AI Accountability Failures: What Can Go Wrong | QuizBy Eyal Doron / December 6, 2025 / 1 minute of reading AI Accountability Failures: What Can Go Wrong | Quiz 1 / 7 1. Why is the misconception that accountability is legal department job while tech builds problematic? 1. It increases the cost of AI development projects 2. It overloads the legal department with technical responsibilities 3. It causes 62 percent of accountability breakdowns by separating builders from governance 4. It creates compliance documentation that is too technical Correct! WHY: This siloed thinking causes 62 percent of accountability breakdowns because it separates the teams who build AI from those who govern it. CONTEXT: Effective accountability requires RACI matrices that unite cross-functional teams under shared responsibility. REMEMBER: Accountability requires collaboration between legal tech and business not siloed ownership. 2 / 7 2. What is the purpose of a RACI matrix in AI accountability? 1. To assess the technical capabilities of AI team members 2. To rank AI systems by their risk level for compliance purposes 3. To calculate the return on investment for AI governance programs 4. To document who is responsible accountable consulted and informed for each AI decision Correct! WHY: A RACI matrix documents who is Responsible Accountable Consulted and Informed for each aspect of the AI system lifecycle preventing ambiguity. CONTEXT: This structured approach unites cross-functional teams under shared responsibility instead of allowing siloed thinking. REMEMBER: RACI prevents the everyone and therefore no one is responsible trap. 3 / 7 3. What was the root cause of the Zillow iBuying collapse according to the article? 1. The company lacked sufficient training data for home prices 2. The AI algorithm was fundamentally flawed from the start 3. COVID market conditions were impossible to predict 4. No one was assigned responsibility for monitoring model drift Correct! WHY: The monitor role was unowned meaning no one was assigned responsibility for watching model drift and escalating concerns to leadership. CONTEXT: This resulted in 569 million dollars in losses and 2000 layoffs because model drift festered for months without anyone accountable for detection. REMEMBER: Models degrade silently and someone must be watching. 4 / 7 4. In the hiring algorithm discrimination case what was the key accountability lesson? 1. Regulatory bodies should audit all AI training data before deployment 2. Historical data automatically creates legal protection for AI users 3. Deployers are accountable for AI behavior even when the AI learned patterns from data 4. AI vendors are solely responsible for bias in their products Correct! WHY: The company remained accountable because humans chose the training data chose to deploy the tool and chose not to audit for bias. CONTEXT: This case demonstrates that deployers cannot transfer responsibility to algorithms by claiming the AI learned from data. REMEMBER: Deployers are accountable for AI behavior regardless of what the AI learned. 5 / 7 5. What percentage of AI incidents trace to unclear roles and responsibilities according to the article? 1. 50 percent 2. 30 percent 3. 90 percent 4. 70 percent Correct! WHY: This statistic from Forrester demonstrates that the majority of AI incidents stem from governance failures not technical failures. CONTEXT: When 70 percent of incidents trace to unclear roles it shows that accountability structures matter more than technical sophistication. REMEMBER: Seventy percent of AI incidents are accountability failures not technical failures. 6 / 7 6. What quick win does the article recommend for improving AI accountability? 1. Purchase AI governance software from a major vendor 2. Create an AI ethics committee with monthly meetings 3. Define the accountable owner and required documentation for your two highest-risk AI systems 4. Hire a dedicated AI ethics officer for your organization Correct! WHY: Defining accountable owners and documentation requirements for high-risk systems is an immediate actionable step that addresses the most critical accountability gaps. CONTEXT: This focuses resources on the systems where accountability failures would cause the most harm. REMEMBER: Start with your two highest-risk AI systems this month. 7 / 7 7. What is an AI accountability failure? 1. When AI systems fail to produce accurate predictions 2. When AI models exceed their computational budget 3. When responsibility for AI decisions is diffused or deflected so no one is clearly responsible 4. When machine learning training takes too long Correct! WHY: An AI accountability failure occurs when no one can be identified as responsible for AI decisions and their consequences. CONTEXT: This creates accountability gaps where harm goes unaddressed and remediation becomes impossible. REMEMBER: If nobody is accountable then nothing gets fixed. Your score isThe average score is 0% Restart quiz Download PDF Please leave this field empty๐ The AI Security Manager's Newsletter Weekly insights on AI risk management, EU AI Act compliance, and practical security strategies. We donโt spam! Read our privacy policy for more info. Thank you! Please check your inbox to confirm your subscription.
