested and technologies are deployed to millions of people without proper oversight or transparency. ● Consumers: People feel increasingly powerless. Consumers do not have the information they need to make educated choices about which products to purchase or which platforms to use. The current state 8 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 10. Mozilla Confidential Challenges posed by AI 10 1. Monopoly and centralization 2. Data governance and privacy 3. Bias and discrimination 4. Accountability and transparency 5. Industry norms 6. Exploitation of workers and the environment 7. Safety and security Overview Introduction Challenges Pathway Forward Timeline Questions
  • 11. Mozilla Confidential Companies have a tendency to stockpile data in order to maintain their competitive advantage. Once AI enters the equation, though, it creates an endless cycle: Those companies who dominate the market have greater access to data, which allows them to develop better machine learning models, which enables them to collect even more data. 1. Monopoly and centralization 11 Only a handful of tech giants have the resources to build AI, stifling innovation and competition. For “platform monopolies” like Facebook and Google that amass huge troves of data about how people behave online, the competitive advantage is even more pronounced. Rapid consolidation of the AI space is likely to continue, as the most dominant tech companies acquire their AI competitors and the data that comes with them. Overview Introduction Challenges Pathway Forward Timeline Questions
  • 12. Mozilla Confidential Privacy concerns intensify with the development of AI. Vast amounts of training data (images, text, video, or audio) are required to teach machine learning models how to recognize patterns and predict behavior. Machine learning incentivizes companies to collect user data without obtaining meaningful consent and without sufficient privacy considerations. 2. Data governance and privacy 12 Because AI requires access to large amounts of training data, companies and researchers are incentivized to develop invasive techniques for collecting, storing, and sharing data without obtaining meaningful consent. As AI continues to drive up the value of consumer data, information asymmetry will continue to increase between users and the companies collecting their data.1 Overview Introduction Challenges Pathway Forward Timeline Questions 1 Ginger Zhe Jin, “Artificial Intelligence and Consumer Privacy,” Working Paper (National Bureau of Economic Research, January 2018), https://doi.org/10.3386/w24253.
  • 13. Mozilla Confidential Every dataset comes with its own set of biases, and it is impossible to build a fully unbiased AI system. Often the bias exhibited in an AI system is the result of incomplete or biased training data. Sometimes the bias in an AI system occurs when the algorithm unintentionally latches onto the wrong things in the dataset to make predictions. 3. Bias and discrimination 13 AI relies on computational models, data, and frameworks that reflect existing bias, often resulting in biased or discriminatory outcomes. Even when steps have been taken to reduce bias in a model, that system can still make decisions that have a discriminatory effect. Computer scientists are rallying around values like “fairness, accountability, and transparency” but this perspective often lacks a justice or equity perspective. Overview Introduction Challenges Pathway Forward Timeline Questions
  • 14. Mozilla Confidential Many platforms develop closed algorithms that rapidly generate, curate, and recommend content. Platforms are now in a position where they are making decisions that will shape society — and there isn’t adequate oversight. So-called “black box” algorithms defy mechanisms for explainability and accountability, which is complicated by the fact that many corporate algorithms remain trade secrets. 4. Accountability and transparency 14 Companies often don’t provide transparency into how theirAI systems work, impairing legal and technical mechanisms for corporate accountability. Experts have spent years trying to boost the overall interpretability and explainability of AI — whether a machine learning system can be understood by and explained to a human. Different methods of building AI inherently have different levels of explainability. And methods for explainability depend on what kind of transparency is desired.1 Overview Introduction Challenges Pathway Forward Timeline Questions 1 Andrew D. Selbst and Solon Barocas, “The Intuitive Appeal of Explainable Machines,” SSRN Scholarly Paper (Rochester, NY: Social Science Research Network, March 2, 2018), https://doi.org/10.2139/ssrn.3126971.
