Organizational
The risk of financial and/or reputational damage to the organization building or using the ML system.
ENTITY
1 - Human
INTENT
2 - Unintentional
TIMING
2 - Post-deployment
Risk ID
mit203
Domain lineage
6. Socioeconomic and Environmental
6.0 > Socioeconomic & Environmental
Mitigation strategy
1. Prioritize Strategic Governance and Oversight: Establish and maintain diligent board oversight for reputational risk, ensuring the systematic integration of reputation management strategies into enterprise risk management and core business planning to align organizational culture and decision-making with ethical and stakeholder expectations. 2. Implement Advanced Reputational Monitoring and Analytics: Employ systematic, data-driven approaches, including social media sentiment analysis and continuous stakeholder expectation monitoring, to quantify, measure, and detect nascent reputational threats and shifts in public perception prior to escalation. 3. Develop and Rehearse Comprehensive Crisis Protocols: Create and regularly rehearse clear, predefined incident response plans with explicitly defined roles and communication strategies, emphasizing speed, radical transparency, and authenticity in messaging to rapidly mitigate post-incident financial and relational damage.
ADDITIONAL EVIDENCE
An organization may incur said damage when said ML system is shown to result in negative consequences for safety, fairness, security, privacy, and the natural environment. For example, a company was lambasted for its search engine’s response to a query about India’s ugliest language [93]. Reputational damage can also occur if the public perceives the system to potentially result in said negative consequences, such as in the case of a police department trialing the Spot robot [88]