GPT-4: While changing the practices of gig companies and AI makers regarding worker rights and ethical practices is challenging, it is not impossible.

Question 1 of 5 for ChatGPT-4: Please read the article "The Gig Is Rigged: How Gig Companies Exploit Private Law to Entrench Power" and tell me what it says in easy to understand language.

ChatGPT-4’s answer:The document "The Gig Is Rigged: How Gig Companies Exploit Private Law to Entrench Power" explores how gig companies exploit various areas of private law to avoid liability and reallocate risks, leading to structural inequalities. It suggests that gig companies rely on tactics that span across employment, corporate, and tort law, among others, to entrench their power. The document advocates for a holistic approach to address these issues through a law-and-political-economy framework. It also uses Amazon Flex and Amazon Delivery Service Partners as examples of gig companies that create risks for workers while evading regulatory oversight.

It discusses the following key points:

• Amazon DSP Program: Describes how Amazon provides extensive support to its Delivery Service Partners (DSPs), but exerts significant control over their operations. DSPs face startup costs, ongoing operation costs, and potential lawsuits due to Amazon's control over routes, deliveries, and other aspects of the business. Amazon benefits from labeling DSPs as independent contractors while exercising significant control.

• Automation and AI: Discusses Amazon's future goals to automate and use AI in its logistics, with a focus on drones and autonomous vehicles. It also highlights the shift towards a gig-based logistics system that externalizes costs and limits liability.

• Defining Gig Economy Harms: Analyzes the risks of harm for gig workers, including loss of professional identity and stability, physical harm, and worker surveillance. It discusses the impact on health, safety, and autonomy due to gig work structures and invasive monitoring by gig companies like Amazon.

• Agency Law: Agency law defines relationships where one party (the agent) acts on behalf of another (the principal). Gig companies may avoid traditional employer-employee relationships while retaining control over workers, suggesting a need for new doctrines like "gig agency" to redefine these relationships.

• Corporate Law: Corporate fragmentation is a strategy gig companies use to limit liability. Amazon, for example, has structured its Delivery Service Partners (DSPs) as separate entities, insulating itself from risk. The document discusses concepts like veil-piercing, corporate family theory, and de facto franchises to hold gig companies accountable for the harms caused by their business models.

• Tort Law: Gig companies shift liability for accidents and other harms to individual workers or small businesses, bypassing traditional tort principles. The document examines enterprise liability, vicarious liability, and other tort concepts to address this issue.

• Regulatory Arbitrage: Gig companies exploit gaps in regulations to avoid compliance, undermining the regulatory state and exacerbating structural inequalities. The document proposes a holistic approach to address these issues, recognizing the broader impact of gig companies' practices.

Question 2 of 5 for ChatGPT-4: List all the practices that gig companies should change when reading this article

ChatGPT-4’s answer:Based on the document "The Gig Is Rigged: How Gig Companies Exploit Private Law to Entrench Power", here are the practices that gig companies should change:

• Employment Classification: Gig companies often classify workers as independent contractors to avoid the costs and obligations of traditional employment, even while retaining significant control. This practice should change to ensure workers receive proper rights and benefits.

• Corporate Fragmentation: Gig companies like Amazon use corporate structures to insulate themselves from liability. Practices like these should be addressed to ensure gig companies can't use corporate fragmentation to avoid responsibility.

• Risk Externalization: Gig companies often externalize risks and costs to workers or small businesses. This practice can leave workers and society bearing the burden of gig economy risks. A more equitable distribution of risks is needed.

• Regulatory Arbitrage: Gig companies design their business models to skirt existing regulations, operating at the edge of legal boundaries. They should embrace compliance and operate within the spirit of the law.

• Platform Immunity: Gig companies often hide behind statutory immunities, like Section 230, to avoid liability. This practice should change to ensure that platform-based companies are held accountable for their actions.

Question 3 of 5 for ChatGPT-4: Are there any analogies with AI companies that may cause the latter to act the same? List all possibilities, whether relating to humans rating, labelling, moderating or 'system prompting' AI and any other possible 'gigs' tied to the activities of AI companies

ChatGPT-4’s answer:Here are analogies between gig companies' practices and potential practices of AI companies that could lead to similar issues:

• Employment Classification: AI companies could classify human moderators, raters, or labelers as independent contractors to avoid the costs associated with traditional employment. This can lead to a lack of benefits, job security, and protection against unfair treatment.

• Corporate Fragmentation: AI companies may fragment their corporate structure to limit liability. This could involve creating separate entities for data collection, AI training, and deployment, making it challenging to hold the main company accountable.

• Risk Externalization: AI companies might externalize risks to human workers by having them perform repetitive or harmful tasks like labeling large data sets, moderating harmful content, or providing feedback under high-pressure conditions. This can result in physical and mental health risks for workers.

• Regulatory Arbitrage: AI companies could design business models to operate at the edge of existing regulations, avoiding compliance by framing their activities differently. This can lead to inadequate oversight and potential harm to workers and users.

• Platform Immunity: Similar to gig companies, AI companies might rely on platform immunity (like Section 230) to avoid responsibility for issues stemming from AI's output or user-generated content. This could limit recourse for those harmed by AI-related activities.

• AI Worker Exploitation: AI companies could use workers for system prompting, annotation, and training in ways that exploit their time and expertise. This can occur when companies offer low wages, poor working conditions, and lack of benefits to individuals performing critical tasks for AI systems.