  • 15. Mozilla Confidential Market pressures — paired with weak legal limits — has contributed to a culture in which new products are not subjected to critical examination, sufficient testing, or oversight. AI is built with a set of assumptions that have gone unchallenged, and companies may optimize for a narrow set of values, such as profitability, engagement, and growth. 5. Industry norms 15 Companies are pressured to build and deployAI rapidlywithout pausing to ask critical questions about the human and societal impacts. As a result, AI systems are embedded with values and assumptions that are not questioned in the development lifecycle. A real crisis of diversity (professional, cultural, ethnic, gender, socioeconomic, and geographic) contributes to this problem. Many engineers, product managers, designers, and investors consider responsibility for AI to be outside the scope of their job. Overview Introduction Challenges Pathway Forward Timeline Questions
  • 16. Mozilla Confidential AI development has spurred companies to collect increasingly large amounts of training data, resulting in unprecedented levels of energy consumption and expanding the need for data centers, which require space and enormous amounts of cooling resources. There is little to no information about how much energy big tech’s algorithms consume, but data suggest the ad tech industry is the biggest pollutant in this area. 6. Exploitation of workers and the environment 16 The workers who perform the invisible work of maintaining AI systems are particularly vulnerable. And, the climate crisis is being accelerated byAI, which intensifies energy consumption and speeds up the extraction of natural resources. Companies building AI-powered services rely on a vast, invisible network of on-demand workers to clean and label datasets, and to train and improve models. There are few employment laws globally that reflect the realities of the gig economy. This labor is often precarious and temporary, with few benefits or support. Overview Introduction Challenges Pathway Forward Timeline Questions
  • 17. Mozilla Confidential Algorithmic curation is increasingly playing a role in information warfare as computational propaganda has become more sophisticated and subtle. AI can be used to surface targeted propaganda, misinformation, and other kinds of political manipulation. Algorithmic curation creates opportunities for a range of actors to exploit or “game” those systems for political and/or financial gain. 7. Safety and security 17 Malicious actors may be able to carry out increasingly sophisticated attacks by exploiting the vulnerabilities of intelligent systems. AI can also be used to automate labor-intensive cyberattacks like spear phishing, carry out new types of attacks like voice impersonation, and exploit AI’s vulnerabilities with adversarial machine learning.1 Overview Introduction Challenges Pathway Forward Timeline Questions 1 Miles Brundage et al., “The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation,” ArXiv:1802.07228 [Cs], February 20, 2018, http://arxiv.org/abs/1802.07228.
  • 19. Mozilla Confidential AI Theory of Change Shifting industry norms Building new tech and products Generating demand 1 2 3 Creating regulations and incentives 4 Agency All AI is designed with personal agency in mind. Privacy, transparency, and human well-being are key considerations. Accountability Companies are held to account when their AI systems make discriminatory decisions, abuse data, or make people unsafe. A B Overview Introduction Challenges Pathway Forward Timeline Questions 19
  • 20. Mozilla Confidential 1. Shifting industry norms: The people building AI increasingly use trustworthy AI guidelines and technologies in their work. 2. Building new tech and products: Trustworthy AI products and services are increasingly embraced by early adopters. 3. Generating demand: Consumers choose trustworthy products when available and demand them when they aren’t. 4. Creating regulations and incentives: New and existing laws are used to make the AI ecosystem more trustworthy. 20 AI Theory of Change Overview Introduction Challenges Pathway Forward Timeline Questions
