Planning Building Deploying Monitoring
The designers of an AI system need to anticipate vulnerabilities and dual-use scenarios by modeling how bad actors might hijack and weaponize the system for malicious activity.

Learn how to organize an "AL" workshop

Have you considered...?

  • Creating scenarios with hypothetical malicious and innocent bystander personas
  • Conducting “red team” exercises
  • Developing processes for long term mitigation and real-time damage control
  • Engaging sociologists, ethnographers, and political scientists to understand the motivations and incentives that underpin threat models
  • Conjuring up a worst-case scenario that might appear in tomorrow’s headline

Case study

Facebook’s ad network allows for political ads. They didn’t anticipate foreigners buying ads to influence elections. Facebook later required identity verification, but this inadvertently prevented nonpartisan news organizations from buying ads to promote their articles about politics.

Have you engaged with...?

  • Social scientists
  • Subject matter experts
  • Cybersecurity experts


What is missing?

Your suggestions

AI Blindspot Cards


AI systems should make the world a better place. Defining a shared goal guides decisions across the lifecycle of an algorithmic decision-making system, promoting trust amongst individuals and the public.


For an algorithm to be effective, its training data must be representative of the communities that it may impact. The way that you collect and organize data will benefit certain groups while excluding or harming others.


The designers of an AI system need to anticipate vulnerabilities and dual-use scenarios by modeling how bad actors might hijack and weaponize the system for malicious activity.


AI systems often gather personal information that can invade our privacy. Systems storing confidential data can also be vulnerable to cyberattacks that result in devastating data breaches to access personal information.


An algorithm can have an adverse effect on vulnerable populations even without explicitly including protected characteristics. This often occurs when a model includes features that are correlated with these characteristics.


The technical logic of algorithms is complex, which make recommendations unclear. People involved in designing and deploying algorithmic systems have a responsibility to explain high-stakes decisions that affect individuals' well-being.


There are trade-offs and potential externalities when determining an AI system's metrics for success. It is important to balance performance metrics against the risk of negatively impacting vulnerable populations.


Between building and deploying an AI system, conditions in the world may change or not reflect the context in which the system was designed, such that training data are no longer representative.


Like any human process, AI systems carry biases that make them subjective and imperfect. The right to contest an algorithmic decision can surface inaccuracies and grant agency to people affected.


Ethical principles, standards, and policies are futile unless monitored and enforced. A diverse oversight body vested with formal authority can help to establish and maintain transparency, accountability, and sanctions.


The first, last, and every step in-between should include public participation. AI practitioners must enable meaningful input, explanations, and disclosures to ensure that AI systems promote human flourishing and mitigate harms.


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The AI Blindspot cards were developed by Ania Calderon, Dan Taber, Hong Qu, and Jeff Wen during the Berkman Klein Center Assembly program.

Learn more about the team.


This work is licensed under a Creative Commons Attribution 4.0 International License.