• Control and Surveillance: AI companies may use technology to monitor and control human workers who interact with AI systems, reducing worker autonomy and leading to privacy concerns. This mirrors gig companies' use of technology to control independent contractors.

These analogies highlight potential issues AI companies could face if they adopt practices similar to gig companies. It underscores the need for comprehensive regulations and ethical considerations to ensure AI companies don't exploit workers or avoid accountability.

Question 4 of 5 for ChatGPT-4: What would your recommendations be for AI makers and regulators and any other stakeholder that you can think of as potentially being relevant when taking the issues flagged in this article into account?

ChatGPT-4’s answer:To address the issues flagged in "The Gig Is Rigged: How Gig Companies Exploit Private Law to Entrench Power," here are some recommendations for AI makers, regulators, and other stakeholders:

Recommendations for AI Makers:

• Fair Employment Practices: AI companies should classify workers, such as moderators, labelers, and raters, as employees if they meet the criteria, ensuring they receive benefits, job security, and protections under labor laws.

• Worker Safety and Well-Being: AI companies must ensure that workers in roles like data labeling, content moderation, and system prompting have safe working conditions, including proper breaks, mental health support, and appropriate compensation.

• Transparency and Accountability: AI makers should be transparent about their corporate structure and ensure clear accountability for AI-related activities. This includes disclosing how they use subcontractors and ensuring these entities comply with employment and safety regulations.

• Ethical Use of AI: AI companies should develop and follow ethical guidelines for AI use, particularly in tasks that involve human workers. This includes avoiding exploitative practices and ensuring AI systems do not infringe on workers' rights or create undue risks.

Recommendations for Regulators:

• Strengthen Labor Laws: Regulators should ensure that labor laws protect all workers, including those in the gig and AI economy, by refining the definitions of employees and independent contractors to prevent misclassification.

• Enhanced Corporate Oversight: Regulatory bodies should monitor corporate fragmentation to prevent companies from evading liability through complex structures. This could involve stricter rules for corporate family liability and veil-piercing.

• Regulate AI Workplaces: Regulators should implement specific guidelines for AI companies to ensure safe and ethical workplaces, focusing on worker protection, safety, and fair compensation.

• Review Platform Immunity: Regulators should review platform immunity laws to ensure AI companies cannot use them to evade liability for harms caused by AI systems or user-generated content.

• Address Regulatory Arbitrage: Regulators should be proactive in closing loopholes that allow AI companies to avoid compliance, ensuring they operate within the spirit of the law.

Recommendations for Other Stakeholders:

• Workers' Rights Groups: These groups should advocate for AI workers' rights, emphasizing fair treatment, safety, and benefits. They can also push for unionization and collective bargaining to give workers more power.

• Technology Ethics Experts: These experts should contribute to developing ethical guidelines for AI companies, ensuring that AI practices do not harm workers or society.

• Industry Associations: Industry associations should establish best practices for AI companies, promoting ethical behavior and accountability.

• Public Awareness Campaigns: These campaigns can raise awareness about potential risks associated with AI work and encourage companies to adopt fair and ethical practices.

Implementing these recommendations can help address issues raised by gig company practices and ensure AI companies and regulators prioritize workers' rights, safety, and corporate accountability.

Question 5 of 5 for ChatGPT-4: How realistic is it that gig companies and AI makers will change their ways? Especially taking into account the fact that these practices might have been going on for ages and that AI services are exceptionally expensive already

ChatGPT-4’s answer:Changing the behavior of gig companies and AI makers, especially given entrenched practices and the high costs of AI services, presents significant challenges. Here are some factors to consider in assessing the realism of these changes:

Factors Supporting Change:

• Public Pressure and Social Movements: Growing awareness and social movements around labor rights, gig work, and AI ethics can create pressure for companies to change. Public campaigns, employee strikes, and advocacy can drive change.

• Regulatory Pressure: Regulatory bodies and governments can play a crucial role in enforcing labor laws, introducing new regulations, and closing loopholes. With effective enforcement, companies may have no choice but to comply.

• Legal Risks: Gig companies and AI makers may face legal risks from lawsuits, class actions, or regulatory fines if they continue to engage in questionable practices. The threat of legal consequences can prompt change.

• Corporate Social Responsibility: As companies become more focused on corporate social responsibility (CSR), they may realize that treating workers fairly and operating ethically is better for their reputation and long-term success.

• Stakeholder Expectations: Investors and stakeholders increasingly value ethical practices. Companies with questionable labor practices may face investor backlash or a drop in public trust, motivating them to change.

Factors Resisting Change:

• Cost and Profitability: Gig companies and AI makers might resist change due to the potential increase in costs associated with compliance, worker benefits, and fair employment practices. This can impact their profitability and competitive edge.

• Complex Corporate Structures: Corporate fragmentation and complex legal structures make it easier for companies to evade accountability. Changing these structures may require significant effort and legal restructuring.

• Industry Norms: In industries where exploitative practices are common, companies may see little incentive to change unless there is a strong push from regulators or public pressure.

• Technology Dependencies: AI companies rely heavily on human labor for data labeling, content moderation, and other tasks. A shift towards more ethical practices may require substantial changes in their business models and processes.

Conclusion:

While changing the practices of gig companies and AI makers is challenging, it is not impossible. A combination of public pressure, regulatory enforcement, legal risks, CSR, and stakeholder expectations can drive change. However, the process may be gradual and require ongoing advocacy, legislation, and oversight to ensure that companies adapt and prioritize worker rights and ethical practices. Realistic change will require a multi-faceted approach that combines social, legal, and economic incentives to shift industry norms.