  • 21. Mozilla Confidential AI Theory of Change SHIFTING INDUSTRY NORMS Best practices emerge in key areas of trustworthy AI, driving changes to industry norms. Engineers, product managers, and designers with trustworthy AI training and experience are in high demand across industry. Diverse stakeholders — including communities and people historically shut out of tech — are involved in the design of AI. There is increased investment in and procurement of trustworthy AI products, services and technologies. BUILDING NEW TECH & PRODUCTS More foundational trustworthy AI technologies emerge as building blocks for developers. Transparency is included as a feature in more AI enabled products, services, and technologies. Entrepreneurs develop — and investors support — alternative business models for consumer tech. The work of artists and journalists helps people understand, imagine, and critique what trustworthy AI looks like. GENERATING DEMAND Trustworthy AI products and services emerge that serve the needs of people and markets previously ignored. Consumers are increasingly willing and able to choose products critically based on information regarding AI trustworthiness. Citizens are increasingly willing and able to pressure and hold companies accountable for the trustworthiness of their AI. A growing number of civil society actors are promoting trustworthy AI as a key part of their work. CREATING REGULATIONS & INCENTIVES Governments develop the vision, skills, and capacities needed to effectively regulate AI, relying on both new and existing laws. Progress towards trustworthy AI is made through wider enforcement of existing rules like the GDPR. Regulators have access to the data and expertise they need to scrutinize the trustworthiness of AI in consumer products and services. Governments develop programs to invest in and incent trustworthy AI. 21 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 22. Mozilla Confidential AI Theory of Change 22 Overview Introduction Challenges Pathway Forward Timeline Questions SHIFTING INDUSTRY NORMS Best practices emerge in key areas of trustworthy AI, driving changes to industry norms. Engineers, product managers, and designers with trustworthy AI training and experience are in high demand across industry. Diverse stakeholders — including communities and people historically shut out of tech — are involved in the design of AI. There is increased investment in and procurement of trustworthy AI products, services and technologies. BUILDING NEW TECH & PRODUCTS More foundational trustworthy AI technologies emerge as building blocks for developers. Transparency is included as a feature in more AI enabled products, services, and technologies. Entrepreneurs develop — and investors support — alternative business models for consumer tech. The work of artists and journalists helps people understand, imagine, and critique what trustworthy AI looks like. GENERATING DEMAND Trustworthy AI products and services emerge that serve the needs of people and markets previously ignored. Consumers are increasingly willing and able to choose products critically based on information regarding AI trustworthiness. Citizens are increasingly willing and able to pressure and hold companies accountable for the trustworthiness of their AI. A growing number of civil society actors are promoting trustworthy AI as a key part of their work. CREATING REGULATIONS & INCENTIVES Governments develop the vision, skills, and capacities needed to effectively regulate AI, relying on both new and existing laws. Progress towards trustworthy AI is made through wider enforcement of existing rules like the GDPR. Regulators have access to the data and expertise they need to scrutinize the trustworthiness of AI in consumer products and services. Governments develop programs to invest in and incent trustworthy AI.
  • 23. Mozilla Confidential ● Dozens of guidelines for “ethical AI” have been published in recent years. ○ Prominent examples: EU’s High-Level Expert Group, the Partnership on AI, the Organization for Economic Co-operation and Development (OECD), Google, SAP, the Association of Computing Machinery (ACM), Access Now ● Frameworks agree on several core principles. ○ The most common principles included were transparency (86.9% of frameworks), justice and fairness (81.0%), a duty not to commit harm (71.4%), responsibility (71.4%), privacy (56.0%), and human well-being (48.8%).1 ● But there are major differences across sectors about what they mean and how they should be implemented. ○ In their definitions of transparency, nonprofits and governments refer to audits and oversight, whereas industry refers to technical solutions to transparency, like explainability. 1.1 Best practices emerge in key areas of trustworthy AI, driving changes to industry norms. 