Beyond Consent: Why We Need Corporate Accountability for Data Privacy

Daniel Solove argues that giving individuals control over their personal data is an ineffective strategy for regulating privacy in the AI era. According to a piece published in the Wall Street Journal, Solove proposes shifting the regulatory focus toward corporate accountability, mirroring the oversight models used for food and drug companies.

The shift toward AI-driven data processing has changed how personal information is leveraged, making individual consent and control mechanisms less practical. Solove suggests that instead of placing the burden on the user to manage their data, the legal framework should hold companies responsible for the outcomes of their technological designs.

This approach moves away from the “notice and choice” model, where users agree to long terms of service, and toward a system of professional and legal liability for the entities collecting and processing the information.

Proposed Accountability Measures for Data Controllers

Solove outlines several specific mechanisms to replace individual data control with corporate obligations. According to the Wall Street Journal, these include the implementation of rigorous data minimization, which limits the amount of data a company can collect to only what is strictly necessary for a specific purpose.

The proposed framework also suggests the following legal and operational requirements:

• Fiduciary Duties: Requiring companies to act in the best interest of the data subject.
• Design Liability: Establishing legal liability for negligent or reckless technological design.
• Algorithmic Liability: Holding companies accountable for algorithms that cause tangible harm to users.
• Multi-Stakeholder Review: Implementing review processes for new technologies that involve various stakeholders before deployment.

Contrast Between Individual Control and Systemic Regulation

The current privacy paradigm often relies on the assumption that users can protect themselves by opting out of data collection or managing privacy settings. Solove contends that this is no longer a viable defense against the scale and complexity of AI systems.

By comparing data regulation to the food and drug industry, Solove highlights a shift from consumer caution to producer responsibility. In the pharmaceutical industry, the government does not rely on the patient to verify the safety of a drug; instead, the manufacturer is legally required to prove safety and is held liable for defects. Solove argues for a similar standard in the tech industry regarding data handling and algorithmic impact.

Impact of AI on Privacy Regulation

The rise of AI complicates traditional privacy laws because these systems can often infer sensitive information about a person from non-sensitive data points. This capability renders traditional “control” mechanisms obsolete, as a user cannot “opt out” of an inference made by an algorithm based on public or aggregated data.

Because AI can generate new insights and potentially harmful outcomes from existing datasets, Solove’s argument emphasizes that the harm occurs not at the point of collection, but at the point of use. This justifies a move toward liability for the “reckless” design of the systems that process the data.