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2.2.3. Beyond Ethics- Why Governance Requires Enforcement

Beyond Ethics: Why Governance Requires Enforcement

As AI systems scale in scope and impact, many organizations cite their ethics boards, value statements, or AI principles as proof of responsible innovation. These gestures often appear in reports, keynotes, and investor decks, creating the impression that ethical concerns are being addressed.

However, in many cases, these initiatives are:

  • Internally developed and self-policed

  • Lacking clear authority or oversight power

  • Disconnected from actual system design, deployment, or redress

Rather than ensuring accountability, these ethics tools often function as reputation shields used to deflect criticism, delay regulation, or position companies as self-regulating. This phenomenon is increasingly referred to as ethics-washing: the practice of using ethics language to avoid meaningful change.

Case Study 010: The Collapse of Google’s AI Ethics Board (Location: Global | Theme: Governance and Trust)

🧾 Overview
In March 2019, Google announced the formation of the Advanced Technology External Advisory Council (ATEAC), a board intended to provide independent advice on ethical issues in AI development. It included academics, policy experts, and industry figures, and was tasked with guiding issues like facial recognition and machine learning fairness.

🚧 Challenges
One appointee's prior public statements drew strong criticism. The board lacked clear governance authority and inclusivity standards, leading to public skepticism.

💥 Impact
Employee petitions and external backlash followed. The board’s credibility was challenged before it even convened.

🛠️ Action
Google disbanded ATEAC within a week of announcing it. No alternative ethics board was established at that time.

🎯 Results
The case highlighted the limitations of symbolic ethics efforts and emphasized the need for clear authority, inclusiveness, and transparency in AI governance structures.

In 2019, Google formed an external advisory board known as the Advanced Technology External Advisory Council (ATEAC) 1 to provide ethical oversight on the company’s AI development. It was announced as a bold step forward bringing in external voices to guide internal practices and help ensure fairness, safety, and societal alignment.

However, within days, the board was engulfed in controversy. One of its appointed members held high-profile, publicly documented anti-immigrant and anti-LGBTQ+ views, sparking immediate backlash from civil rights organizations, researchers, and Google’s own employees.

The board also:

  • Had no clear authority to review or block deployments

  • Provided no mechanism for public input or transparency

  • Was not embedded within any operational decision-making process

Thousands of employees signed an open letter demanding its dissolution. Public pressure mounted, and within one week, Google disbanded the board before it held a single meeting.

Despite Google’s claims of leading responsible AI efforts, the ATEAC episode revealed a critical governance flaw: ethics structures without inclusivity, clarity, or enforcement can collapse quickly under scrutiny.

Without real power, representation, or structural accountability, even high-profile governance initiatives are perceived as hollow. When governance is symbolic, public trust erodes rapidly.

Together, these subsections argue that while AI governance is not new, its effectiveness is deeply uneven. As we move forward, the conversation must shift from principles to power, and from promises to policies that can be measured, verified, and enforced.

Up to this point, we have explored how AI governance evolved from ethical ideals into enforceable structures, and examined where symbolic frameworks have fallen short. But what does governance look like in practice at the national level? In the next section, we analyze how different countries have translated governance principles into real-world policy, law, and technical infrastructure. These models are not just case studies; they represent competing visions for how trustworthy AI should be built and managed across societies.

TRAI Challenges: Governance Breakdown Analysis


Revisit: Case Study 007 (Amazon Résumé Filter) or Case Study 010 (Google ATEAC).
Reflect on the governance breakdown in either case.


🧩 Tasks:

  • Use the Governance Evaluation Table (Table 2 in Section 2.2.1) to identify specific gaps in areas like risk assessment, transparency, role assignment, or human oversight.

  • Recommend two concrete interventions that could have prevented harm before deployment

For example: A fairness audit at the training stage, A designated AI ethics officer during hiring system development, etc..

Bibliography

  1. Vincent, J. (2019). Google’s AI ethics board shut down after backlash. The Verge. https://www.theverge.com/2019/4/4/18295847/google-ethics-board-disband-ateac-ai ↩