23 1 Anna Jobin, Marcello Ienca and Effy Vayena, “The global landscape of AI ethics guidelines,” Nature Machine Intelligence, vol. 1, no. 9, Sept. 2019, pp. 389–99, https://www.nature.com/articles/s42256-019-0088-2 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 24. Mozilla Confidential ● Engineers and other AI domain experts wield a great degree of decision-making power in development and deployment of AI systems. ● By supporting education and training in building tech responsibly, we aim to put pressure on companies seeking to attract top engineering talent. ○ The traditional approach to tech ethics education in CS is far removed from the day-to-day experience of engineers. A skills-based, situated pedagogy gets students one step closer to operationalizing trustworthy AI principles in the workplace. ● Crucially, research suggests that the actions of internal advocates won’t have impact unless their work is aligned with organizational practices.1 1.2 Engineers, product managers, and designers with trustworthy AI training and experience are in high demand across industry. 24 1 Michael Madaio et al., “Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI,” March 19, 2020, https://www.microsoft.com/en-us/research/publication/co-designing-checklists-to-understand-organizational-challenges-and-opportunities-around-f airness-in-ai/. Overview Introduction Challenges Pathway Forward Timeline Questions
  • 25. Mozilla Confidential ● The diversity crisis in AI has a direct link to problems with bias in AI. ● The teams building AI should strive to reflect the diversity of the people who use the technology, representing a range of identities, communities, and perspectives. ● Diverse communities should be consulted throughout the AI design and development process. ● Companies must foster an open, transparent culture in which the status quo can be questioned or challenged without fears of retaliation. ○ In its analysis of the diversity crisis in AI, AI Now concluded that a worker-driven movement aimed at addressing inequities holds the most promise for pushing for real change in diversity.1 1.3 Diverse stakeholders — including communities and people historically shut out of tech — are involved in the design of AI. 25 1 Sarah Myers West, Meredith Whittaker, and Kate Crawford, “Discriminating Systems: Gender, Race, and Power in AI,” AI Now Institute, https://ainowinstitute.org/discriminatingsystems.pdf. Overview Introduction Challenges Pathway Forward Timeline Questions
  • 26. Mozilla Confidential ● Although there has been a rise in “impact investments” in socially responsible companies and startups, there is still a lot of work that needs to be done to ensure trustworthy AI products are getting the funding they need to become viable. ● Tech investors are paying more attention to privacy. ● Tech companies are paying attention to privacy in their acquisition strategy. ● There is a clear opportunity now for such “impact investors” who care about building tech responsibly to shape the AI product landscape. 1.4 There is increased investment in and procurement of trustworthy AI products, services and technologies. 26 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 27. Mozilla Confidential AI Theory of Change 27 Overview Introduction Challenges Pathway Forward Timeline Questions SHIFTING INDUSTRY NORMS Best practices emerge in key areas of trustworthy AI, driving changes to industry norms. Engineers, product managers, and designers with trustworthy AI training and experience are in high demand across industry. Diverse stakeholders — including communities and people historically shut out of tech — are involved in the design of AI. There is increased investment in and procurement of trustworthy AI products, services and technologies. BUILDING NEW TECH & PRODUCTS More foundational trustworthy AI technologies emerge as building blocks for developers. Transparency is included as a feature in more AI enabled products, services, and technologies. Entrepreneurs develop — and investors support — alternative business models for consumer tech. The work of artists and journalists helps people understand, imagine, and critique what trustworthy AI looks like. GENERATING DEMAND Trustworthy AI products and services emerge that serve the needs of people and markets previously ignored. Consumers are increasingly willing and able to choose products critically based on information regarding AI trustworthiness. Citizens are increasingly willing and able to pressure and hold companies accountable for the trustworthiness of their AI. A growing number of civil society actors are promoting trustworthy AI as a key part of their work. CREATING REGULATIONS & INCENTIVES Governments develop the vision, skills, and capacities needed to effectively regulate AI, relying on both new and existing laws. Progress towards trustworthy AI is made through wider enforcement of existing rules like the GDPR. Regulators have access to the data and expertise they need to scrutinize the trustworthiness of AI in consumer products and services. Governments develop programs to invest in and incent trustworthy AI.
  • 28. Mozilla Confidential ● A first major step towards better products and services is developing technological building blocks that can power more responsible AI. These building blocks could include alternative data governance models, privacy-preserving methods for machine learning, and decentralized, open source datasets. ● Innovations in privacy-preserving AI include: ○ Edge computing / decentralized computing ○ Federated learning ○ Differential privacy ○ Homomorphic encryption ● Legal innovations in data governance include: ○ Information fiduciaries ○ Data trusts ○ Data co-ops ● And: We need trustworthy pre-trained models & datasets. 2.1 More foundational trustworthy AI technologies emerge as building blocks for developers. 28 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 29. Mozilla Confidential ● Tech infrastructure: ○ Explainability: The methods used to explain a particular system depend on what kind of algorithm or ML technique is being used. ○ Auditability: While developers should be regularly auditing their AI systems, they can also build those systems in a way that makes them easier to audit by third parties. ○ Human-in-the-loop: Human in the loop means that humans are directly involved in training, tuning, and verifying the data used in an ML system. ● Product design: ○ User control: Platforms and services can be designed in a way that gives users greater control and agency over the algorithm’s inputs/outputs. ○ Archives/Libraries: Platforms develop transparency products and offerings. This is part of a broader bulk disclosure demand. 2.2 Transparency is included as a feature in more AI enabled products, services, and technologies. 29 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 30. Mozilla Confidential ● Companies that demonstrate they care about people’s privacy and well-being increasingly have a market advantage. ● There is a hunger in the market for different business models that aren’t focused on aggressively monetizing people’s data. ● Examples of alternative business models: ○ Set up the platform so that people pay to use it. ○ For two-sided businesses, opt to use privacy-preserving methods of doing data analysis. Offers a new way to identify patterns without exploiting people’s data. 2.3 Entrepreneurs develop — and investors support — alternative business models for consumer tech. 30 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 31. Mozilla Confidential ● Journalists can serve as corporate watchdogs by investigating computational systems, and they can also help us understand what is happening by providing context and evidence. ● Artists are exposing the limitations and shortcomings of AI. ○ Artists critique current systems and imagine different ones by providing us a new lens through which we can see our world. ○ Art is also a speculative tool that helps us see what alternative worlds and technologies could look like. 2.4 The work of artists and journalists helps people understand, imagine, and critique what trustworthy AI looks like. 31 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 32. Mozilla Confidential AI Theory of Change 32 Overview Introduction Challenges Pathway Forward Timeline Questions SHIFTING INDUSTRY NORMS Best practices emerge in key areas of trustworthy AI, driving changes to industry norms. Engineers, product managers, and designers with trustworthy AI training and experience are in high demand across industry. Diverse stakeholders — including communities and people historically shut out of tech — are involved in the design of AI. There is increased investment in and procurement of trustworthy AI products, services and technologies. BUILDING NEW TECH & PRODUCTS More foundational trustworthy AI technologies emerge as building blocks for developers. Transparency is included as a feature in more AI enabled products, services, and technologies. Entrepreneurs develop — and investors support — alternative business models for consumer tech. The work of artists and journalists helps people understand, imagine, and critique what trustworthy AI looks like. GENERATING DEMAND Trustworthy AI products and services emerge that serve the needs of people and markets previously ignored. Consumers are increasingly willing and able to choose products critically based on information regarding AI trustworthiness. Citizens are increasingly willing and able to pressure and hold companies accountable for the trustworthiness of their AI. A growing number of civil society actors are promoting trustworthy AI as a key part of their work. CREATING REGULATIONS & INCENTIVES Governments develop the vision, skills, and capacities needed to effectively regulate AI, relying on both new and existing laws. Progress towards trustworthy AI is made through wider enforcement of existing rules like the GDPR. Regulators have access to the data and expertise they need to scrutinize the trustworthiness of AI in consumer products and services. Governments develop programs to invest in and incent trustworthy AI.
  • 33. Mozilla Confidential ● A new market of privacy-forward consumers ○ A new wave of startups whose core focus is bringing technologies like federated learning into consumer products. ○ Hints that established big tech players want to tap into the market for privacy. ● People who speak non-dominant languages or who use non-Latin characters have historically been left out of products. ○ Open source initiatives aimed at inclusion and privacy, e.g. Mozilla’s Common Voice 3.1 Trustworthy AI products and services emerge that serve the needs of people and markets previously ignored. 33 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 34. Mozilla Confidential ● At the moment, consumers don’t feel they can make educated choices about what products to buy or platforms to use. ● As more products using trustworthy AI reach the market, consumers will need better information about who and what to trust. ○ Mozilla’s Privacy Not Included ○ Consumer Reports’ Digital Standard ○ Data Nutrition Project 3.2 Consumers are increasingly willing and able to choose products critically based on information regarding AI trustworthiness. 34 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 35. Mozilla Confidential ● As we wait for clear consumer protection regulations or a mature market for trustworthy AI products and services to emerge, consumers will need to pressure companies directly. ● Direct consumer campaigns with precise asks for product changes and transparency is one way to pressure companies to change their practices. 3.3 Citizens are increasingly willing and able to pressure and hold companies accountable for the trustworthiness of their AI. 35 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 36. Mozilla Confidential ● Over the last 25 years, a number of public interest organizations have emerged to promote digital rights and a healthy internet. ● A new crop of AI-focused public interest organizations has also emerged. ● Established, non-tech organizations are getting involved: ○ Increased focus on privacy, data, and AI in traditional consumer rights groups. ○ Increased interest by civil and human rights organizations in the ways in which AI will impact the communities they serve. ● Building alliances between digital rights groups and groups from other public interest sectors is likely the most effective way to meet this need. 3.4 A growing number of civil society actors are promoting trustworthy AI as a key part of their work. 36 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 37. Mozilla Confidential AI Theory of Change 37 Overview Introduction Challenges Pathway Forward Timeline Questions SHIFTING INDUSTRY NORMS Best practices emerge in key areas of trustworthy AI, driving changes to industry norms. Engineers, product managers, and designers with trustworthy AI training and experience are in high demand across industry. Diverse stakeholders — including communities and people historically shut out of tech — are involved in the design of AI. There is increased investment in and procurement of trustworthy AI products, services and technologies. BUILDING NEW TECH & PRODUCTS More foundational trustworthy AI technologies emerge as building blocks for developers. Transparency is included as a feature in more AI enabled products, services, and technologies. Entrepreneurs develop — and investors support — alternative business models for consumer tech. The work of artists and journalists helps people understand, imagine, and critique what trustworthy AI looks like. GENERATING DEMAND Trustworthy AI products and services emerge that serve the needs of people and markets previously ignored. Consumers are increasingly willing and able to choose products critically based on information regarding AI trustworthiness. Citizens are increasingly willing and able to pressure and hold companies accountable for the trustworthiness of their AI. A growing number of civil society actors are promoting trustworthy AI as a key part of their work. CREATING REGULATIONS & INCENTIVES Governments develop the vision, skills, and capacities needed to effectively regulate AI, relying on both new and existing laws. Progress towards trustworthy AI is made through wider enforcement of existing rules like the GDPR. Regulators have access to the data and expertise they need to scrutinize the trustworthiness of AI in consumer products and services. Governments develop programs to invest in and incent trustworthy AI.
  • 38. Mozilla Confidential ● There’s evidence that policymakers are listening to technologists from civil society. But nonprofits don’t always have the technical capacity and they are often up against tech lobbyists and experts representing the interests of big tech companies. ● Policymakers are strengthening their capacity by working with more technologists. ○ Emerging field of “public interest tech” has enabled technologists to influence tech policy. ● Some governments are developing AI-specific centers of expertise. ● Areas to invest: ○ Expanding cross-disciplinary university programs that combine public policy and tech, and growing the number of research institutions with a focus on AI ○ Creating centers of tech expertise that can be used across departments 4.1 Governments develop the vision, skills, and capacities needed to effectively regulate AI, relying on both new and existing laws. 38 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 39. Mozilla Confidential ● Governments are working together to develop global governance frameworks for AI. ○ In 2019, 42 countries took a critical step when they came together to endorse a global governance framework on AI, the OECD AI Principles.1 The G20 adopted a set of global AI Principles, largely based on the OECD framework. ● At the same time, countries are putting together their own governance frameworks. ○ European Commission’s 2020 White Paper ○ UK Lords Select Committee’s 2017 AI guidelines ○ China’s 2019 Governance Principles for Responsible AI ○ Singapore’s 2020 Model AI Governance Framework ● The EU’s vision is the most mature. But there’s a gap: The EU has yet to consider the use of AI in consumer technologies as “high risk”, despite the fact that such technologies pose major collective risks. 39 Overview Introduction Challenges Pathway Forward Timeline Questions 4.1 Governments develop the vision, skills, and capacities needed to effectively regulate AI, relying on both new and existing laws.
  • 40. Mozilla Confidential ● Existing laws and regulations that protect data rights can be wielded in a meaningful way to address many of the challenges outlined in this paper. ● Existing privacy laws like the GDPR ○ The GDPR has been used to pressure companies into taking data security seriously and to tackle the surveillance economy and rampant data collection that powers AI. ○ But there are parts of the GDPR that apply to AI that have not yet been tested ■ Article 22: “Automated individual decision-making, including profiling” - mandates that AI cannot be used to make decisions that have significant impact AND affirms a ‘right to explanation’ ■ Article 5: “Principles Relating to Processing of Personal Data” - requires that data processing is fair AND affirms the principle of data minimization ■ Article 35: “Data Protection Impact Assessment” - requires data protection impact assessments. 4.2 Progress towards trustworthy AI is made through wider enforcement of existing laws like the GDPR. 40 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 41. Mozilla Confidential ● Antitrust law ○ Antitrust laws could be applied to break up monopolies in the tech industry, which would help spur competition and innovation in AI. ○ In the EU, authorities have not shied from imposing fines on big tech companies based on competition law. ○ In the U.S., a renewed interest in antitrust laws among legal scholars and regulators has presented an opportunity to strengthen competition policy. 4.2 Progress towards trustworthy AI is made through wider enforcement of existing laws like the GDPR. 41 Overview Introduction Challenges Pathway Forward Timeline Questions
  • 42. Mozilla Confidential ● Full transparency has limitations: it often ignores systems of power, obscures itself further by overwhelming people, and can promote a false sense of knowledge.1 ● Transparency in this context could mean many things: ○ Source code / open source ○ Training data documentation ■ A comprehensive list of all the datasets used, an assessment of the quality of the datasets, an explanation of how the datasets were manipulated, any records of possible sources of bias, and a plan for how to account or correct for that bias. ○ AI documentation ■ The model’s training methods, processes and techniques used to test and validate the AI, what values the model is optimizing for, weights for each parameter at the outset, etc. Should also include normative explanations for why a particular method was chosen. ○ Data archives/APIs (see 2.2) 4.3 Regulators have access to the data and expertise they need to scrutinize the trustworthiness of AI in consumer products and services. 42 1 Mike Ananny and Kate Crawford, “Seeing without Knowing: Limitations of the Transparency Ideal and Its Application to Algorithmic Accountability,” New Media, Dec 13, 2016, https://doi.org/10.1177/1461444816676645. Overview Introduction Challenges Pathway Forward Timeline Questions
  • 43. Mozilla Confidential ● Governments are developing industrial policy that matches their policy goals and vision for AI. ● Governments are developing a procurement strategy that matches their strategic vision for AI. ○ Cities Coalition for Digital Rights ○ UK’s “Guide to using AI in the Public Sector” ● Government agencies adopt procurement guidelines directly into the terms and conditions of vendor contracts. 4.4 Governments develop programs to invest in and incent trustworthy AI. 43 Overview Introduction Challenges Pathway Forward Timeline Questions